def __init__(
self,
prob=0.7,
blur_prob=0.7,
jitter_prob=0.7,
rotate_prob=0.7,
flip_prob=0.7,
):
super().__init__()
self.prob = prob
self.blur_prob = blur_prob
self.jitter_prob = jitter_prob
self.rotate_prob = rotate_prob
self.flip_prob = flip_prob
self.transforms = al.Compose(
[
transforms.RandomRotate90(),
transforms.Flip(),
transforms.HueSaturationValue(),
transforms.RandomBrightnessContrast(),
transforms.Transpose(),
OneOf([
transforms.RandomCrop(220, 220, p=0.5),
transforms.CenterCrop(220, 220, p=0.5)
],
p=0.5),
# transforms.Resize(352,352),
# transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
],
p=self.prob)
def augmentation(mode, target_size, prob=0.5, aug_m=2):
'''
description: augmentation
mode: 'train' 'test'
target_size: int or list, the shape of image ,
aug_m: Strength of transform
'''
high_p = prob
low_p = high_p / 2.0
M = aug_m
first_size = [int(x / 0.7) for x in target_size]
if mode == 'train':
return composition.Compose([
transforms.Resize(first_size[0], first_size[1], interpolation=3),
transforms.Flip(p=0.5),
composition.OneOf([
RandomCenterCut(scale=0.1 * M),
transforms.ShiftScaleRotate(shift_limit=0.05 * M,
scale_limit=0.1 * M,
rotate_limit=180,
border_mode=cv2.BORDER_CONSTANT,
value=0),
albumentations.imgaug.transforms.IAAAffine(
shear=(-10 * M, 10 * M), mode='constant')
],
p=high_p),
transforms.RandomBrightnessContrast(
brightness_limit=0.1 * M, contrast_limit=0.03 * M, p=high_p),
transforms.HueSaturationValue(hue_shift_limit=5 * M,
sat_shift_limit=15 * M,
val_shift_limit=10 * M,
p=high_p),
transforms.OpticalDistortion(distort_limit=0.03 * M,
shift_limit=0,
border_mode=cv2.BORDER_CONSTANT,
value=0,
p=low_p),
composition.OneOf([
transforms.Blur(blur_limit=7),
albumentations.imgaug.transforms.IAASharpen(),
transforms.GaussNoise(var_limit=(2.0, 10.0), mean=0),
transforms.ISONoise()
],
p=low_p),
transforms.Resize(target_size[0], target_size[1], interpolation=3),
transforms.Normalize(mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
max_pixel_value=255.0)
],
p=1)
else:
return composition.Compose([
transforms.Resize(target_size[0], target_size[1], interpolation=3),
transforms.Normalize(mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
max_pixel_value=255.0)
],
p=1)
def get_presize_combine_transforms_V4():
transforms_presize = A.Compose([
transforms.PadIfNeeded(600, 800),
geometric.Perspective(
scale=[0, .1],
pad_mode=cv2.BORDER_REFLECT,
interpolation=cv2.INTER_AREA, p = .3),
transforms.Flip(),
geometric.ShiftScaleRotate(interpolation=cv2.INTER_LANCZOS4, p = 0.95, scale_limit=0.0),
crops.RandomResizedCrop(
TARGET_SIZE, TARGET_SIZE,
scale=(config['rrc_scale_min'], config['rrc_scale_max']),
ratio=(.70, 1.4),
interpolation=cv2.INTER_CUBIC,
p=1.0),
transforms.Transpose()
#rotate.Rotate(interpolation=cv2.INTER_LANCZOS4, p = 0.99),
])
transforms_postsize = A.Compose([
#imgaug.IAAPiecewiseAffine(),
transforms.CoarseDropout(),
transforms.CLAHE(p=.1),
transforms.RandomToneCurve(scale=.1, p=0.2),
transforms.RandomBrightnessContrast(
brightness_limit=.1,
contrast_limit=0.4,
p=.8),
transforms.HueSaturationValue(
hue_shift_limit=20,
sat_shift_limit=50,
val_shift_limit=0,
p=0.5),
transforms.Equalize(p=0.05),
transforms.FancyPCA(p=0.05),
transforms.RandomGridShuffle(p=0.1),
A.OneOf([
transforms.MotionBlur(blur_limit=(3, 9)),
transforms.GaussianBlur(),
transforms.MedianBlur()
], p=0.1),
transforms.ISONoise(p=.2),
transforms.GaussNoise(var_limit=127., p=.3),
A.OneOf([
transforms.GridDistortion(interpolation=cv2.INTER_AREA, distort_limit=[0.7, 0.7], p=0.5),
transforms.OpticalDistortion(interpolation=cv2.INTER_AREA, p=.3),
], p=.3),
geometric.ElasticTransform(alpha=4, sigma=4, alpha_affine=4, interpolation=cv2.INTER_AREA, p=0.3),
transforms.CoarseDropout(),
transforms.Normalize(),
ToTensorV2()
])
return transforms_presize, transforms_postsize
def get_augumentation(phase, width=512, height=512, min_area=0., min_visibility=0., ft='coco'):
# from albumentations for all detection and segmentation
list_transforms = []
if phase == 'train':
list_transforms.extend([
transforms.LongestMaxSize(
max_size=width, always_apply=True),
albu.PadIfNeeded(min_height=height, min_width=width,
always_apply=True, border_mode=0, value=[0, 0, 0]),
transforms.RandomResizedCrop(
height=height,
width=width, p=0.3),
transforms.Flip(),
transforms.Transpose(),
albu.OneOf([
albu.RandomBrightnessContrast(brightness_limit=0.5,
contrast_limit=0.4),
albu.RandomGamma(gamma_limit=(50, 150)),
albu.NoOp()
]),
albu.OneOf([
albu.RGBShift(r_shift_limit=20, b_shift_limit=15,
g_shift_limit=15),
albu.HueSaturationValue(hue_shift_limit=5,
sat_shift_limit=5),
albu.NoOp()
]),
albu.CLAHE(p=0.8),
albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
])
if phase == 'test':
list_transforms.extend([
albu.Resize(height=height, width=width)
])
list_transforms.extend([
albu.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225), p=1),
ToTensorV2()
])
if phase == 'test':
return albu.Compose(list_transforms)
return albu.Compose(list_transforms, bbox_params=albu.BboxParams(format=ft,
min_area=min_area,
min_visibility=min_visibility,
label_fields=['category_id']))
def get_presize_combine_tune_transforms():
transforms_presize = A.Compose([
transforms.Transpose(),
transforms.Flip(),
#transforms.PadIfNeeded(600, 800),
crops.RandomResizedCrop(
TARGET_SIZE, TARGET_SIZE,
scale=(.75, 1),
interpolation=cv2.INTER_CUBIC,
p=1.0),
rotate.Rotate(interpolation=cv2.INTER_LANCZOS4, p = 0.99),
])
transforms_postsize = A.Compose([
transforms.CoarseDropout(),
# transforms.CLAHE(p=.1),
transforms.RandomToneCurve(scale=.1),
transforms.RandomBrightnessContrast(
brightness_limit=.1,
contrast_limit=0.2,
p=.7),
transforms.HueSaturationValue(
hue_shift_limit=20,
sat_shift_limit=60,
val_shift_limit=0,
p=0.6),
#transforms.Equalize(p=0.1),
#transforms.FancyPCA(p=0.05),
#transforms.RandomGridShuffle(p=0.1),
#A.OneOf([
# transforms.MotionBlur(blur_limit=(3, 9)),
# transforms.GaussianBlur(),
# transforms.MedianBlur()
# ], p=0.2),
transforms.ISONoise(p=.3),
transforms.GaussNoise(var_limit=255., p=.3),
#A.OneOf([
# transforms.GridDistortion(interpolation=cv2.INTER_AREA, distort_limit=[0.7, 0.7], p=0.5),
# transforms.OpticalDistortion(interpolation=cv2.INTER_AREA, p=.3),
# ], p=.3),
geometric.ElasticTransform(alpha=4, sigma=100, alpha_affine=100, interpolation=cv2.INTER_AREA, p=0.3),
transforms.CoarseDropout(),
transforms.Normalize(),
ToTensorV2()
])
return transforms_presize, transforms_postsize
def get_valid_transforms():
return A.Compose([
transforms.Transpose(),
transforms.PadIfNeeded(600, 800),
rotate.Rotate(interpolation=cv2.INTER_LANCZOS4, p = 0.90),
crops.RandomResizedCrop(
TARGET_SIZE_VALID, TARGET_SIZE_VALID,
scale=(.75, 1),
interpolation=cv2.INTER_CUBIC,
p=1.0),
transforms.Flip(),
#transforms.RandomToneCurve(scale=.1),
#transforms.RandomBrightnessContrast(brightness_limit=0.0, contrast_limit=0.3, p=.7),
#transforms.HueSaturationValue(hue_shift_limit=10,
# sat_shift_limit=10,
# val_shift_limit=5, p=0.6),
transforms.Normalize(),
ToTensorV2()
])
def get_train_transforms():
return A.Compose([
transforms.PadIfNeeded(600, 800),
geometric.ShiftScaleRotate(interpolation=cv2.INTER_LANCZOS4, p = 0.99, scale_limit=0.8),
geometric.Perspective(pad_mode=cv2.BORDER_REFLECT,interpolation=cv2.INTER_AREA),
crops.RandomResizedCrop(
TARGET_SIZE, TARGET_SIZE,
scale=(config['rrc_scale_min'], config['rrc_scale_max']),
interpolation=cv2.INTER_CUBIC,
p=1.0),
transforms.Transpose(),
transforms.Flip(),
transforms.CoarseDropout(),
transforms.CLAHE(p=.1),
transforms.RandomToneCurve(scale=.1),
transforms.RandomBrightnessContrast(
brightness_limit=.1,
contrast_limit=0.3,
p=.7),
transforms.HueSaturationValue(
hue_shift_limit=20,
sat_shift_limit=60,
val_shift_limit=0,
p=0.6),
transforms.RandomGridShuffle(p=0.1),
A.OneOf([
transforms.MotionBlur(blur_limit=(3, 9)),
transforms.GaussianBlur(),
transforms.MedianBlur()
], p=0.2),
transforms.ISONoise(p=.3),
transforms.GaussNoise(var_limit=255., p=.3),
A.OneOf([
transforms.GridDistortion(interpolation=cv2.INTER_AREA, distort_limit=[0.7, 0.7], p=0.5),
transforms.OpticalDistortion(interpolation=cv2.INTER_AREA, p=.3),
], p=.3),
geometric.ElasticTransform(alpha=4, sigma=100, alpha_affine=100, interpolation=cv2.INTER_AREA, p=0.3),
transforms.CoarseDropout(),
transforms.Normalize(),
ToTensorV2()
])
def train_dataloader(self):
augmentations = Compose(
[
A.RandomResizedCrop(
height=self.hparams.sz,
width=self.hparams.sz,
scale=(0.7, 1.0),
),
# AdvancedHairAugmentation(),
A.GridDistortion(),
A.RandomBrightnessContrast(),
A.ShiftScaleRotate(),
A.Flip(p=0.5),
A.CoarseDropout(
max_height=int(self.hparams.sz / 10),
max_width=int(self.hparams.sz / 10),
),
# A.HueSaturationValue(),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255,
),
ToTensorV2(),
]
)
train_ds = MelanomaDataset(
df=self.train_df,
images_path=self.train_images_path,
augmentations=augmentations,
train_or_valid=True,
)
return DataLoader(
train_ds,
# sampler=sampler,
batch_size=self.hparams.bs,
shuffle=True,
num_workers=os.cpu_count(),
pin_memory=True,
)
def get_tta_transforms():
return Compose([
A.RandomResizedCrop(
height=hparams.sz,
width=hparams.sz,
scale=(0.7, 1.0),
),
# AdvancedHairAugmentation(),
A.GridDistortion(),
A.RandomBrightnessContrast(),
A.ShiftScaleRotate(),
A.Flip(p=0.5),
A.CoarseDropout(
max_height=int(hparams.sz / 10),
max_width=int(hparams.sz / 10),
),
# A.HueSaturationValue(),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255,
),
ToTensorV2(),
])
def __init__(self, config):
train_transform = Compose([
transforms.RandomRotate90(),
transforms.Flip(),
transforms.Resize(config['input_h'], config['input_w']),
])
val_transform = Compose([
transforms.Resize(config['input_h'], config['input_w']),
])
super().__init__()
self.img_ext = config['img_ext']
self.mask_ext = config['mask_ext']
self.num_classes = config['num_classes']
self.num_workers = config['num_workers']
self.data_dir = os.path.join('data', config['dataset'],
config['sub_dataset'])
self.batch_size = config['batch_size']
self.pin_memory = config['pin_memory']
self.train_transform = train_transform
self.val_transform = val_transform
self.test_transform = None
self.train_ids, self.val_ids, self.test_ids = None, None, None
self.num_inputs = config['num_inputs']
def __init__(
self,
prob=0,
Flip_prob=0,
HueSaturationValue_prob=0,
RandomBrightnessContrast_prob=0,
crop_prob=0,
randomrotate90_prob=0,
elastictransform_prob=0,
gridistortion_prob=0,
opticaldistortion_prob=0,
verticalflip_prob=0,
horizontalflip_prob=0,
randomgamma_prob=0,
CoarseDropout_prob=0,
RGBShift_prob=0,
MotionBlur_prob=0,
MedianBlur_prob=0,
GaussianBlur_prob=0,
GaussNoise_prob=0,
ChannelShuffle_prob=0,
ColorJitter_prob=0,
):
super().__init__()
self.prob = prob
self.randomrotate90_prob = randomrotate90_prob
self.elastictransform_prob = elastictransform_prob
self.transforms = al.Compose(
[
transforms.RandomRotate90(p=randomrotate90_prob),
transforms.Flip(p=Flip_prob),
transforms.HueSaturationValue(p=HueSaturationValue_prob),
transforms.RandomBrightnessContrast(
p=RandomBrightnessContrast_prob),
transforms.Transpose(),
OneOf(
[
transforms.RandomCrop(220, 220, p=0.5),
transforms.CenterCrop(220, 220, p=0.5),
],
p=crop_prob,
),
ElasticTransform(
p=elastictransform_prob,
alpha=120,
sigma=120 * 0.05,
alpha_affine=120 * 0.03,
),
GridDistortion(p=gridistortion_prob),
OpticalDistortion(p=opticaldistortion_prob,
distort_limit=2,
shift_limit=0.5),
VerticalFlip(p=verticalflip_prob),
HorizontalFlip(p=horizontalflip_prob),
RandomGamma(p=randomgamma_prob),
RGBShift(p=RGBShift_prob),
MotionBlur(p=MotionBlur_prob, blur_limit=7),
MedianBlur(p=MedianBlur_prob, blur_limit=9),
GaussianBlur(p=GaussianBlur_prob, blur_limit=9),
GaussNoise(p=GaussNoise_prob),
ChannelShuffle(p=ChannelShuffle_prob),
CoarseDropout(p=CoarseDropout_prob,
max_holes=8,
max_height=32,
max_width=32),
ColorJitter(p=ColorJitter_prob)
# transforms.Resize(352, 352),
# transforms.Normalize(
# mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)
# ),
],
p=self.prob,
)
from glob import glob
train_img_paths = []
train_mask_paths = []
train_data_path = ["data/kvasir-seg/TrainDataset"]
for i in train_data_path:
train_img_paths.extend(glob(os.path.join(i, "images", "*")))
train_mask_paths.extend(glob(os.path.join(i, "masks", "*")))
train_img_paths.sort()
train_mask_paths.sort()
transforms = al.Compose(
[
transforms.RandomRotate90(),
transforms.Flip(),
transforms.HueSaturationValue(),
transforms.RandomBrightnessContrast(),
transforms.Transpose(),
OneOf(
[
transforms.RandomCrop(220, 220, p=0.5),
transforms.CenterCrop(220, 220, p=0.5),
],
p=1,
),
# transforms.Resize(352,352),
# transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
],
p=1,
)
def main():
# config = vars(parse_args_func())
#config_file = "../configs/config_v1.json"
args = vars(parse_args_func())
config_file = args['config']
config_dict = json.loads(open(config_file, 'rt').read())
# config_dict = json.loads(open(sys.argv[1], 'rt').read())
file_dict = config_dict['file_path']
config = config_dict['opt_config']
input_folder = file_dict['input_path'] # '../inputs'
checkpoint_folder = file_dict['checkpoint_path'] # '../checkpoint'
model_folder = file_dict['model_path'] # '../models'
if 'False' in config['deep_supervision']:
config['deep_supervision'] = False
else:
config['deep_supervision'] = True
if 'False' in config['nesterov']:
config['nesterov'] = False
else:
config['nesterov'] = True
if 'None' in config['name']:
config['name'] = None
if config['name'] is None:
config['name'] = '%s_%s_segmodel' % (config['dataset'], config['arch'])
os.makedirs(os.path.join(model_folder, '%s' % config['name']),
exist_ok=True)
print('-' * 20)
for key in config:
print('%s: %s' % (key, config[key]))
print('-' * 20)
with open(os.path.join(model_folder, '%s/config.yml' % config['name']),
'w') as f:
yaml.dump(config, f)
# define loss function (criterion)
if config['loss'] == 'BCEWithLogitsLoss':
criterion = nn.BCEWithLogitsLoss().cuda()
else:
criterion = losses.__dict__[config['loss']]().cuda()
cudnn.benchmark = True
# create model
if 'False' in config['resume']:
config['resume'] = False
else:
config['resume'] = True
# Data loading code
img_ids = glob(
os.path.join(input_folder, config['dataset'], 'images', 'training',
'*' + config['img_ext']))
train_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
img_ids = glob(
os.path.join(input_folder, config['val_dataset'], 'images',
'validation', '*' + config['img_ext']))
val_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
img_ids = glob(
os.path.join(input_folder, config['val_dataset'], 'images', 'test',
'*' + config['img_ext']))
test_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
train_transform = Compose([
# transforms.RandomScale ([config['scale_min'], config['scale_max']]),
# transforms.RandomRotate90(),
transforms.Rotate([config['rotate_min'], config['rotate_max']],
value=mean,
mask_value=0),
# transforms.GaussianBlur (),
transforms.Flip(),
# transforms.HorizontalFlip (),
transforms.HueSaturationValue(hue_shift_limit=10,
sat_shift_limit=10,
val_shift_limit=10),
transforms.RandomBrightnessContrast(brightness_limit=0.10,
contrast_limit=0.10,
brightness_by_max=True),
transforms.Resize(config['input_h'], config['input_w']),
transforms.Normalize(mean=mean, std=std),
])
val_transform = Compose([
transforms.Resize(config['input_h'], config['input_w']),
transforms.Normalize(mean=mean, std=std),
])
train_dataset = Dataset(img_ids=train_img_ids,
img_dir=os.path.join(input_folder,
config['dataset'], 'images',
'training'),
mask_dir=os.path.join(input_folder,
config['dataset'],
'annotations', 'training'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
input_channels=config['input_channels'],
transform=train_transform)
val_dataset = Dataset(img_ids=val_img_ids,
img_dir=os.path.join(input_folder, config['dataset'],
'images', 'validation'),
mask_dir=os.path.join(input_folder,
config['dataset'],
'annotations', 'validation'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
input_channels=config['input_channels'],
transform=val_transform)
test_dataset = Dataset(img_ids=val_img_ids,
img_dir=os.path.join(input_folder,
config['dataset'], 'images',
'test'),
mask_dir=os.path.join(input_folder,
config['dataset'],
'annotations', 'test'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
input_channels=config['input_channels'],
transform=val_transform)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['num_workers'],
drop_last=True)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=1, # config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
drop_last=False)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=1, # config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
drop_last=False)
log = OrderedDict([
('epoch', []),
('lr', []),
('loss', []),
('iou', []),
('dice', []),
('val_loss', []),
('val_iou', []),
('val_dice', []),
])
if not os.path.isdir(checkpoint_folder):
os.mkdir(checkpoint_folder)
# create generator model
#val_config = config_dict['config']
generator_name = config['generator_name']
with open(os.path.join(model_folder, '%s/config.yml' % generator_name),
'r') as f:
g_config = yaml.load(f, Loader=yaml.FullLoader)
generator = Generator(g_config)
generator.initialize_with_srresnet(model_folder, g_config)
lr = config['gan_lr']
# Initialize generator's optimizer
optimizer_g = torch.optim.Adam(params=filter(lambda p: p.requires_grad,
generator.parameters()),
lr=lr)
#params = filter(lambda p: p.requires_grad, generator.parameters())
#optimizer_g, scheduler_g = optimizer_scheduler(params, config)
# Discriminator
# Discriminator parameters
num_classes = config['num_classes']
kernel_size_d = 3 # kernel size in all convolutional blocks
n_channels_d = 64 # number of output channels in the first convolutional block, after which it is doubled in every 2nd block thereafter
n_blocks_d = 8 # number of convolutional blocks
fc_size_d = 1024 # size of the first fully connected layer
discriminator = Discriminator(num_classes,
kernel_size=kernel_size_d,
n_channels=n_channels_d,
n_blocks=n_blocks_d,
fc_size=fc_size_d)
# Initialize discriminator's optimizer
optimizer_d = torch.optim.Adam(params=filter(lambda p: p.requires_grad,
discriminator.parameters()),
lr=lr)
#params = filter(lambda p: p.requires_grad, discriminator.parameters())
#optimizer_d, scheduler_d = optimizer_scheduler(params, config)
adversarial_loss_criterion = nn.BCEWithLogitsLoss()
content_loss_criterion = nn.MSELoss()
generator = generator.cuda()
discriminator = discriminator.cuda()
#truncated_vgg19 = truncated_vgg19.cuda()
content_loss_criterion = content_loss_criterion.cuda()
adversarial_loss_criterion = adversarial_loss_criterion.cuda()
generator = torch.nn.DataParallel(generator)
discriminator = torch.nn.DataParallel(discriminator)
if not os.path.isdir(checkpoint_folder):
os.mkdir(checkpoint_folder)
log_name = config['name']
log_dir = os.path.join(checkpoint_folder, log_name)
writer = SummaryWriter(logdir=log_dir)
best_iou = 0
trigger = 0
Best_dice = 0
iou_AtBestDice = 0
start_epoch = 0
for epoch in range(start_epoch, config['epochs']):
print('Epoch [%d/%d]' % (epoch, config['epochs']))
# train for one epoch
train_log = train(epoch, config, train_loader, generator,
discriminator, criterion, adversarial_loss_criterion,
content_loss_criterion, optimizer_g, optimizer_d)
# evaluate on validation set
val_log = validate(config, val_loader, generator, criterion)
test_log = validate(config, test_loader, generator, criterion)
if Best_dice < test_log['dice']:
Best_dice = test_log['dice']
iou_AtBestDice = test_log['iou']
print(
'loss %.4f - iou %.4f - dice %.4f - val_loss %.4f - val_iou %.4f - val_dice %.4f - test_iou %.4f - test_dice %.4f - Best_dice %.4f - iou_AtBestDice %.4f'
% (train_log['loss'], train_log['iou'], train_log['dice'],
val_log['loss'], val_log['iou'], val_log['dice'],
test_log['iou'], test_log['dice'], Best_dice, iou_AtBestDice))
save_tensorboard(writer, train_log, val_log, test_log, epoch)
log['epoch'].append(epoch)
log['lr'].append(config['lr'])
log['loss'].append(train_log['loss'])
log['iou'].append(train_log['iou'])
log['dice'].append(train_log['dice'])
log['val_loss'].append(val_log['loss'])
log['val_iou'].append(val_log['iou'])
log['val_dice'].append(val_log['dice'])
pd.DataFrame(log).to_csv(os.path.join(model_folder,
'%s/log.csv' % config['name']),
index=False)
trigger += 1
if test_log['iou'] > best_iou:
torch.save(
generator.state_dict(),
os.path.join(model_folder, '%s/model.pth' % config['name']))
best_iou = test_log['iou']
print("=> saved best model")
trigger = 0
# early stopping
if config['early_stopping'] >= 0 and trigger >= config[
'early_stopping']:
print("=> early stopping")
break
torch.cuda.empty_cache()
def main():
config = vars(parse_args())
if config['name'] is None:
if config['deep_supervision']:
config['name'] = '%s_%s_wDS' % (config['dataset'], config['arch'])
else:
config['name'] = '%s_%s_woDS' % (config['dataset'], config['arch'])
os.makedirs('models/%s' % config['name'], exist_ok=True)
print('-' * 20)
for key in config:
print('%s: %s' % (key, config[key]))
print('-' * 20)
with open('models/%s/config.yml' % config['name'], 'w') as f:
yaml.dump(config, f)
# define loss function (criterion)
if config['loss'] == 'BCEWithLogitsLoss':
criterion = nn.BCEWithLogitsLoss().cuda()
else:
criterion = losses.__dict__[config['loss']]().cuda()
cudnn.benchmark = True
# create model
print("=> creating model %s" % config['arch'])
model = archs.__dict__[config['arch']](config['num_classes'],
config['input_channels'],
config['deep_supervision'])
model = model.cuda()
params = filter(lambda p: p.requires_grad, model.parameters())
if config['optimizer'] == 'Adam':
optimizer = optim.Adam(params,
lr=config['lr'],
weight_decay=config['weight_decay'])
elif config['optimizer'] == 'SGD':
optimizer = optim.SGD(params,
lr=config['lr'],
momentum=config['momentum'],
nesterov=config['nesterov'],
weight_decay=config['weight_decay'])
else:
raise NotImplementedError
if config['scheduler'] == 'CosineAnnealingLR':
scheduler = lr_scheduler.CosineAnnealingLR(optimizer,
T_max=config['epochs'],
eta_min=config['min_lr'])
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer,
factor=config['factor'],
patience=config['patience'],
verbose=1,
min_lr=config['min_lr'])
elif config['scheduler'] == 'MultiStepLR':
scheduler = lr_scheduler.MultiStepLR(
optimizer,
milestones=[int(e) for e in config['milestones'].split(',')],
gamma=config['gamma'])
elif config['scheduler'] == 'ConstantLR':
scheduler = None
else:
raise NotImplementedError
# Data loading code
img_ids = glob(
os.path.join('inputs', config['dataset'], 'images',
'*' + config['img_ext']))
img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
train_img_ids, val_img_ids = train_test_split(img_ids,
test_size=0.2,
random_state=41)
#######################
train_img_ids = img_ids
#######################
train_transform = Compose([
transforms.RandomRotate90(),
transforms.Flip(),
OneOf([
transforms.HueSaturationValue(),
transforms.RandomBrightness(),
transforms.RandomContrast(),
],
p=1),
transforms.Resize(config['input_h'], config['input_w']),
transforms.Normalize(),
])
val_transform = Compose([
transforms.Resize(config['input_h'], config['input_w']),
transforms.Normalize(),
])
train_dataset = Dataset(img_ids=train_img_ids,
img_dir=os.path.join('inputs', config['dataset'],
'images'),
mask_dir=os.path.join('inputs', config['dataset'],
'masks'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
transform=train_transform)
val_dataset = Dataset(img_ids=val_img_ids,
img_dir=os.path.join('inputs', config['dataset'],
'images'),
mask_dir=os.path.join('inputs', config['dataset'],
'masks'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
transform=val_transform)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['num_workers'],
drop_last=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
drop_last=False)
log = OrderedDict([
('epoch', []),
('lr', []),
('loss', []),
('iou', []),
('val_loss', []),
('val_iou', []),
])
best_iou = 0
trigger = 0
for epoch in range(config['epochs']):
print('Epoch [%d/%d]' % (epoch, config['epochs']))
# train for one epoch
train_log = train(config, train_loader, model, criterion, optimizer)
# evaluate on validation set
val_log = validate(config, val_loader, model, criterion)
if config['scheduler'] == 'CosineAnnealingLR':
scheduler.step()
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler.step(val_log['loss'])
print('loss %.4f - iou %.4f - val_loss %.4f - val_iou %.4f' %
(train_log['loss'], train_log['iou'], val_log['loss'],
val_log['iou']))
log['epoch'].append(epoch)
log['lr'].append(config['lr'])
log['loss'].append(train_log['loss'])
log['iou'].append(train_log['iou'])
log['val_loss'].append(val_log['loss'])
log['val_iou'].append(val_log['iou'])
pd.DataFrame(log).to_csv('models/%s/log.csv' % config['name'],
index=False)
trigger += 1
if val_log['iou'] > best_iou:
torch.save(model.state_dict(),
'models/%s/model.pth' % config['name'])
best_iou = val_log['iou']
print("=> saved best model")
trigger = 0
# early stopping
if config['early_stopping'] >= 0 and trigger >= config[
'early_stopping']:
print("=> early stopping")
break
torch.cuda.empty_cache()
def get_transforms(phase: str, cli_args) -> Dict[str, Compose]:
"""Get composed albumentations augmentations
Parameters
----------
phase : str
Phase of learning
In ['train', 'val']
cli_args
Arguments coming all the way from `main.py`
Returns
-------
transforms: dict[str, albumentations.core.composition.Compose]
Composed list of transforms
"""
aug_transforms = []
im_sz = (cli_args.image_size, cli_args.image_size)
if phase == "train":
# Data augmentation for training only
aug_transforms.extend([
tf.ShiftScaleRotate(
shift_limit=0,
scale_limit=0.1,
rotate_limit=15,
p=0.5),
tf.Flip(p=0.5),
tf.RandomRotate90(p=0.5),
])
# Exotic Augmentations for train only 🤤
aug_transforms.extend([
tf.RandomBrightnessContrast(p=0.5),
tf.ElasticTransform(p=0.5),
tf.MultiplicativeNoise(multiplier=(0.5, 1.5),
per_channel=True, p=0.2),
])
aug_transforms.extend([
tf.RandomSizedCrop(min_max_height=im_sz,
height=im_sz[0],
width=im_sz[1],
w2h_ratio=1.0,
interpolation=cv2.INTER_LINEAR,
p=1.0),
])
aug_transforms = Compose(aug_transforms)
mask_only_transforms = Compose([
tf.Normalize(mean=0, std=1, always_apply=True)
])
image_only_transforms = Compose([
tf.Normalize(mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0),
always_apply=True)
])
final_transforms = Compose([
ToTensorV2()
])
transforms = {
'aug': aug_transforms,
'img_only': image_only_transforms,
'mask_only': mask_only_transforms,
'final': final_transforms
}
return transforms
def main():
config = vars(parse_args())
now = datetime.datetime.now()
if config["name"] is None:
if config["deep_supervision"]:
config["name"] = "%s_%s_wDS_%s" % (
config["dataset"],
config["arch"],
now.strftime("%Y%m%d_%H%M%S"),
)
else:
config["name"] = "%s_%s_woDS_%s" % (
config["dataset"],
config["arch"],
now.strftime("%Y%m%d_%H%M%S"),
)
output_path = os.path.join(cfg.UNET_RESULTS_DIR, config["name"])
try:
os.makedirs(output_path, exist_ok=True)
except Exception as e:
print(e)
models_path = os.path.join(output_path, "models")
os.mkdir(models_path)
with open(os.path.join(models_path, "config.yml"), "w") as f:
yaml.dump(config, f)
print("-" * 20)
for key in config:
print("%s: %s" % (key, config[key]))
print("-" * 20)
# Tensorboad 用のログを記録するディレクトリパス
log_dir = os.path.join(output_path, "log")
os.mkdir(log_dir)
writer = SummaryWriter(log_dir=log_dir)
# define loss function(criterion)
if config["loss"] == "BCEWithLogitsLoss":
criterion = nn.BCEWithLogitsLoss().cuda()
else:
criterion = losses.__dict__[config["loss"]]().cuda()
cudnn.benchmark = True
# create model
print("=> creating model %s" % config["arch"])
model = archs.__dict__[config["arch"]](config["num_classes"],
config["input_channels"],
config["deep_supervision"])
model = model.cuda()
# モデルを TensorBorad で表示するため，ログに保存
# image = torch.randn(1, 3, 2224, 224)
# writer.add_graph(model, image)
params = filter(lambda p: p.requires_grad, model.parameters())
if config["optimizer"] == "Adam":
optimizer = optim.Adam(params,
lr=config["lr"],
weight_decay=config["weight_decay"])
elif config["optimizer"] == "SGD":
optimizer = optim.SGD(
params,
lr=config["lr"],
momentum=config["momentum"],
nesterov=config["nesterov"],
weight_decay=config["weight_decay"],
)
else:
raise NotImplementedError
# scheduler
if config["scheduler"] == "CosineAnnealingLR":
scheduler = lr_scheduler.CosineAnnealingLR(optimizer=optimizer,
T_max=config["epochs"],
eta_min=config["min_lr"])
elif config["scheduler"] == "ReduceLROnPlateau":
scheduler = lr_scheduler(
optimizer=optimizer,
factor=config["factor"],
patience=config["patience"],
verbose=1,
min_lr=config["min_lr"],
)
elif config["scheduler"] == "MultiStepLR":
scheduler = lr_scheduler.MultiStepLR(
optimizer=optimizer,
milestones=[int(e) for e in config["milestones"].split(",")],
gamma=config["gamma"],
)
elif config["scheduler"] == "ConstantLR":
scheduler = None
else:
raise NotImplementedError
# Data loading code
if config["dataset"] == "dsb2018_96":
input_dir = cfg.DSB2018_96_DIR
img_ids = glob(
os.path.join(input_dir, "images", "*" + config["img_ext"]))
img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
train_img_ids, val_img_ids = train_test_split(img_ids,
test_size=0.2,
random_state=41)
train_transform = Compose([
transforms.RandomRotate90(),
transforms.Flip(),
OneOf(
[
transforms.HueSaturationValue(),
transforms.RandomBrightness(),
transforms.RandomContrast(),
],
p=1,
),
transforms.Resize(config["input_h"], config["input_w"]),
transforms.Normalize(),
])
val_transform = Compose([
transforms.Resize(config["input_h"], config["input_w"]),
transforms.Normalize(),
])
train_dataset = Dataset(
img_ids=train_img_ids,
img_dir=os.path.join(input_dir, "images"),
mask_dir=os.path.join(input_dir, "masks"),
img_ext=config["img_ext"],
mask_ext=config["mask_ext"],
num_classes=config["num_classes"],
transform=train_transform,
)
val_dataset = Dataset(
img_ids=val_img_ids,
img_dir=os.path.join(input_dir, "images"),
mask_dir=os.path.join(input_dir, "masks"),
img_ext=config["img_ext"],
mask_ext=config["mask_ext"],
num_classes=config["num_classes"],
transform=val_transform,
)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config["batch_size"],
shuffle=True,
num_workers=config["num_workers"],
drop_last=True,
)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=config["batch_size"],
shuffle=False,
num_workers=config["num_workers"],
drop_last=False,
)
log = OrderedDict([
("epoch", []),
("lr", []),
("loss", []),
("iou", []),
("val_loss", []),
("val_iou", []),
])
best_iou = 0
trigger = 0
for epoch in range(config["epochs"] + 1):
print("Epoch [%d/%d]" % (epoch, config["epochs"]))
# train for one epoch
train_log = train(config, train_loader, model, criterion, optimizer)
# evaluate on validation set
val_log = validate(config, val_loader, model, criterion)
if config["scheduler"] == "CosineAnnealingLR":
scheduler.step()
elif config["scheduler"] == "ReduceLROnPlateau":
scheduler.step(val_log["loss"])
print("loss %.4f - iou %.4f - val_loss %.4f - val_iou %.4f" %
(train_log["loss"], train_log["iou"], val_log["loss"],
val_log["iou"]))
log["epoch"].append(epoch)
log["lr"].append(config["lr"])
log["loss"].append(train_log["loss"])
log["iou"].append(train_log["iou"])
log["val_loss"].append(val_log["loss"])
log["val_iou"].append(val_log["iou"])
# Tensorboard用のデータ
writer.add_scalar("training loss", train_log["loss"], epoch)
writer.add_scalar("validation loss", val_log["loss"], epoch)
pd.DataFrame(log).to_csv("%s/log.csv" % (log_dir), index=False)
if epoch == 0:
best_loss = val_log["loss"]
trigger += 1
# Best Model Save
# if val_log['iou'] > best_iou:
if (val_log["iou"] > best_iou) and (val_log["loss"] <= best_loss):
torch.save(model.state_dict(), "%s/model.pth" % (models_path))
best_iou = val_log["iou"]
best_loss = val_log["loss"]
print("=> saved best model")
trigger = 0
# early stopping
if (config["early_stopping"] >= 0 and
trigger >= config["early_stopping"]) or val_log["loss"] < 1e-4:
print("=> early stopping")
break
torch.cuda.empty_cache()
# summary writer を必要としない場合，close()メソッドを呼び出す
writer.close()
def main():
args = vars(parse_args_func())
#config_file = "../configs/config_SN7.json"
config_file = args['config'] # "../configs/config_v1.json"
config_dict = json.loads(open(config_file, 'rt').read())
#config_dict = json.loads(open(sys.argv[1], 'rt').read())
file_dict = config_dict['file_path']
config = config_dict['opt_config']
input_folder = file_dict['input_path'] # '../inputs'
checkpoint_folder = file_dict['checkpoint_path'] # '../checkpoint'
model_folder = file_dict['model_path'] # '../models'
if 'False' in config['deep_supervision']:
config['deep_supervision'] = False
else:
config['deep_supervision'] = True
if 'False' in config['nesterov']:
config['nesterov'] = False
else:
config['nesterov'] = True
if 'None' in config['name']:
config['name'] = None
if config['name'] is None:
config['name'] = '%s_%s_segmodel' % (config['dataset'], config['arch'])
os.makedirs(os.path.join(model_folder, '%s' % config['name']),
exist_ok=True)
if not os.path.isdir(checkpoint_folder):
os.mkdir(checkpoint_folder)
log_name = config['name']
log_dir = os.path.join(checkpoint_folder, log_name)
writer = SummaryWriter(logdir=log_dir)
print('-' * 20)
for key in config:
print('%s: %s' % (key, config[key]))
print('-' * 20)
with open(os.path.join(model_folder, '%s/config.yml' % config['name']),
'w') as f:
yaml.dump(config, f)
# define loss function (criterion)
if config['loss'] == 'BCEWithLogitsLoss':
criterion = nn.BCEWithLogitsLoss().cuda()
else:
criterion = losses.__dict__[config['loss']]().cuda()
cudnn.benchmark = True
# create model
print("=> creating model %s" % config['arch'])
model = archs.__dict__[config['arch']](config['num_classes'],
config['input_channels'],
config['deep_supervision'])
if 'False' in config['resume']:
config['resume'] = False
else:
config['resume'] = True
resume_flag = False
if resume_flag == True:
save_path = os.path.join(model_folder, config['name'], 'model.pth')
weights = torch.load(save_path)
model.load_state_dict(weights)
name_yaml = config['name']
with open(os.path.join(model_folder, '%s/config.yml' % name_yaml),
'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
#start_epoch = config['epochs']
start_epoch = 0
else:
start_epoch = 0
model = model.cuda()
if 'effnet' in config['arch']:
eff_flag = True
else:
eff_flag = False
if eff_flag == True:
cnn_subs = list(model.encoder.eff_conv.children())[1:]
#cnn_params = [list(sub_module.parameters()) for sub_module in cnn_subs]
#cnn_params = [item for sublist in cnn_params for item in sublist]
summary(model,
(config['input_channels'], config['input_w'], config['input_h']))
params = filter(lambda p: p.requires_grad, model.parameters())
if eff_flag == True:
params = list(params) + list(model.encoder.conv_a.parameters())
model = torch.nn.DataParallel(model)
if config['optimizer'] == 'Adam':
optimizer = optim.Adam(params,
lr=config['lr'],
weight_decay=config['weight_decay'])
elif config['optimizer'] == 'SGD':
optimizer = optim.SGD(params,
lr=config['lr'],
momentum=config['momentum'],
nesterov=config['nesterov'],
weight_decay=config['weight_decay'])
else:
raise NotImplementedError
if eff_flag == True:
cnn_params = [list(sub_module.parameters()) for sub_module in cnn_subs]
cnn_params = [item for sublist in cnn_params for item in sublist]
cnn_optimizer = torch.optim.Adam(cnn_params,
lr=0.001,
weight_decay=config['weight_decay'])
#cnn_optimizer = None
else:
cnn_optimizer = None
if config['optimizer'] == 'SGD':
if config['scheduler'] == 'CosineAnnealingLR':
scheduler = lr_scheduler.CosineAnnealingLR(
optimizer, T_max=config['epochs'], eta_min=config['min_lr'])
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler = lr_scheduler.ReduceLROnPlateau(
optimizer,
factor=config['factor'],
patience=config['patience'],
verbose=1,
min_lr=config['min_lr'])
elif config['scheduler'] == 'MultiStepLR':
scheduler = lr_scheduler.MultiStepLR(
optimizer,
milestones=[int(e) for e in config['milestones'].split(',')],
gamma=config['gamma'])
elif config['scheduler'] == 'ConstantLR':
scheduler = None
else:
raise NotImplementedError
else:
scheduler = None
# Data loading code
img_ids = glob(
os.path.join(input_folder, config['dataset'], 'images', 'training',
'*' + config['img_ext']))
train_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
#img_dir = os.path.join(input_folder, config['dataset'], 'images', 'training')
#mask_dir = os.path.join(input_folder, config['dataset'], 'annotations', 'training')
#train_image_mask = image_to_afile(img_dir, mask_dir, None, train_img_ids, config)
img_ids = glob(
os.path.join(input_folder, config['val_dataset'], 'images',
'validation', '*' + config['img_ext']))
val_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
img_ids = glob(
os.path.join(input_folder, config['val_dataset'], 'images', 'test',
'*' + config['img_ext']))
test_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
train_transform = Compose([
#transforms.RandomScale ([config['scale_min'], config['scale_max']]),
#transforms.RandomRotate90(),
transforms.Rotate([config['rotate_min'], config['rotate_max']],
value=mean,
mask_value=0),
transforms.Flip(),
#transforms.HorizontalFlip (),
transforms.HueSaturationValue(hue_shift_limit=10,
sat_shift_limit=10,
val_shift_limit=10),
transforms.RandomBrightnessContrast(brightness_limit=0.10,
contrast_limit=0.10,
brightness_by_max=True),
transforms.Resize(config['input_h'], config['input_w']),
transforms.Normalize(mean=mean, std=std),
])
val_transform = Compose([
transforms.Resize(config['input_h'], config['input_w']),
transforms.Normalize(mean=mean, std=std),
])
train_dataset = Dataset(img_ids=train_img_ids,
img_dir=os.path.join(input_folder,
config['dataset'], 'images',
'training'),
mask_dir=os.path.join(input_folder,
config['dataset'],
'annotations', 'training'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
input_channels=config['input_channels'],
transform=train_transform,
from_file=None)
val_dataset = Dataset(img_ids=val_img_ids,
img_dir=os.path.join(input_folder,
config['val_dataset'], 'images',
'validation'),
mask_dir=os.path.join(input_folder,
config['val_dataset'],
'annotations', 'validation'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
input_channels=config['input_channels'],
transform=val_transform,
from_file=None)
test_dataset = Dataset(img_ids=test_img_ids,
img_dir=os.path.join(input_folder,
config['val_dataset'],
'images', 'test'),
mask_dir=os.path.join(input_folder,
config['val_dataset'],
'annotations', 'test'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
input_channels=config['input_channels'],
transform=val_transform,
from_file=None)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['num_workers'],
drop_last=True)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=1, #config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
drop_last=False)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=1, #config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
drop_last=False)
log = OrderedDict([
('epoch', []),
('lr', []),
('loss', []),
('iou', []),
('dice', []),
('val_loss', []),
('val_iou', []),
('val_dice', []),
])
best_iou = 0
trigger = 0
Best_dice = 0
iou_AtBestDice = 0
for epoch in range(start_epoch, config['epochs']):
print('{:s} Epoch [{:d}/{:d}]'.format(config['arch'], epoch,
config['epochs']))
# train for one epoch
train_log = train(epoch, config, train_loader, model, criterion,
optimizer, cnn_optimizer)
if config['optimizer'] == 'SGD':
if config['scheduler'] == 'CosineAnnealingLR':
scheduler.step()
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler.step(val_log['loss'])
elif config['scheduler'] == 'MultiStepLR':
scheduler.step()
# evaluate on validation set
val_log = validate(config, val_loader, model, criterion)
test_log = validate(config, test_loader, model, criterion)
if Best_dice < test_log['dice']:
Best_dice = test_log['dice']
iou_AtBestDice = test_log['iou']
print(
'loss %.4f - iou %.4f - dice %.4f - val_loss %.4f - val_iou %.4f - val_dice %.4f - test_iou %.4f - test_dice %.4f - Best_dice %.4f - iou_AtBestDice %.4f'
% (train_log['loss'], train_log['iou'], train_log['dice'],
val_log['loss'], val_log['iou'], val_log['dice'],
test_log['iou'], test_log['dice'], Best_dice, iou_AtBestDice))
save_tensorboard(writer, train_log, val_log, test_log, epoch)
log['epoch'].append(epoch)
log['lr'].append(config['lr'])
log['loss'].append(train_log['loss'])
log['iou'].append(train_log['iou'])
log['dice'].append(train_log['dice'])
log['val_loss'].append(val_log['loss'])
log['val_iou'].append(val_log['iou'])
log['val_dice'].append(val_log['dice'])
pd.DataFrame(log).to_csv(os.path.join(model_folder,
'%s/log.csv' % config['name']),
index=False)
trigger += 1
if val_log['iou'] > best_iou:
torch.save(
model.state_dict(),
os.path.join(model_folder, '%s/model.pth' % config['name']))
best_iou = val_log['iou']
print("=> saved best model")
trigger = 0
# early stopping
if config['early_stopping'] >= 0 and trigger >= config[
'early_stopping']:
print("=> early stopping")
break
torch.cuda.empty_cache()
def train_entry(config, rank=-1, world_size=0):
if (rank > 0):
setup(rank, world_size)
os.makedirs('models/%s' % config['name'], exist_ok=True)
with open('models/%s/config.yml' % config['name'], 'w') as f:
yaml.dump(config, f)
# define loss function (criterion)
if config['loss'] == 'BCEWithLogitsLoss':
criterion = nn.BCEWithLogitsLoss().cuda()
else:
criterion = losses.__dict__[config['loss']]().cuda()
cudnn.benchmark = True
# create model
arch = config['arch'].split('_')[0]
print("=> creating model %s" % arch)
model = getattr(archs, arch)(config['num_classes'],
config['input_channels'],
config['deep_supervision'])
model = nn.DataParallel(model)
print(f'rank={rank}, device={torch.cuda.current_device()}')
if rank >= 0:
model = DDP(model, device_ids=[rank])
else:
model = model.cuda()
# load trained model
model_file = 'models/%s/model.pth' % config['name']
if os.path.isfile(model_file):
print("Reloading model ...")
model.load_state_dict(torch.load(model_file))
params = filter(lambda p: p.requires_grad, model.parameters())
if config['optimizer'] == 'Adam':
optimizer = optim.Adam(
params, lr=config['lr'], weight_decay=config['weight_decay'])
elif config['optimizer'] == 'SGD':
optimizer = optim.SGD(params, lr=config['lr'], momentum=config['momentum'],
nesterov=config['nesterov'], weight_decay=config['weight_decay'])
else:
raise NotImplementedError
if config['scheduler'] == 'CosineAnnealingLR':
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=config['epochs'], eta_min=config['min_lr'])
elif config['scheduler'] == 'CosineAnnealingWarmRestarts':
scheduler = lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=50)
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=config['factor'], patience=config['patience'], min_lr=config['min_lr'])
elif config['scheduler'] == 'MultiStepLR':
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[int(e) for e in config['milestones'].split(',')], gamma=config['gamma'])
elif config['scheduler'] == 'ConstantLR':
scheduler = None
else:
raise NotImplementedError
# Data loading code
data_path = os.path.join('data', config['dataset'])
img_ids = glob(os.path.join(data_path, config['sub_dataset'], 'images', '*' + config['img_ext']))
# img_ids = [os.path.basename(p) for p in img_ids]
img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
if config['num_inputs'] > 0:
img_ids = img_ids[:config['num_inputs']]
train_img_ids, val_img_ids = train_test_split(img_ids, test_size=0.2, random_state=41)
split_filename = os.path.join(data_path, 'split.p')
if config['save_split'] is True:
split_file = open(split_filename, 'wb')
pickle.dump((train_img_ids, val_img_ids), split_file)
if config['load_split'] is True:
split_file = open(split_filename, 'rb')
train_img_ids, val_img_ids = pickle.load(split_file)
'''
Since it is gray image, some augmentation methods not used
'''
# read statistics
with open('data_processing/data_statistics.json') as json_file:
data = json.load(json_file)
entry = '%s_%s' % (config['dataset'], config['sub_dataset'])
mean = data[entry]['mean']
std = data[entry]['std']
print("%s: mean=[%s] std=[%s]" % (config['dataset'], ', '.join(map(str, mean)), ', '.join(map(str, std))))
# data normalization
normalize = transforms.Normalize(mean=mean, std=std)
train_transform = Compose([
transforms.RandomRotate90(),
transforms.Flip(),
# transforms.HorizontalFlip(),
# transforms.Rotate(limit=5),
# OneOf([
# transforms.HueSaturationValue(),
# transforms.RandomBrightnessContrast(),
# # transforms.RandomContrast(),
# ], p=1),
transforms.Resize(config['input_h'], config['input_w']),
# normalize,
])
val_transform = Compose([
transforms.Resize(config['input_h'], config['input_w']),
# normalize,
])
train_dataset = Dataset(
img_ids=train_img_ids,
img_dir=os.path.join('data', config['dataset'], config['sub_dataset'], 'images'),
mask_dir=os.path.join('data', config['dataset'], config['sub_dataset'], 'masks'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
transform=train_transform)
val_dataset = Dataset(
img_ids=val_img_ids,
img_dir=os.path.join('data', config['dataset'], config['sub_dataset'], 'images'),
mask_dir=os.path.join('data', config['dataset'], config['sub_dataset'], 'masks'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
transform=val_transform)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['num_workers'],
drop_last=True)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
drop_last=False)
log = OrderedDict([
('epoch', []),
('lr', []),
('loss', []),
('iou', []),
('dice', []),
('val_loss', []),
('val_iou', []),
('val_dice', [])
])
log_per_class = []
for _ in range(config['num_classes']):
log_per_class.append(OrderedDict([('val_iou', []), ('val_dice', [])]))
best_iou = 0
best_iou_per_class = [0] * config['num_classes']
trigger = 0
for epoch in range(config['epochs']):
print('Epoch [%d/%d]' % (epoch, config['epochs']))
# train for one epoch
train_log = train(config, train_loader, model, criterion, optimizer)
# evaluate on validation set
val_log = validate(config, val_loader, model, criterion)
# evaluate on validation set for each class
val_per_class_log = test_per_class(config, val_loader, model)
if config['scheduler'] == 'CosineAnnealingLR':
scheduler.step()
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler.step(val_log['loss'])
print('loss %.4f - iou %.4f - dice %.4f - val_loss %.4f - val_iou %.4f - val_dice %.4f'
% (train_log['loss'], train_log['iou'], train_log['dice'],
val_log['loss'], val_log['iou'], val_log['dice']))
log['epoch'].append(epoch)
log['lr'].append(config['lr'])
log['loss'].append(train_log['loss'])
log['iou'].append(train_log['iou'])
log['dice'].append(train_log['dice'])
log['val_loss'].append(val_log['loss'])
log['val_iou'].append(val_log['iou'])
log['val_dice'].append(val_log['dice'])
pd.DataFrame(log).to_csv('models/%s/log.csv' %
config['name'], index=False)
# write into log for each class
for class_id in range(config['num_classes']):
log_per_class[class_id]['val_iou'].append(val_per_class_log[class_id]['iou'])
log_per_class[class_id]['val_dice'].append(val_per_class_log[class_id]['dice'])
print('[class %d] val_iou %.4f - val_dice %.4f'
% (class_id, val_per_class_log[class_id]['iou'], val_per_class_log[class_id]['dice']))
pd.DataFrame(log_per_class[class_id]).to_csv('models/%s/log_%d.csv' %
(config['name'], class_id), index=False)
if val_per_class_log[class_id]['iou'] > best_iou_per_class[class_id]:
torch.save(model.state_dict(), 'models/%s/model_%d.pth' % (config['name'], class_id))
best_iou_per_class[class_id] = val_per_class_log[class_id]['iou']
print("===> saved best model for class %d" % class_id)
trigger += 1
# torch.save(model.state_dict(), 'models/%s/model.pth' % config['name'])
if val_log['iou'] > best_iou:
torch.save(model.state_dict(), 'models/%s/model.pth' %
config['name'])
best_iou = val_log['iou']
print("=> saved best model")
trigger = 0
# early stopping
if config['early_stopping'] >= 0 and trigger >= config['early_stopping']:
print("=> early stopping")
break
torch.cuda.empty_cache()
print("\n[Best Results:]")
print('- [Overall IoU] %.4f' % best_iou)
for class_id in range(config['num_classes']):
print('-- [class %d IoU] %.4f' % (class_id, best_iou_per_class[class_id]))
if rank >= 0:
cleanup()
return best_iou
