def get_augmentations(resize: int = 224, augmentation_intensity: Optional[str] = None) -> A.Compose:
fix_seeds(24)
crop_limits = (int(resize * 0.85), resize)
if augmentation_intensity == "slight":
p_augment = 0.15
p_scale = 0.15
p_blur = 0.05
p_dropout = 0.05
p_flip = 0.15
p_noise = 0.15
gauss_limit = 0.005
elif augmentation_intensity == "light":
p_augment = 0.25
p_scale = 0.2
p_blur = 0.1
p_dropout = 0.05
p_flip = 0.2
p_noise = 0.2
gauss_limit = 0.01
elif augmentation_intensity == "medium":
p_augment = 0.5
p_scale = 0.2
p_blur = 0.2
p_dropout = 0.1
p_flip = 0.2
p_noise = 0.2
gauss_limit = 0.015
elif augmentation_intensity == "heavy":
p_augment = 0.5
p_scale = 0.35
p_blur = 0.35
p_dropout = 0.15
p_flip = 0.35
p_noise = 0.35
gauss_limit = 0.02
elif augmentation_intensity is None:
return None
else:
raise ValueError("Improper augmentation flag: should be equal to None, light, medium, or heavraisey")
augmentation = A.Compose(
[
A.OneOf(
[A.HorizontalFlip(), A.VerticalFlip()],
p=p_flip,
),
A.OneOf(
[A.Rotate(p=1.0, limit=30), A.RandomRotate90(p=1.0)],
p=p_scale,
),
A.OneOf(
[
A.ShiftScaleRotate(p=1.0, rotate_limit=30),
A.RandomSizedCrop(
min_max_height=crop_limits,
height=resize,
width=resize,
w2h_ratio=1.0,
interpolation=cv2.INTER_CUBIC,
),
],
p=p_scale,
),
A.Blur(blur_limit=3, p=p_blur),
A.CoarseDropout(max_height=7, max_width=7, p=p_dropout),
A.GaussNoise(var_limit=(0.0, gauss_limit), p=p_noise),
],
p=p_augment,
)
return augmentation
# Faster tha nTorch vision
# good for segmetnation , instance segmetnaion
import cv2
import albumentations as A
import numpy as np
from PIL import Image
from utils import plot_examples
image = Image.open("images/elon.jpeg")
mask = Image.open("images/mask.jpeg")
transform = A.Compose([
A.Resize(width=1920, height=1080),
A.RandomCrop(width=1280, height=720),
A.Rotate(limit=40, p=0.9, border_mode=cv2.BORDER_CONSTANT),
A.HorizontalFlip(p=0.3),
A.RGBShift(r_shift_limit=25, g_shift_limit=25, b_shift_limit=25, p=0.9),
A.OneOf([A.Blur(blur_limit=3, p=0.5),
A.ColorJitter(p=0.5)])
])
images_list = [image]
image = np.array(image)
mask = np.array(mask)
for i in range(15):
augumentations = transform(image=image, mask=mask)
augument_img = augumentations["image"]
augument_mask = augumentations["mask"]
df['Filename'] = img_dir[0] + df['Filename'].astype(str)
for extra in range(1, len(labels)):
extra_df = pd.read_csv(labels[extra])
extra_df['Filename'] = img_dir[extra] + '/' + extra_df[
'Filename'] # .astype(str)
df = pd.concat([df, extra_df], ignore_index=True)
# Exclude all entries with "Missing" Died stats
df = df[~df['Died'].isin(['Missing'])]
df['Died'] = pd.to_numeric(df['Died'])
# Augmentations
A_transform = A.Compose([
A.Flip(p=1),
A.RandomRotate90(p=1),
A.Rotate(p=1, limit=45, interpolation=3),
A.RandomResizedCrop(input_size[0],
input_size[1],
scale=(0.8, 1.0),
ratio=(0.8, 1.2),
interpolation=3,
p=1),
A.OneOf([
A.IAAAdditiveGaussianNoise(),
A.GaussNoise(),
], p=0.25),
A.OneOf([
A.MotionBlur(p=0.25),
A.MedianBlur(blur_limit=3, p=0.25),
A.Blur(blur_limit=3, p=0.25),
A.GaussianBlur(p=0.25)
def aug_rot(angle,image,mask):
aug = A.Compose([
A.Rotate([angle,angle],interpolation=1, border_mode=4, value=None, mask_value=None, always_apply=False, p=1)
])
return aug(image=image, mask=mask)
from ml_stratifiers import MultilabelStratifiedKFold
import warnings
warnings.filterwarnings("ignore")
from classification_models.resnet.models import ResNet34
import albumentations as A
MODEL_PATH = 'Christof/models/GAPNet/5_SE/'
exp_suffix = '_2'
SIZE = 256
# Load dataset info
path_to_train = 'Christof/assets/train_rgb_256/'
data = pd.read_csv('Christof/assets/train.csv')
normal_aug = A.Compose([A.Rotate((0,30),p=0.75),
A.RandomRotate90(p=1),
A.HorizontalFlip(p=0.5),
A.RandomBrightness(0.05),
A.RandomContrast(0.05),
A.Normalize(mean=(0.08069, 0.05258, 0.05487), std=(0.13704, 0.10145, 0.15313),
max_pixel_value=255.)
])
val_aug = A.Compose([A.HorizontalFlip(p=0.5),
A.Normalize(mean=(0.08069, 0.05258, 0.05487), std=(0.13704, 0.10145, 0.15313),
max_pixel_value=255.)])
train_dataset_info = []
for name, labels in zip(data['Id'], data['Target'].str.split(' ')):
train_dataset_info.append({
import warnings
warnings.filterwarnings("ignore")
from classification_models.resnet.models import ResNet34
MODEL_PATH = 'Christof/models/ResNet34/tests/19/'
exp_suffix = '2albub'
SIZE = 256
# Load dataset info
path_to_train = 'Christof/assets/train_rgb_256/'
data = pd.read_csv('Christof/assets/train.csv').sample(frac=0.5)
normal_aug = A.Compose(
[A.Flip(p=0.5),
A.Rotate((-180, 180), border_mode=cv2.BORDER_CONSTANT)])
train_dataset_info = []
for name, labels in zip(data['Id'], data['Target'].str.split(' ')):
train_dataset_info.append({
'path':
os.path.join(path_to_train, name),
'labels':
np.array([int(label) for label in labels])
})
train_dataset_info = np.array(train_dataset_info)
counts = np.zeros(28)
for item in train_dataset_info:
for l in item['labels']:
counts[l] = counts[l] + 1
import albumentations
from albumentations import torch as AT
train_transforms = albumentations.Compose([
albumentations.Resize(256, 256),
albumentations.HorizontalFlip(),
albumentations.Rotate(limit=10),
albumentations.JpegCompression(80),
albumentations.HueSaturationValue(),
albumentations.Normalize(),
AT.ToTensor()
])
val_transforms = albumentations.Compose([
albumentations.Resize(256, 256),
albumentations.Normalize(),
AT.ToTensor()
])
test_norm_transforms = albumentations.Compose([
albumentations.Resize(256, 256),
albumentations.Normalize(),
AT.ToTensor()
])
test_flip_transforms = albumentations.Compose([
albumentations.Resize(256, 256),
albumentations.HorizontalFlip(p=1.1),
albumentations.Normalize(),
AT.ToTensor()
#%%
chosen_image = cv2.imread(image_path)
plt.imshow(chosen_image)
#%%
# ref)
# Albumentations part adapted from my good friend Hongnan's notebbok in the Global Wheat Detection competition (https://www.kaggle.com/reighns/augmentations-data-cleaning-and-bounding-boxes#Bounding-Boxes-with-Albumentations)
#%%
albumentation_list = [
A.RandomSunFlare(p=1),
A.RandomFog(p=1),
A.RandomBrightness(p=1),
A.RandomCrop(p=1, height=512, width=512),
A.Rotate(p=1, limit=90),
A.RGBShift(p=1),
A.RandomSnow(p=1),
A.HorizontalFlip(p=1),
A.VerticalFlip(p=1),
A.RandomContrast(limit=0.5, p=1),
A.HueSaturationValue(p=1,
hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=50),
A.Cutout(p=1),
A.Transpose(p=1),
A.JpegCompression(p=1),
A.CoarseDropout(p=1),
A.IAAAdditiveGaussianNoise(loc=0,
scale=(2.5500000000000003, 12.75),
# transforms.ToTensor(),
# transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[
# 0.229, 0.224, 0.225])
# ])
# valid_transform=transforms.Compose([
# # transforms.Resize((256,256)),
# transforms.ToTensor(),
# transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[
# 0.229, 0.224, 0.225])])
train_transform = A.Compose([
A.JpegCompression(p=0.5),
A.Rotate(limit=80, p=1.0),
A.OneOf([
A.OpticalDistortion(),
A.GridDistortion(),
A.IAAPiecewiseAffine(),
]),
A.RandomSizedCrop(min_max_height=(int(resolution*0.7), input_res),
height=resolution, width=resolution, p=1.0),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.5),
A.GaussianBlur(p=0.3),
A.OneOf([
A.RandomBrightnessContrast(),
A.HueSaturationValue(),
]),
A.OneOf([#off in most cases
def __call__(self, image, boxes=None, labels=None):
#initialize the format for lib albumentations
if boxes.shape[0] == 0:
return image, boxes, labels
bbox = []
for i in boxes:
bbox.append(list(i))
#create annotations
annotations = {
'image': image,
'bboxes': boxes,
'category_id': list(labels)
}
#create translation
#Color_Level Change
if self.cfg.DATA_LOADER.AUGMENTATION_WEATHER:
trans_color_level = A.Compose([
A.Cutout(num_holes=20,
max_h_size=64,
max_w_size=64,
fill_value=255,
always_apply=False,
p=0.8),
A.Equalize(p=1),
A.HueSaturationValue(hue_shift_limit=50,
sat_shift_limit=50,
val_shift_limit=50,
always_apply=False,
p=0.8),
A.OneOf([
A.RandomFog(fog_coef_lower=0.3,
fog_coef_upper=0.7,
alpha_coef=0.08,
always_apply=False,
p=0.5),
A.RandomSnow(snow_point_lower=0.1,
snow_point_upper=0.3,
brightness_coeff=2.5,
always_apply=False,
p=0.5),
A.RandomSunFlare(flare_roi=(0, 0, 1, 0.5),
angle_lower=0,
angle_upper=1,
num_flare_circles_lower=6,
num_flare_circles_upper=10,
src_radius=400,
src_color=(255, 255, 255),
always_apply=False,
p=0.5),
A.RandomRain(slant_lower=-10,
slant_upper=10,
drop_length=20,
drop_width=1,
drop_color=(200, 200, 200),
blur_value=7,
brightness_coefficient=0.7,
rain_type=None,
always_apply=False,
p=0.5)
]),
A.OneOf([
A.RandomSizedBBoxSafeCrop(720,
960,
erosion_rate=0.0,
interpolation=1,
always_apply=False,
p=0.5),
A.RandomSizedBBoxSafeCrop(480,
640,
erosion_rate=0.0,
interpolation=1,
always_apply=False,
p=0.5),
A.RandomSizedBBoxSafeCrop(240,
320,
erosion_rate=0.0,
interpolation=1,
always_apply=False,
p=0.5),
]),
])
else:
trans_color_level = A.Compose([
A.Cutout(num_holes=20,
max_h_size=64,
max_w_size=64,
fill_value=255,
always_apply=False,
p=0.5),
A.Equalize(p=1),
A.HueSaturationValue(hue_shift_limit=50,
sat_shift_limit=50,
val_shift_limit=50,
always_apply=False,
p=0.5),
A.OneOf([
A.RandomSizedBBoxSafeCrop(720,
960,
erosion_rate=0.0,
interpolation=1,
always_apply=False,
p=0.5),
A.RandomSizedBBoxSafeCrop(480,
640,
erosion_rate=0.0,
interpolation=1,
always_apply=False,
p=0.5),
A.RandomSizedBBoxSafeCrop(240,
320,
erosion_rate=0.0,
interpolation=1,
always_apply=False,
p=0.5),
]),
])
#Spatial_Level
if self.cfg.DATA_LOADER.AUGMENTATION_SPATIAL_LEVEL:
trans_rotate_level = A.Compose([
A.OneOf([
A.Rotate(limit=90,
interpolation=1,
border_mode=4,
value=None,
mask_value=None,
always_apply=False,
p=0.5),
A.RandomRotate90(always_apply=False, p=0.5),
A.VerticalFlip(always_apply=False, p=0.5),
A.HorizontalFlip(always_apply=False, p=0.5)
]),
])
#Apply the trans
aug = get_aug(trans_color_level)
augmented = aug(**annotations)
img = augmented['image']
bbox = augmented['bboxes']
bbox = np.array(bbox)
label = augmented['category_id']
#try rotate
if self.cfg.DATA_LOADER.AUGMENTATION_SPATIAL_LEVEL:
aug1 = get_aug(trans_rotate_level)
augmented1 = aug1(**augmented)
img1 = augmented1['image']
bbox1 = augmented1['bboxes']
bbox1 = np.array(bbox1)
label1 = augmented1['category_id']
#if rotate fail
if bbox1.shape[0] == 0:
return img, bbox.astype(np.float32), np.array(label)
else:
return img1, bbox1.astype(np.float32), np.array(label1)
else:
return img, bbox.astype(np.float32), np.array(label)
csv_file = f'GIZ_SIZE_{img_size}_arch_{arch}_n_folds_{n_folds}_num_epochs_{n_epochs}_train_bs_{train_batch_size}.csv'
submission.to_csv(os.path.join(submissions_folder, csv_file), index=False)
print(f'[INFO] Submission file save to {os.path.join(submissions_folder, csv_file)}')
if __name__ == '__main__':
args = parser.parse_args()
_ = seed_everything(args.seed_value)
# data augmentations
data_transforms = {
'train': al.Compose([
al.Resize(args.img_size, args.img_size),
al.Cutout(p=.6, max_h_size=15, max_w_size=10, num_holes=4),
al.Rotate(limit=35, p=.04),
al.Normalize((0.1307,), (0.3081,))
]),
'test': al.Compose([
al.Resize(args.img_size, args.img_size),
al.Cutout(p=.6, max_h_size=15, max_w_size=10, num_holes=4),
al.Normalize((0.1307,), (0.3081,))
])
}
test = pd.read_csv(args.test_csv_path)
sample = pd.read_csv(args.sample_csv_path)
# load models
models = load_models(models_path=args.models_path, n_folds=args.kfold, arch=args.arch)
validation_dataset = make_dataset(validation_jpg_list, tag2label)
validation_dataset_mask = make_dataset_mask(validation_jpg_list,
tag2label_mask)
validation_dataset_gender = make_dataset_gender(validation_jpg_list,
tag2label_gender)
validation_dataset_age = make_dataset_age(validation_jpg_list,
tag2label_age)
dataset = make_dataset_age_58(jpg_list, tag2label_age) # img_path, label
# 데이터 불러오기
batch_size = args.batch
transform = A.Compose([
A.Resize(height=256 * 2, width=192 * 2),
A.OneOf([A.Rotate(5), A.Rotate(10)]),
A.HorizontalFlip(p=0.5),
A.Normalize(),
])
train_data = MyDataset(dataset, transform)
train_iter = DataLoader(train_data,
sampler=ImbalancedDatasetSampler(train_data),
batch_size=batch_size,
num_workers=4)
validation_data = MyDataset(validation_dataset_age, transform)
validation_iter = DataLoader(validation_data,
batch_size=batch_size,
shuffle=True,
num_workers=4)
def dataset_selector(train_aug, train_albumentation, val_aug,
val_albumentation, args):
if args.segmentation_problem and args.selected_class == "Grietas":
train_dataset_longitudinales = SIMEPU_Segmentation_Dataset(
data_partition='train',
transform=train_aug,
fold=args.fold,
augmentation=train_albumentation,
selected_class="Grietas longitudinales",
data_mod=args.data_mod,
)
train_albumentation.append(albumentations.Rotate(limit=(90, 90), p=1))
train_dataset_transversales = SIMEPU_Segmentation_Dataset(
data_partition='train',
transform=train_aug,
fold=args.fold,
augmentation=train_albumentation,
selected_class="Grietas transversales",
rotate=True,
data_mod=args.data_mod,
)
train_dataset = torch.utils.data.ConcatDataset(
[train_dataset_longitudinales, train_dataset_transversales])
num_classes = 1
val_dataset_longitudinales = SIMEPU_Segmentation_Dataset(
data_partition='validation',
transform=val_aug,
fold=args.fold,
augmentation=val_albumentation,
selected_class="Grietas longitudinales",
data_mod=args.data_mod,
)
val_albumentation.append(albumentations.Rotate(limit=(90, 90), p=1))
val_dataset_transversales = SIMEPU_Segmentation_Dataset(
data_partition='validation',
transform=val_aug,
fold=args.fold,
augmentation=val_albumentation,
selected_class="Grietas transversales",
rotate=True,
data_mod=args.data_mod,
)
val_dataset = torch.utils.data.ConcatDataset(
[val_dataset_longitudinales, val_dataset_transversales])
train_loader = DataLoader(
train_dataset,
batch_size=args.batch_size,
pin_memory=True,
shuffle=True,
collate_fn=train_dataset_longitudinales.segmentation_collate)
val_loader = DataLoader(
val_dataset,
batch_size=args.batch_size,
pin_memory=True,
shuffle=False,
collate_fn=val_dataset_longitudinales.segmentation_collate)
elif args.segmentation_problem:
train_dataset = SIMEPU_Segmentation_Dataset(
data_partition='train',
transform=train_aug,
fold=args.fold,
augmentation=train_albumentation,
selected_class=args.selected_class)
num_classes = train_dataset.num_classes
val_dataset = SIMEPU_Segmentation_Dataset(
data_partition='validation',
transform=val_aug,
fold=args.fold,
augmentation=val_albumentation,
selected_class=args.selected_class,
)
train_loader = DataLoader(
train_dataset,
batch_size=args.batch_size,
pin_memory=True,
shuffle=True,
collate_fn=train_dataset.segmentation_collate)
val_loader = DataLoader(val_dataset,
batch_size=args.batch_size,
pin_memory=True,
shuffle=False,
collate_fn=val_dataset.segmentation_collate)
return train_dataset, train_loader, val_dataset, val_loader, num_classes
else:
train_dataset = SIMEPU_Dataset_MultiLabel(
data_partition='train',
transform=train_aug,
fold=args.fold,
augmentation=train_albumentation,
segmentation_problem=args.segmentation_problem,
selected_class=args.selected_class)
num_classes = train_dataset.num_classes
val_dataset = SIMEPU_Dataset_MultiLabel(
data_partition='validation',
transform=val_aug,
fold=args.fold,
segmentation_problem=args.segmentation_problem,
augmentation=val_albumentation,
selected_class=args.selected_class,
)
train_loader = DataLoader(
train_dataset,
batch_size=args.batch_size,
pin_memory=True,
shuffle=True,
)
val_loader = DataLoader(val_dataset,
batch_size=args.batch_size,
pin_memory=True,
shuffle=False)
return train_dataset, train_loader, val_dataset, val_loader, num_classes
import torch.nn as nn
from training.lrfinder import LRFinder
from gradcam.grad_cam import *
"""**TRANSFORMS**
In this section we can define the alnumAlbumentationTransforms
"""
#Transforms
channel_means = (0.49139968, 0.48215841, 0.44653091)
channel_stdevs = (0.24703223, 0.24348513, 0.26158784)
# Train Phase transformations
train_transforms = AlbumentationTransforms([
A.Rotate((-30.0, 30.0)),
A.HorizontalFlip(),
A.RGBShift(r_shift_limit=50, g_shift_limit=50, b_shift_limit=50, p=0.5),
A.Normalize(mean=channel_means, std=channel_stdevs),
A.Cutout(num_holes=4) # fillvalue is 0 after normalizing as mean is 0
])
fillmeans = (np.array(channel_means)).astype(np.uint8)
# Test Phase transformations
test_transforms = AlbumentationTransforms([A.Normalize(mean=channel_means, std=channel_stdevs)])
"""**DATA LOADER**
We have loaded the data and plotted a few images
"""
import cv2
import torch
import numpy as np
from torch.utils.data import Dataset
import pickle
from conet.config import get_cfg
train_size_aug = alb.Compose([
# alb.RandomSizedCrop(min_max_height=(300, 500)),
alb.HorizontalFlip(),
alb.VerticalFlip(),
# alb.RandomBrightness(limit=0.01),
alb.RandomScale(),
alb.ElasticTransform(),
alb.Rotate(limit=50),
alb.PadIfNeeded(530, border_mode=cv2.BORDER_REFLECT101),
alb.RandomCrop(512, 512),
# alb.Normalize(),
# alb.pytorch.ToTensor(),
# ToTensorV2()
])
train_content_aug = alb.Compose([
alb.MedianBlur(),
alb.RGBShift(r_shift_limit=10, g_shift_limit=10, b_shift_limit=10),
alb.RandomBrightnessContrast(brightness_limit=0.1),
alb.Normalize(),
# ToTensorV2()
])
val_aug = alb.Compose([
def get_augmentation(version):
if version == "v1":
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v2":
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.OneOf([
A.RandomCrop(height=256, width=256, p=1),
A.RandomCrop(height=384, width=384, p=1),
A.RandomCrop(height=512, width=512, p=1),
A.RandomCrop(height=640, width=640, p=1),
],
p=0.5),
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v3":
return {
"train":
A.Compose([
A.RandomCrop(height=1024, width=1024, p=1),
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v4":
# for 3x-scaled dataset | 256x256
return {
"train":
A.Compose([
A.RandomCrop(height=512, width=512, p=1),
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v5":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.RandomCrop(height=512, width=512, p=1),
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v6":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
always_apply=False,
p=0.25),
A.RandomBrightness(limit=0.4, always_apply=False, p=0.25),
A.RandomContrast(limit=0.2, always_apply=False, p=0.25),
A.RandomShadow(shadow_roi=(0, 0, 1, 1),
num_shadows_lower=1,
num_shadows_upper=50,
shadow_dimension=5,
always_apply=False,
p=0.25),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v7":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.RandomCrop(height=768, width=768, p=1),
A.Resize(height=256, width=256, p=1.0),
A.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
always_apply=False,
p=0.25),
A.RandomBrightness(limit=0.4, always_apply=False, p=0.25),
A.RandomContrast(limit=0.2, always_apply=False, p=0.25),
A.RandomShadow(shadow_roi=(0, 0, 1, 1),
num_shadows_lower=1,
num_shadows_upper=50,
shadow_dimension=5,
always_apply=False,
p=0.25),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v8":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.Rotate(p=0.5),
A.RandomCrop(height=512, width=512, p=1),
A.Flip(p=0.5),
# color
A.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
always_apply=False,
p=0.25),
A.RandomBrightness(limit=0.4, always_apply=False, p=0.25),
A.RandomContrast(limit=0.2, always_apply=False, p=0.25),
A.RandomShadow(shadow_roi=(0, 0, 1, 1),
num_shadows_lower=1,
num_shadows_upper=50,
shadow_dimension=5,
always_apply=False,
p=0.25),
# transform
A.ElasticTransform(p=1, alpha=120, sigma=6, alpha_affine=0.25),
A.GridDistortion(p=0.25),
A.OpticalDistortion(distort_limit=2, shift_limit=0.5, p=0.25),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v9":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
always_apply=False,
p=0.25),
A.RandomBrightness(limit=0.4, always_apply=False, p=0.25),
A.RandomContrast(limit=0.2, always_apply=False, p=0.25),
A.RandomShadow(shadow_roi=(0, 0, 1, 1),
num_shadows_lower=1,
num_shadows_upper=50,
shadow_dimension=5,
always_apply=False,
p=0.25),
# transform
A.OneOf([
A.ElasticTransform(
alpha=120, sigma=6, alpha_affine=0.25, p=0.25),
A.GridDistortion(p=0.25),
A.OpticalDistortion(
distort_limit=2, shift_limit=0.5, p=0.25)
],
p=0.75),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v10":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
# size
A.OneOf([
A.CropNonEmptyMaskIfExists(height=128, width=128, p=1.0),
A.CropNonEmptyMaskIfExists(height=256, width=256, p=1.0),
A.CropNonEmptyMaskIfExists(height=384, width=384, p=1.0)
],
p=0.25),
A.PadIfNeeded(min_height=512,
min_width=512,
border_mode=4,
p=1),
# array shuffle
A.OneOf([A.MaskDropout(p=1),
A.RandomGridShuffle(p=1)], p=0.5),
# quality
A.Downscale(scale_min=0.25,
scale_max=0.75,
interpolation=0,
always_apply=False,
p=0.5),
A.GaussNoise(p=0.5),
A.OneOf([
A.GlassBlur(p=1),
A.GaussianBlur(p=1),
], p=0.5),
# colors
A.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
always_apply=False,
p=0.5),
A.RandomBrightness(limit=0.4, always_apply=False, p=0.5),
A.RandomContrast(limit=0.2, always_apply=False, p=0.5),
A.RandomShadow(shadow_roi=(0, 0, 1, 1),
num_shadows_lower=1,
num_shadows_upper=50,
shadow_dimension=5,
always_apply=False,
p=0.5),
A.CLAHE(p=0.5),
A.Equalize(p=0.5),
A.ChannelShuffle(p=0.5),
# transform
A.OneOf([
A.ElasticTransform(
alpha=120, sigma=6, alpha_affine=0.25, p=1),
A.GridDistortion(p=1),
A.OpticalDistortion(distort_limit=2, shift_limit=0.5, p=1)
],
p=0.5),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v11":
# custom normalization (see other/img_normalization.py)
return {
"train":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v12":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
# size / quality
A.OneOf([
A.CropNonEmptyMaskIfExists(height=128, width=128, p=1.0),
A.CropNonEmptyMaskIfExists(height=256, width=256, p=1.0),
A.CropNonEmptyMaskIfExists(height=384, width=384, p=1.0),
A.Downscale(scale_min=0.25,
scale_max=0.75,
interpolation=0,
always_apply=False,
p=1.0),
],
p=0.25),
A.PadIfNeeded(min_height=512,
min_width=512,
border_mode=4,
p=1),
# array shuffle
A.OneOf([A.MaskDropout(p=1),
A.RandomGridShuffle(p=1)], p=0.15),
# noise
A.OneOf([
A.GaussNoise(p=1),
A.GlassBlur(p=1),
A.GaussianBlur(p=1),
],
p=0.15),
# colors
A.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
always_apply=False,
p=0.15),
A.RandomBrightness(limit=0.4, always_apply=False, p=0.15),
A.RandomContrast(limit=0.2, always_apply=False, p=0.15),
A.RandomShadow(shadow_roi=(0, 0, 1, 1),
num_shadows_lower=1,
num_shadows_upper=50,
shadow_dimension=5,
always_apply=False,
p=0.15),
A.OneOf([
A.CLAHE(p=1),
A.Equalize(p=1),
A.ChannelShuffle(p=1),
],
p=0.15),
# transform
A.OneOf([
A.ElasticTransform(
alpha=120, sigma=6, alpha_affine=0.25, p=1),
A.GridDistortion(p=1),
A.OpticalDistortion(distort_limit=2, shift_limit=0.5, p=1)
],
p=0.15),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
else:
raise Exception(f"Augmentation version '{version}' is UNKNOWN!")
warnings.filterwarnings("ignore")
from classification_models.resnet.models import ResNet34
MODEL_PATH = 'Christof/models/ResNet34/tests/24/'
exp_suffix = '0'
SIZE = 256
# Load dataset info
path_to_train = 'Christof/assets/train_rgb_256/'
data = pd.read_csv('Christof/assets/train.csv').sample(frac=0.3)
normal_aug = A.Compose(
[A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.5),
A.Rotate((-180, 180))])
train_dataset_info = []
for name, labels in zip(data['Id'], data['Target'].str.split(' ')):
train_dataset_info.append({
'path':
os.path.join(path_to_train, name),
'labels':
np.array([int(label) for label in labels])
})
train_dataset_info = np.array(train_dataset_info)
counts = np.zeros(28)
for item in train_dataset_info:
for l in item['labels']:
counts[l] = counts[l] + 1
import warnings
warnings.filterwarnings("ignore")
from classification_models.resnet.models import ResNet34
MODEL_PATH = 'Christof/models/train_vs_test/ResNet34/1/'
SIZE = 256
# Load dataset info
path_to_train = 'Christof/assets/train_rgb_256/'
path_to_test = 'Christof/assets/test_rgb_256/'
train_data = pd.read_csv('Christof/assets/train.csv')
test_data = pd.read_csv('Christof/assets/sample_submission.csv')
normal_aug = A.Compose([A.Flip(p=0.75), A.Rotate((-180, 180))])
train_dataset_info = []
for name in train_data['Id']:
train_dataset_info.append({
'path': os.path.join(path_to_train, name),
'labels': np.array(0)
})
train_dataset_info = np.array(train_dataset_info)
test_dataset_info = []
for name in test_data['Id']:
test_dataset_info.append({
'path': os.path.join(path_to_test, name),
'labels': np.array(1)
})
def get_filenames_of_path(path: pathlib.Path, ext: str = '*'):
"""Returns a list of files in a directory/path. Uses pathlib."""
filenames = [file for file in path.glob(ext) if file.is_file()]
return filenames
# input and target files
images_train = get_filenames_of_path(root / 'train_frames')
masks_train = get_filenames_of_path(root / 'train_masks')
images_valid = get_filenames_of_path(root / 'val_frames')
masks_valid = get_filenames_of_path(root / 'val_masks')
# training transformations and augmentations
transforms = Compose([
Resize(input_size=(480, 720), target_size=(240, 360)),
AlbuSeg2d(albu=albumentations.HorizontalFlip(p=0.5)),
AlbuSeg2d(albu=albumentations.Rotate(limit=20,p=0.2)),
DenseTarget(),
Normalize_to_01(),
FixGreyScale()
])
# dataset training
dataset_train = dataset.EyeDataset(inputs=images_train,
targets=masks_train,
transform=transforms)
# dataset validation
dataset_valid = dataset.EyeDataset(inputs=images_valid,
targets=masks_valid,
transform=transforms)
def sample_custom_augmentations_constructor(
num_features: int, window_radius: int) -> albumentations.Compose:
"""
This function returns a custom augmentations object for use with sequences via the load_sequences function in
data_core.py. Please note that these augmentations have only been tested with RGB data between 0 and 1 and that
order of operations is critical. e.g., blurs don't like missing data so shouldn't be applied before dropout, noise
probably shouldn't be applied before color changes or blurs... of course, this is all dependent on your specific
problem.
Args:
num_features: number of features used in the model
window_size: window_size from the data configs
Returns:
custom augmentations function for use with sequences
"""
max_kernel = int(round(0.1 * window_radius))
max_hole_size = int(round(0.1 * window_radius))
additional_targets = [
ADDITIONAL_TARGETS_KEY.format(idx) for idx in range(1, num_features)
]
return albumentations.Compose(
[
# The augmentations assume an image is RGB between 0 and 1
albumentations.ToFloat(max_value=255, always_apply=True, p=1.0),
# These augmentations should be order independent, toss 'em up front
albumentations.Flip(p=0.5),
albumentations.Transpose(p=0.5),
albumentations.Rotate(limit=90, p=0.5),
# Fogging as it's quite similar to top-down cloud effects, seems reasonable to apply up front
albumentations.RandomFog(
fog_coef_lower=0.2, fog_coef_upper=0.8, alpha_coef=0.08,
p=0.5),
# Color modifications
albumentations.OneOf(
[
albumentations.RandomBrightnessContrast(
brightness_limit=0.2,
contrast_limit=0.6,
brightness_by_max=True,
p=1.0),
albumentations.RGBShift(r_shift_limit=0.2,
g_shift_limit=0.2,
b_shift_limit=0.2,
p=1.0),
],
p=0.25,
),
# Distortions
albumentations.OneOf(
[
albumentations.ElasticTransform(
alpha=1, sigma=50, alpha_affine=50, p=1.0),
albumentations.GridDistortion(
num_steps=5, distort_limit=0.4, p=1.0),
albumentations.OpticalDistortion(
distort_limit=0.1, shift_limit=0.1, p=1.0),
],
p=0.25,
),
albumentations.GaussianBlur(blur_limit=max_kernel, p=0.25),
# Noise
albumentations.OneOf(
[
albumentations.CoarseDropout(max_holes=8,
max_height=max_hole_size,
max_width=max_hole_size,
fill_value=np.nan,
p=1.0),
albumentations.GaussNoise(var_limit=0.05, mean=0, p=1.0),
],
p=0.25,
),
# Scaling, adding last so that other augmentations are applied at a consistent resolution
albumentations.RandomScale(scale_limit=0.05, p=0.25),
# Augmentations may not return images of the same size, images can be both smaller and larger than expected, so
# these two augmentations are added to keep things consistent
albumentations.PadIfNeeded(
2 * window_radius, 2 * window_radius, always_apply=True,
p=1.0),
albumentations.CenterCrop(
2 * window_radius, 2 * window_radius, always_apply=True,
p=1.0),
# Return the data to its original scale
albumentations.FromFloat(max_value=255, always_apply=True, p=1.0),
],
p=1.0,
additional_targets={target: "image"
for target in additional_targets},
)
def __init__(self,
resize=(0, 0),
padding=(0, 0),
crop=(0, 0),
horizontal_flip_prob=0.0,
vertical_flip_prob=0.0,
gaussian_blur_prob=0.0,
rotate_degree=0.0,
cutout_prob=0.0,
cutout_dim=(8, 8),
hue_saturation_prob=0.0,
contrast_prob=0.0,
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
normalize=True,
train=True):
"""Create data transformation pipeline.
Args:
resize (tuple, optional): Resize the input to the given height and
width. (default: (0, 0))
padding (tuple, optional): Pad the image if the image size is less
than the specified dimensions (height, width). (default= (0, 0))
crop (tuple, optional): Randomly crop the image with the specified
dimensions (height, width). (default: (0, 0))
horizontal_flip_prob (float, optional): Probability of an image
being horizontally flipped. (default: 0)
vertical_flip_prob (float, optional): Probability of an image
being vertically flipped. (default: 0)
rotate_prob (float, optional): Probability of an image being
rotated. (default: 0)
rotate_degree (float, optional): Angle of rotation for image
augmentation. (default: 0)
cutout_prob (float, optional): Probability that cutout will be
performed. (default: 0)
cutout_dim (tuple, optional): Dimensions of the cutout box (height, width).
(default: (8, 8))
hue_saturation_prob (float, optional): Probability of randomly changing hue,
saturation and value of the input image. (default: 0)
contrast_prob (float, optional): Randomly changing contrast of the input image.
(default: 0)
mean (float or tuple, optional): Dataset mean. (default: 0.5 for each channel)
std (float or tuple, optional): Dataset standard deviation. (default: 0.5 for each channel)
"""
transforms_list = []
if sum(resize) > 0:
transforms_list += [
A.Resize(height=resize[0], width=resize[1], always_apply=True)
]
if train:
if sum(padding) > 0:
transforms_list += [
A.PadIfNeeded(min_height=padding[0],
min_width=padding[1],
always_apply=True)
]
if sum(crop) > 0:
transforms_list += [
A.RandomCrop(crop[0], crop[1], always_apply=True)
]
if horizontal_flip_prob > 0: # Horizontal Flip
transforms_list += [A.HorizontalFlip(p=horizontal_flip_prob)]
if vertical_flip_prob > 0: # Vertical Flip
transforms_list += [A.VerticalFlip(p=vertical_flip_prob)]
if gaussian_blur_prob > 0: # Patch Gaussian Augmentation
transforms_list += [A.GaussianBlur(p=gaussian_blur_prob)]
if rotate_degree > 0: # Rotate image
transforms_list += [A.Rotate(limit=rotate_degree)]
if cutout_prob > 0: # CutOut
if isinstance(mean, float):
fill_value = mean * 255.0
else:
fill_value = tuple([x * 255.0 for x in mean])
transforms_list += [
A.CoarseDropout(p=cutout_prob,
max_holes=1,
fill_value=fill_value,
max_height=cutout_dim[0],
max_width=cutout_dim[1])
]
if hue_saturation_prob > 0: # Hue Saturation
transforms_list += [
A.HueSaturationValue(p=hue_saturation_prob)
]
if contrast_prob > 0: # Random Contrast
transforms_list += [A.RandomContrast(p=contrast_prob)]
if normalize:
# normalize the data with mean and standard deviation to keep values in range [-1, 1]
# since there are 3 channels for each image,
# we have to specify mean and std for each channel
transforms_list += [
A.Normalize(mean=mean, std=std, always_apply=True),
]
# convert the data to torch.FloatTensor
transforms_list += [ToTensor()]
self.transform = A.Compose(transforms_list)
txt = glob(
"D:/Usuarios/diego/Documentos/GitHub/Annotation_Tools/annotation/*.txt")
#List where bb and labels will be saved
bbs = []
category_ids = []
##We will use the mapping from category_id to the class name
#to visualize the class label for the bounding box on the image
category_id_to_name = {"Person": "Person"}
#Transformation to be applied
transform = A.Compose(
[
A.HorizontalFlip(p=1.0),
A.Rotate(limit=25, border_mode=cv2.BORDER_CONSTANT, value=1, p=1),
A.ISONoise(color_shift=(0.01, 0.01), intensity=(0.1, 0.1), p=1.0),
A.Blur(blur_limit=5, p=1.0),
A.RandomBrightnessContrast(brightness_limit=0.2,
contrast_limit=0.2,
brightness_by_max=True,
p=1),
A.GaussNoise(
var_limit=(100.0, 300.0), mean=40, always_apply=False, p=1)
],
bbox_params=A.BboxParams(format='pascal_voc',
label_fields=['category_ids']),
)
i = 1
def main():
train_transform = A.Compose([
A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),
A.Rotate(limit=35, p=1.0),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.1),
A.Normalize(
mean=[0.0, 0.0, 0.0],
std=[1.0, 1.0, 1.0],
max_pixel_value=255.0,
),
ToTensorV2(),
], )
val_transforms = A.Compose([
A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),
A.Normalize(
mean=[0.0, 0.0, 0.0],
std=[1.0, 1.0, 1.0],
max_pixel_value=255.0,
),
ToTensorV2(),
], )
model = ESNet(in_ch=3, n_class=1).to(DEVICE)
loss_fn = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
train_loader, val_loader = get_loaders(
TRAIN_IMG_DIR,
TRAIN_MASK_DIR,
VAL_IMG_DIR,
VAL_MASK_DIR,
BATCH_SIZE,
train_transform,
val_transforms,
NUM_WORKERS,
PIN_MEMORY,
)
if LOAD_MODEL:
load_checkpoint(torch.load("model.pth.tar"), model)
check_accuracy(val_loader, model, device=DEVICE)
scaler = torch.cuda.amp.GradScaler()
for epoch in range(NUM_EPOCHS):
train_fn(train_loader, model, optimizer, loss_fn, scaler)
# save model
checkpoint = {
"state_dict": model.state_dict(),
"optimizer": optimizer.state_dict(),
}
save_checkpoint(checkpoint)
# check accuracy
check_accuracy(val_loader, model, device=DEVICE)
# print some examples to a folder
save_predictions_as_imgs(val_loader,
model,
folder=SAVING_PATH,
device=DEVICE)
def augment(source, images_output_path, size):
images_path = images_output_path + "/JPEGImages/"
os.makedirs(images_path, exist_ok=True)
xml_path = images_output_path + "/Annotations/"
os.makedirs(xml_path, exist_ok=True)
transform = A.Compose(
[
# A.CLAHE(),
# A.RandomScale(scale_limit=[0.5, 1]),
# A.RandomCrop(width=450, height=450),
A.OneOf([
A.Sequential(
[A.RandomCrop(width=800, height=600),
A.RandomRotate90()]),
# A.Sequential(
# [
# A.RandomSizedBBoxSafeCrop(width=800, height=600),
# A.RandomRotate90(),
# ]
# ),
A.Sequential([
A.RandomScale(scale_limit=0.2),
A.Flip(),
A.RandomRotate90(),
],
# p=0.3,
),
A.Sequential(
[
A.Rotate(),
],
p=0.3,
),
])
# A.Transpose(),
# A.Resize(0.9, 0.9),
# A.Blur(blur_limit=3),
# A.OpticalDistortion(),
# A.GridDistortion(),
# A.HueSaturationValue(),
],
bbox_params=A.BboxParams(format="pascal_voc",
min_visibility=0.5,
label_fields=["class_labels"]),
)
rows = []
random.seed(42)
images_index = 1
for name, group in source.groupby("filename"):
row = group.iloc[0]
print(row["filename"])
image = cv2.imread(row["filename"])
same = set()
bboxes = []
class_labels = []
aleady_box = {}
for _, vrow in group.iterrows():
bboxes.append(
[vrow["xmin"], vrow["ymin"], vrow["xmax"], vrow["ymax"]])
class_labels.append(vrow["class"])
aleady_box[vrow["class"]] = set()
all_count = 0
print(aleady_box)
while int(all_count) < size:
augmented = transform(
image=image,
bboxes=bboxes,
class_labels=class_labels,
)
file_name = f"{images_index}.jpg"
if len(augmented["bboxes"]) < 1:
continue
writer = Writer(file_name, augmented["image"].shape[1],
augmented["image"].shape[0])
findbox = False
for index, bbox in enumerate(augmented["bboxes"]):
x_min, y_min, x_max, y_max = map(lambda v: int(v), bbox[:4])
same.add(x_min)
rows.append({
"filename": f"{images_path}/{file_name}",
"width": augmented["image"].shape[1],
"height": augmented["image"].shape[0],
"class": augmented["class_labels"][index],
"xmin": x_min,
"ymin": y_min,
"xmax": x_max,
"ymax": y_max,
"imageindex": str(images_index),
})
writer.addObject(augmented["class_labels"][index], x_min,
y_min, x_max, y_max)
if len(aleady_box[augmented["class_labels"][index]]) >= size:
continue
aleady_box[augmented["class_labels"][index]].add(x_min)
findbox = True
if findbox:
cv2.imwrite(f"{images_path}/{file_name}", augmented["image"])
writer.save(f"{xml_path}/{images_index}.xml")
images_index += 1
print(aleady_box)
all_count = sum([min(len(v), size)
for k, v in aleady_box.items()]) / len(aleady_box)
df = pd.DataFrame(rows)
return df
def get_images(self, imgpath):
# Pick random clone, crypt or fufi
u01 = np.random.uniform()
if u01 < self.cpfr_frac[0]:
img, mask = self.all_svs_opened[imgpath].fetch_clone(
prop_displ=0.45)
elif u01 < np.sum(self.cpfr_frac[0:2]):
img, mask = self.all_svs_opened[imgpath].fetch_partial(
prop_displ=0.45)
elif u01 < np.sum(self.cpfr_frac[0:3]):
img, mask = self.all_svs_opened[imgpath].fetch_fufi(
prop_displ=0.45)
else:
img, mask = self.all_svs_opened[imgpath].fetch_rndmtile()
if self.dilate_masks == True:
st_3 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
n_dil = int(5 / self.um_per_pixel) # if mpp is one or less than 1
# dilate if desired
for i in range(mask.shape[2]):
mask[:, :, i] = cv2.morphologyEx(mask[:, :, i].copy(),
cv2.MORPH_DILATE,
st_3,
iterations=n_dil)
if self.aug == True:
composition = A.Compose(
[
A.HorizontalFlip(),
A.VerticalFlip(),
A.Rotate(border_mode=cv2.BORDER_CONSTANT),
A.OneOf(
[
A.ElasticTransform(alpha=1000,
sigma=30,
alpha_affine=30,
border_mode=cv2.BORDER_CONSTANT,
p=1),
A.GridDistortion(border_mode=cv2.BORDER_CONSTANT,
p=1),
# A.OpticalDistortion(distort_limit=0.5, shift_limit=0.5,
# border_mode = cv2.BORDER_CONSTANT, p = 1),
],
p=0.7),
A.CLAHE(p=0.3),
A.HueSaturationValue(hue_shift_limit=12,
sat_shift_limit=12,
val_shift_limit=12,
p=0.6),
A.RandomBrightnessContrast(p=0.6),
A.Posterize(p=0.2, num_bits=4),
A.OneOf([
A.JpegCompression(p=1),
A.MedianBlur(p=1),
A.Blur(p=1),
A.GlassBlur(p=1, max_delta=2, sigma=0.4),
A.IAASharpen(p=1)
],
p=0.3)
],
p=1)
transformed = composition(image=img, mask=mask)
img, mask = transformed['image'], transformed['mask']
mask_list = [mask[:, :, ii] for ii in range(mask.shape[2])]
if self.stride_bool:
mask_list = [cv2.pyrDown(mask_ii.copy()) for mask_ii in mask_list]
mask_list = [
cv2.threshold(mask_ii, 120, 255, cv2.THRESH_BINARY)[1]
for mask_ii in mask_list
]
## convert to floating point space, normalize and mask non-used clones
img = img.astype(np.float32) / 255
mask_list = [mask_ii.astype(np.float32) / 255 for mask_ii in mask_list]
if self.normalize:
img = (img - self.norm_mean) / self.norm_std
return img, np.stack(mask_list, axis=2)
def __init__(self, p=0.2, limit=90):
self.p = p
self.limit = limit
self.augmenter = albu.Rotate(p=self.p, limit=self.limit)
import warnings
warnings.filterwarnings("ignore")
from classification_models.resnet.models import ResNet34
import albumentations as A
MODEL_PATH = 'Christof/models/ResNet34/23/'
exp_suffix = '_base'
SIZE = 256
# Load dataset info
path_to_train = 'Christof/assets/train_rgb_256/'
data = pd.read_csv('Christof/assets/train.csv')
normal_aug = A.Compose([
A.Rotate((0, 30), p=0.75),
A.RandomRotate90(p=1),
A.HorizontalFlip(p=0.5),
A.RandomBrightness(0.05),
A.RandomContrast(0.05),
A.Normalize(mean=(0.08069, 0.05258, 0.05487),
std=(0.13704, 0.10145, 0.15313),
max_pixel_value=255.)
])
val_aug = A.Compose([
A.HorizontalFlip(p=0.5),
A.Normalize(mean=(0.08069, 0.05258, 0.05487),
std=(0.13704, 0.10145, 0.15313),
max_pixel_value=255.)
])
import pickle
with open('Russ/folds2.pkl', 'rb') as f:
folds = pickle.load(f)
data['fold'] = data['Id'].apply(lambda x: folds[x])
def get_fold_ids(fold_id,data_set_info, shuff = True):
fold_info = np.array([item['fold'] for item in data_set_info])
val_ids = np.where(fold_info == fold_id)[0]
train_ids = np.where(fold_info != fold_id)[0]
if shuff:
shuffle(val_ids)
shuffle(train_ids)
return train_ids, val_ids
normal_aug = A.Compose([A.OneOf([A.Rotate((-180,180)),
A.Rotate((-180,180),border_mode=cv2.BORDER_CONSTANT)]),
A.Flip(p=0.75)
])
train_dataset_info = []
for name, labels in zip(data['Id'], data['Target'].str.split(' ')):
train_dataset_info.append({
'path': os.path.join(path_to_train, name),
'labels': np.array([int(label) for label in labels]),
'fold':folds[name]})
train_dataset_info = np.array(train_dataset_info)
counts = np.zeros(28)
for item in train_dataset_info:
for l in item['labels']:
from glob import glob
import imageio
import numpy as np
from torch.utils.data import Dataset
import pickle
from conet.config import get_cfg
train_aug = alb.Compose([
# alb.RandomSizedCrop(min_max_height=(300, 500)),
alb.RandomScale(),
# alb.HorizontalFlip(),
alb.VerticalFlip(),
alb.RandomBrightness(limit=0.01),
alb.Rotate(limit=30),
# 224 548
alb.PadIfNeeded(min_height=224, min_width=548, border_mode=cv2.BORDER_REFLECT101),
alb.RandomCrop(224, 512),
alb.Normalize(),
# alb.pytorch.ToTensor(),
ToTensorV2()
])
val_aug = alb.Compose([
alb.PadIfNeeded(min_height=224, min_width=512, border_mode=cv2.BORDER_REFLECT101),
alb.Normalize(),
# alb.Resize(512, 512),
alb.CenterCrop(224, 512),
ToTensorV2(),
])
def create_submission(model, model_name, files_dir):
my_transforms = {
"base":
A.Compose([
A.Resize(height=240, width=240),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255.0,
),
ToTensorV2(),
]),
"horizontal_flip":
A.Compose([
A.Resize(height=240, width=240),
A.HorizontalFlip(p=1.0),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255.0,
),
ToTensorV2(),
]),
"vertical_flip":
A.Compose([
A.Resize(height=240, width=240),
A.VerticalFlip(p=1.0),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255.0,
),
ToTensorV2(),
]),
"coloring":
A.Compose([
A.Resize(height=240, width=240),
A.ColorJitter(p=1.0),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255.0,
),
ToTensorV2(),
]),
"rotate":
A.Compose([
A.Resize(height=240, width=240),
A.Rotate(p=1.0, limit=45),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255.0,
),
ToTensorV2(),
]),
"shear":
A.Compose([
A.Resize(height=240, width=240),
A.IAAAffine(p=1.0),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255.0,
),
ToTensorV2(),
]),
}
for t in [
"base", "horizontal_flip", "vertical_flip", "coloring", "rotate",
"shear"
]:
predictions = []
labels = []
all_files = []
test_dataset = MyDataset(root=files_dir, transform=my_transforms[t])
test_loader = DataLoader(test_dataset,
batch_size=32,
num_workers=4,
shuffle=False,
pin_memory=True)
model.eval()
for idx, (x, y, filenames) in enumerate(tqdm(test_loader)):
x = x.to(config.DEVICE)
with torch.no_grad():
outputs = (torch.clip(torch.sigmoid(model(x)), 0.005,
0.995).squeeze(1).cpu().numpy())
predictions.append(outputs)
labels += y.numpy().tolist()
all_files += filenames
df = pd.DataFrame({
"id":
np.arange(
1,
(len(predictions) - 1) * predictions[0].shape[0] +
predictions[-1].shape[0] + 1,
),
"label":
np.concatenate(predictions, axis=0),
})
df.to_csv(f"predictions_test/submission_{model_name}_{t}.csv",
index=False)
model.train()
print(
f"Created submission file for model {model_name} and transform {t}"
)