def get_transformv2(opt):
transform_list = []
# Transforms in opt.preprocess
if 'fixsize' in opt.preprocess:
transform_list.append(tr.Resize(286, 286, interpolation=2, p=1))
if 'resize' in opt.preprocess:
transform_list.append(
tr.Resize(opt.load_size, opt.load_size, interpolation=2, p=1))
if 'crop' in opt.preprocess:
transform_list.append(tr.RandomCrop(opt.crop_size, opt.crop_size, p=1))
# Transforms in colorspace
if 'color' in opt.preprocess:
transform_list.extend([
tr.RandomContrast(limit=0.2, p=0.5),
tr.RandomBrightness(limit=0.2, p=0.5),
tr.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
p=0.5),
# tr.ISONoise()
])
# Necessary transforms
transform_list.extend([
tr.HorizontalFlip(p=0.5),
tr.VerticalFlip(p=0.5),
tr.Normalize(p=1.0),
ToTensorV2(p=1)
])
return Compose(transform_list, additional_targets={'imageB': 'image'})
def __init__(self, im_paths=None, labels=None, phase=None, resize=False):
"""
Args:
im_paths (numpy): image_data
y (numpy): label data
transform: pytorch transforms for transforms and tensor conversion
"""
self.im_paths = im_paths
self.labels = labels
self.resize = resize
self.albumentations_transform = {
'train':
Compose([
ab_transforms.HorizontalFlip(p=0.2),
ab_transforms.VerticalFlip(p=0.2),
ab_transforms.Rotate(limit=180, p=0.2),
ab_transforms.HueSaturationValue(p=0.1),
ab_transforms.RandomContrast(p=0.1),
ab_transforms.GaussianBlur(blur_limit=3, p=0.2),
ab_transforms.GaussNoise(p=0.05),
ab_transforms.CLAHE(p=0.2),
ab_transforms.Normalize(mean=[0.5944, 0.4343, 0.5652],
std=[0.2593, 0.2293, 0.2377]),
ToTensor()
]),
'val':
Compose([
ab_transforms.Normalize(mean=[0.5944, 0.4343, 0.5652],
std=[0.2593, 0.2293, 0.2377]),
ToTensor()
]),
}
if phase == 'train':
self.transform = self.albumentations_transform['train']
else:
self.transform = self.albumentations_transform['val']
def main():
args = parse_args()
if args.name is None:
args.name = '%s_%s' % (args.arch, datetime.now().strftime('%m%d%H'))
if not os.path.exists('models/%s' % args.name):
os.makedirs('models/%s' % args.name)
if args.resume:
args = joblib.load('models/%s/args.pkl' % args.name)
args.resume = True
print('Config -----')
for arg in vars(args):
print('- %s: %s' % (arg, getattr(args, arg)))
print('------------')
with open('models/%s/args.txt' % args.name, 'w') as f:
for arg in vars(args):
print('- %s: %s' % (arg, getattr(args, arg)), file=f)
joblib.dump(args, 'models/%s/args.pkl' % args.name)
if args.seed is not None and not args.resume:
print('set random seed')
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.loss == 'BCEWithLogitsLoss':
criterion = BCEWithLogitsLoss().cuda()
elif args.loss == 'WeightedBCEWithLogitsLoss':
criterion = BCEWithLogitsLoss(weight=torch.Tensor([1., 1., 1., 1., 1., 2.]),
smooth=args.label_smooth).cuda()
elif args.loss == 'FocalLoss':
criterion = FocalLoss().cuda()
elif args.loss == 'WeightedFocalLoss':
criterion = FocalLoss(weight=torch.Tensor([1., 1., 1., 1., 1., 2.])).cuda()
else:
raise NotImplementedError
if args.pred_type == 'all':
num_outputs = 6
elif args.pred_type == 'except_any':
num_outputs = 5
else:
raise NotImplementedError
cudnn.benchmark = True
# create model
model = get_model(model_name=args.arch,
num_outputs=num_outputs,
freeze_bn=args.freeze_bn,
dropout_p=args.dropout_p,
pooling=args.pooling,
lp_p=args.lp_p)
model = model.cuda()
train_transform = Compose([
transforms.Resize(args.img_size, args.img_size),
transforms.HorizontalFlip() if args.hflip else NoOp(),
transforms.VerticalFlip() if args.vflip else NoOp(),
transforms.ShiftScaleRotate(
shift_limit=args.shift_limit,
scale_limit=args.scale_limit,
rotate_limit=args.rotate_limit,
border_mode=cv2.BORDER_CONSTANT,
value=0,
p=args.shift_scale_rotate_p
) if args.shift_scale_rotate else NoOp(),
transforms.RandomContrast(
limit=args.contrast_limit,
p=args.contrast_p
) if args.contrast else NoOp(),
RandomErase() if args.random_erase else NoOp(),
transforms.CenterCrop(args.crop_size, args.crop_size) if args.center_crop else NoOp(),
ForegroundCenterCrop(args.crop_size) if args.foreground_center_crop else NoOp(),
transforms.RandomCrop(args.crop_size, args.crop_size) if args.random_crop else NoOp(),
transforms.Normalize(mean=model.mean, std=model.std),
ToTensor(),
])
if args.img_type:
stage_1_train_dir = 'processed/stage_1_train_%s' %args.img_type
else:
stage_1_train_dir = 'processed/stage_1_train'
df = pd.read_csv('inputs/stage_1_train.csv')
img_paths = np.array([stage_1_train_dir + '/' + '_'.join(s.split('_')[:-1]) + '.png' for s in df['ID']][::6])
labels = np.array([df.loc[c::6, 'Label'].values for c in range(6)]).T.astype('float32')
df = df[::6]
df['img_path'] = img_paths
for c in range(6):
df['label_%d' %c] = labels[:, c]
df['ID'] = df['ID'].apply(lambda s: '_'.join(s.split('_')[:-1]))
meta_df = pd.read_csv('processed/stage_1_train_meta.csv')
meta_df['ID'] = meta_df['SOPInstanceUID']
test_meta_df = pd.read_csv('processed/stage_1_test_meta.csv')
df = pd.merge(df, meta_df, how='left')
patient_ids = meta_df['PatientID'].unique()
test_patient_ids = test_meta_df['PatientID'].unique()
if args.remove_test_patient_ids:
patient_ids = np.array([s for s in patient_ids if not s in test_patient_ids])
train_img_paths = np.hstack(df[['img_path', 'PatientID']].groupby(['PatientID'])['img_path'].apply(np.array).loc[patient_ids].to_list()).astype('str')
train_labels = []
for c in range(6):
train_labels.append(np.hstack(df[['label_%d' %c, 'PatientID']].groupby(['PatientID'])['label_%d' %c].apply(np.array).loc[patient_ids].to_list()))
train_labels = np.array(train_labels).T
if args.resume:
checkpoint = torch.load('models/%s/checkpoint.pth.tar' % args.name)
# train
train_set = Dataset(
train_img_paths,
train_labels,
transform=train_transform)
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
# pin_memory=True,
)
if args.optimizer == 'Adam':
optimizer = optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, weight_decay=args.weight_decay)
elif args.optimizer == 'AdamW':
optimizer = optim.AdamW(
filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, weight_decay=args.weight_decay)
elif args.optimizer == 'RAdam':
optimizer = RAdam(
filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, weight_decay=args.weight_decay)
elif args.optimizer == 'SGD':
optimizer = optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr,
momentum=args.momentum, weight_decay=args.weight_decay, nesterov=args.nesterov)
else:
raise NotImplementedError
if args.apex:
amp.initialize(model, optimizer, opt_level='O1')
if args.scheduler == 'CosineAnnealingLR':
scheduler = lr_scheduler.CosineAnnealingLR(
optimizer, T_max=args.epochs, eta_min=args.min_lr)
elif args.scheduler == 'MultiStepLR':
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=[int(e) for e in args.milestones.split(',')], gamma=args.gamma)
else:
raise NotImplementedError
log = {
'epoch': [],
'loss': [],
}
start_epoch = 0
if args.resume:
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
start_epoch = checkpoint['epoch']
log = pd.read_csv('models/%s/log.csv' % args.name).to_dict(orient='list')
for epoch in range(start_epoch, args.epochs):
print('Epoch [%d/%d]' % (epoch + 1, args.epochs))
# train for one epoch
train_loss = train(args, train_loader, model, criterion, optimizer, epoch)
if args.scheduler == 'CosineAnnealingLR':
scheduler.step()
print('loss %.4f' % (train_loss))
log['epoch'].append(epoch)
log['loss'].append(train_loss)
pd.DataFrame(log).to_csv('models/%s/log.csv' % args.name, index=False)
torch.save(model.state_dict(), 'models/%s/model.pth' % args.name)
print("=> saved model")
state = {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
}
torch.save(state, 'models/%s/checkpoint.pth.tar' % args.name)
def main():
config = vars(parse_args())
if config['name'] is None:
config['name'] = '%s_%s' % (config['arch'],
datetime.now().strftime('%m%d%H'))
config['num_filters'] = [int(n) for n in config['num_filters'].split(',')]
if not os.path.exists('models/detection/%s' % config['name']):
os.makedirs('models/detection/%s' % config['name'])
if config['resume']:
with open('models/detection/%s/config.yml' % config['name'], 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
config['resume'] = True
with open('models/detection/%s/config.yml' % config['name'], 'w') as f:
yaml.dump(config, f)
print('-' * 20)
for key in config.keys():
print('- %s: %s' % (key, str(config[key])))
print('-' * 20)
cudnn.benchmark = True
df = pd.read_csv('inputs/train.csv')
img_paths = np.array('inputs/train_images/' + df['ImageId'].values +
'.jpg')
mask_paths = np.array('inputs/train_masks/' + df['ImageId'].values +
'.jpg')
labels = np.array(
[convert_str_to_labels(s) for s in df['PredictionString']])
test_img_paths = None
test_mask_paths = None
test_outputs = None
if config['pseudo_label'] is not None:
test_df = pd.read_csv('inputs/sample_submission.csv')
test_img_paths = np.array('inputs/test_images/' +
test_df['ImageId'].values + '.jpg')
test_mask_paths = np.array('inputs/test_masks/' +
test_df['ImageId'].values + '.jpg')
ext = os.path.splitext(config['pseudo_label'])[1]
if ext == '.pth':
test_outputs = torch.load('outputs/raw/test/%s' %
config['pseudo_label'])
elif ext == '.csv':
test_labels = pd.read_csv('outputs/submissions/test/%s' %
config['pseudo_label'])
null_idx = test_labels.isnull().any(axis=1)
test_img_paths = test_img_paths[~null_idx]
test_mask_paths = test_mask_paths[~null_idx]
test_labels = test_labels.dropna()
test_labels = np.array([
convert_str_to_labels(
s, names=['pitch', 'yaw', 'roll', 'x', 'y', 'z', 'score'])
for s in test_labels['PredictionString']
])
print(test_labels)
else:
raise NotImplementedError
if config['resume']:
checkpoint = torch.load('models/detection/%s/checkpoint.pth.tar' %
config['name'])
heads = OrderedDict([
('hm', 1),
('reg', 2),
('depth', 1),
])
if config['rot'] == 'eular':
heads['eular'] = 3
elif config['rot'] == 'trig':
heads['trig'] = 6
elif config['rot'] == 'quat':
heads['quat'] = 4
else:
raise NotImplementedError
if config['wh']:
heads['wh'] = 2
criterion = OrderedDict()
for head in heads.keys():
criterion[head] = losses.__dict__[config[head + '_loss']]().cuda()
train_transform = Compose([
transforms.ShiftScaleRotate(shift_limit=config['shift_limit'],
scale_limit=0,
rotate_limit=0,
border_mode=cv2.BORDER_CONSTANT,
value=0,
p=config['shift_p'])
if config['shift'] else NoOp(),
OneOf([
transforms.HueSaturationValue(hue_shift_limit=config['hue_limit'],
sat_shift_limit=config['sat_limit'],
val_shift_limit=config['val_limit'],
p=config['hsv_p'])
if config['hsv'] else NoOp(),
transforms.RandomBrightness(
limit=config['brightness_limit'],
p=config['brightness_p'],
) if config['brightness'] else NoOp(),
transforms.RandomContrast(
limit=config['contrast_limit'],
p=config['contrast_p'],
) if config['contrast'] else NoOp(),
],
p=1),
transforms.ISONoise(p=config['iso_noise_p'], )
if config['iso_noise'] else NoOp(),
transforms.CLAHE(p=config['clahe_p'], ) if config['clahe'] else NoOp(),
],
keypoint_params=KeypointParams(
format='xy', remove_invisible=False))
val_transform = None
folds = []
best_losses = []
# best_scores = []
kf = KFold(n_splits=config['n_splits'], shuffle=True, random_state=41)
for fold, (train_idx, val_idx) in enumerate(kf.split(img_paths)):
print('Fold [%d/%d]' % (fold + 1, config['n_splits']))
if (config['resume'] and fold < checkpoint['fold'] - 1) or (
not config['resume']
and os.path.exists('models/%s/model_%d.pth' %
(config['name'], fold + 1))):
log = pd.read_csv('models/detection/%s/log_%d.csv' %
(config['name'], fold + 1))
best_loss = log.loc[log['val_loss'].values.argmin(), 'val_loss']
# best_loss, best_score = log.loc[log['val_loss'].values.argmin(), ['val_loss', 'val_score']].values
folds.append(str(fold + 1))
best_losses.append(best_loss)
# best_scores.append(best_score)
continue
train_img_paths, val_img_paths = img_paths[train_idx], img_paths[
val_idx]
train_mask_paths, val_mask_paths = mask_paths[train_idx], mask_paths[
val_idx]
train_labels, val_labels = labels[train_idx], labels[val_idx]
if config['pseudo_label'] is not None:
train_img_paths = np.hstack((train_img_paths, test_img_paths))
train_mask_paths = np.hstack((train_mask_paths, test_mask_paths))
train_labels = np.hstack((train_labels, test_labels))
# train
train_set = Dataset(
train_img_paths,
train_mask_paths,
train_labels,
input_w=config['input_w'],
input_h=config['input_h'],
transform=train_transform,
lhalf=config['lhalf'],
hflip=config['hflip_p'] if config['hflip'] else 0,
scale=config['scale_p'] if config['scale'] else 0,
scale_limit=config['scale_limit'],
# test_img_paths=test_img_paths,
# test_mask_paths=test_mask_paths,
# test_outputs=test_outputs,
)
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['num_workers'],
# pin_memory=True,
)
val_set = Dataset(val_img_paths,
val_mask_paths,
val_labels,
input_w=config['input_w'],
input_h=config['input_h'],
transform=val_transform,
lhalf=config['lhalf'])
val_loader = torch.utils.data.DataLoader(
val_set,
batch_size=config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
# pin_memory=True,
)
# create model
model = get_model(config['arch'],
heads=heads,
head_conv=config['head_conv'],
num_filters=config['num_filters'],
dcn=config['dcn'],
gn=config['gn'],
ws=config['ws'],
freeze_bn=config['freeze_bn'])
model = model.cuda()
if config['load_model'] is not None:
model.load_state_dict(
torch.load('models/detection/%s/model_%d.pth' %
(config['load_model'], fold + 1)))
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'] == 'AdamW':
optimizer = optim.AdamW(params,
lr=config['lr'],
weight_decay=config['weight_decay'])
elif config['optimizer'] == 'RAdam':
optimizer = RAdam(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['apex']:
amp.initialize(model, optimizer, opt_level='O1')
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'])
else:
raise NotImplementedError
log = {
'epoch': [],
'loss': [],
# 'score': [],
'val_loss': [],
# 'val_score': [],
}
best_loss = float('inf')
# best_score = float('inf')
start_epoch = 0
if config['resume'] and fold == checkpoint['fold'] - 1:
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
start_epoch = checkpoint['epoch']
log = pd.read_csv(
'models/detection/%s/log_%d.csv' %
(config['name'], fold + 1)).to_dict(orient='list')
best_loss = checkpoint['best_loss']
for epoch in range(start_epoch, config['epochs']):
print('Epoch [%d/%d]' % (epoch + 1, config['epochs']))
# train for one epoch
train_loss = train(config, heads, train_loader, model, criterion,
optimizer, epoch)
# evaluate on validation set
val_loss = validate(config, heads, val_loader, model, criterion)
if config['scheduler'] == 'CosineAnnealingLR':
scheduler.step()
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler.step(val_loss)
print('loss %.4f - val_loss %.4f' % (train_loss, val_loss))
# print('loss %.4f - score %.4f - val_loss %.4f - val_score %.4f'
# % (train_loss, train_score, val_loss, val_score))
log['epoch'].append(epoch)
log['loss'].append(train_loss)
# log['score'].append(train_score)
log['val_loss'].append(val_loss)
# log['val_score'].append(val_score)
pd.DataFrame(log).to_csv('models/detection/%s/log_%d.csv' %
(config['name'], fold + 1),
index=False)
if val_loss < best_loss:
torch.save(
model.state_dict(), 'models/detection/%s/model_%d.pth' %
(config['name'], fold + 1))
best_loss = val_loss
# best_score = val_score
print("=> saved best model")
state = {
'fold': fold + 1,
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
}
torch.save(
state,
'models/detection/%s/checkpoint.pth.tar' % config['name'])
print('val_loss: %f' % best_loss)
# print('val_score: %f' % best_score)
folds.append(str(fold + 1))
best_losses.append(best_loss)
# best_scores.append(best_score)
results = pd.DataFrame({
'fold':
folds + ['mean'],
'best_loss':
best_losses + [np.mean(best_losses)],
# 'best_score': best_scores + [np.mean(best_scores)],
})
print(results)
results.to_csv('models/detection/%s/results.csv' % config['name'],
index=False)
del model
torch.cuda.empty_cache()
del train_set, train_loader
del val_set, val_loader
gc.collect()
if not config['cv']:
break
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 main():
config = vars(parse_args())
if config['name'] is None:
config['name'] = '%s_%s' % (config['arch'], datetime.now().strftime('%m%d%H'))
if not os.path.exists('models/pose/%s' % config['name']):
os.makedirs('models/pose/%s' % config['name'])
if config['resume']:
with open('models/pose/%s/config.yml' % config['name'], 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
config['resume'] = True
with open('models/pose/%s/config.yml' % config['name'], 'w') as f:
yaml.dump(config, f)
print('-'*20)
for key in config.keys():
print('- %s: %s' % (key, str(config[key])))
print('-'*20)
cudnn.benchmark = True
df = pd.read_csv('inputs/train.csv')
img_ids = df['ImageId'].values
pose_df = pd.read_csv('processed/pose_train.csv')
pose_df['img_path'] = 'processed/pose_images/train/' + pose_df['img_path']
if config['resume']:
checkpoint = torch.load('models/pose/%s/checkpoint.pth.tar' % config['name'])
if config['rot'] == 'eular':
num_outputs = 3
elif config['rot'] == 'trig':
num_outputs = 6
elif config['rot'] == 'quat':
num_outputs = 4
else:
raise NotImplementedError
if config['loss'] == 'L1Loss':
criterion = nn.L1Loss().cuda()
elif config['loss'] == 'MSELoss':
criterion = nn.MSELoss().cuda()
else:
raise NotImplementedError
train_transform = Compose([
transforms.ShiftScaleRotate(
shift_limit=config['shift_limit'],
scale_limit=0,
rotate_limit=0,
border_mode=cv2.BORDER_CONSTANT,
value=0,
p=config['shift_p']
) if config['shift'] else NoOp(),
OneOf([
transforms.HueSaturationValue(
hue_shift_limit=config['hue_limit'],
sat_shift_limit=config['sat_limit'],
val_shift_limit=config['val_limit'],
p=config['hsv_p']
) if config['hsv'] else NoOp(),
transforms.RandomBrightness(
limit=config['brightness_limit'],
p=config['brightness_p'],
) if config['brightness'] else NoOp(),
transforms.RandomContrast(
limit=config['contrast_limit'],
p=config['contrast_p'],
) if config['contrast'] else NoOp(),
], p=1),
transforms.ISONoise(
p=config['iso_noise_p'],
) if config['iso_noise'] else NoOp(),
transforms.CLAHE(
p=config['clahe_p'],
) if config['clahe'] else NoOp(),
transforms.Resize(config['input_w'], config['input_h']),
transforms.Normalize(),
ToTensor(),
])
val_transform = Compose([
transforms.Resize(config['input_w'], config['input_h']),
transforms.Normalize(),
ToTensor(),
])
folds = []
best_losses = []
kf = KFold(n_splits=config['n_splits'], shuffle=True, random_state=41)
for fold, (train_idx, val_idx) in enumerate(kf.split(img_ids)):
print('Fold [%d/%d]' %(fold + 1, config['n_splits']))
if (config['resume'] and fold < checkpoint['fold'] - 1) or (not config['resume'] and os.path.exists('pose_models/%s/model_%d.pth' % (config['name'], fold+1))):
log = pd.read_csv('models/pose/%s/log_%d.csv' %(config['name'], fold+1))
best_loss = log.loc[log['val_loss'].values.argmin(), 'val_loss']
# best_loss, best_score = log.loc[log['val_loss'].values.argmin(), ['val_loss', 'val_score']].values
folds.append(str(fold + 1))
best_losses.append(best_loss)
# best_scores.append(best_score)
continue
train_img_ids, val_img_ids = img_ids[train_idx], img_ids[val_idx]
train_img_paths = []
train_labels = []
for img_id in train_img_ids:
tmp = pose_df.loc[pose_df.ImageId == img_id]
img_path = tmp['img_path'].values
train_img_paths.append(img_path)
yaw = tmp['yaw'].values
pitch = tmp['pitch'].values
roll = tmp['roll'].values
roll = rotate(roll, np.pi)
if config['rot'] == 'eular':
label = np.array([
yaw,
pitch,
roll
]).T
elif config['rot'] == 'trig':
label = np.array([
np.cos(yaw),
np.sin(yaw),
np.cos(pitch),
np.sin(pitch),
np.cos(roll),
np.sin(roll),
]).T
elif config['rot'] == 'quat':
raise NotImplementedError
else:
raise NotImplementedError
train_labels.append(label)
train_img_paths = np.hstack(train_img_paths)
train_labels = np.vstack(train_labels)
val_img_paths = []
val_labels = []
for img_id in val_img_ids:
tmp = pose_df.loc[pose_df.ImageId == img_id]
img_path = tmp['img_path'].values
val_img_paths.append(img_path)
yaw = tmp['yaw'].values
pitch = tmp['pitch'].values
roll = tmp['roll'].values
roll = rotate(roll, np.pi)
if config['rot'] == 'eular':
label = np.array([
yaw,
pitch,
roll
]).T
elif config['rot'] == 'trig':
label = np.array([
np.cos(yaw),
np.sin(yaw),
np.cos(pitch),
np.sin(pitch),
np.cos(roll),
np.sin(roll),
]).T
elif config['rot'] == 'quat':
raise NotImplementedError
else:
raise NotImplementedError
val_labels.append(label)
val_img_paths = np.hstack(val_img_paths)
val_labels = np.vstack(val_labels)
# train
train_set = PoseDataset(
train_img_paths,
train_labels,
transform=train_transform,
)
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['num_workers'],
# pin_memory=True,
)
val_set = PoseDataset(
val_img_paths,
val_labels,
transform=val_transform,
)
val_loader = torch.utils.data.DataLoader(
val_set,
batch_size=config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
# pin_memory=True,
)
# create model
model = get_pose_model(config['arch'],
num_outputs=num_outputs,
freeze_bn=config['freeze_bn'])
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'] == 'AdamW':
optimizer = optim.AdamW(params, lr=config['lr'], weight_decay=config['weight_decay'])
elif config['optimizer'] == 'RAdam':
optimizer = RAdam(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'])
else:
raise NotImplementedError
log = {
'epoch': [],
'loss': [],
# 'score': [],
'val_loss': [],
# 'val_score': [],
}
best_loss = float('inf')
# best_score = float('inf')
start_epoch = 0
if config['resume'] and fold == checkpoint['fold'] - 1:
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
start_epoch = checkpoint['epoch']
log = pd.read_csv('models/pose/%s/log_%d.csv' % (config['name'], fold+1)).to_dict(orient='list')
best_loss = checkpoint['best_loss']
for epoch in range(start_epoch, config['epochs']):
print('Epoch [%d/%d]' % (epoch + 1, config['epochs']))
# train for one epoch
train_loss = train(config, train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
val_loss = validate(config, val_loader, model, criterion)
if config['scheduler'] == 'CosineAnnealingLR':
scheduler.step()
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler.step(val_loss)
print('loss %.4f - val_loss %.4f' % (train_loss, val_loss))
# print('loss %.4f - score %.4f - val_loss %.4f - val_score %.4f'
# % (train_loss, train_score, val_loss, val_score))
log['epoch'].append(epoch)
log['loss'].append(train_loss)
# log['score'].append(train_score)
log['val_loss'].append(val_loss)
# log['val_score'].append(val_score)
pd.DataFrame(log).to_csv('models/pose/%s/log_%d.csv' % (config['name'], fold+1), index=False)
if val_loss < best_loss:
torch.save(model.state_dict(), 'models/pose/%s/model_%d.pth' % (config['name'], fold+1))
best_loss = val_loss
# best_score = val_score
print("=> saved best model")
state = {
'fold': fold + 1,
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
}
torch.save(state, 'models/pose/%s/checkpoint.pth.tar' % config['name'])
print('val_loss: %f' % best_loss)
# print('val_score: %f' % best_score)
folds.append(str(fold + 1))
best_losses.append(best_loss)
# best_scores.append(best_score)
results = pd.DataFrame({
'fold': folds + ['mean'],
'best_loss': best_losses + [np.mean(best_losses)],
# 'best_score': best_scores + [np.mean(best_scores)],
})
print(results)
results.to_csv('models/pose/%s/results.csv' % config['name'], index=False)
del model
torch.cuda.empty_cache()
del train_set, train_loader
del val_set, val_loader
gc.collect()
if not config['cv']:
break
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():
op = np.random.choice(augmentations)
print(op())
transforms.RandomContrast()
