def test_random_resized_crop_size():
image = np.ones((100, 100, 3))
keypoints = [(0.2, 0.3, 0.6, 0.8), (0.3, 0.4, 0.7, 0.9, 99)]
aug = RandomResizedCrop(height=50, width=50, p=1.0)
transformed = aug(image=image, keypoints=keypoints)
assert transformed["image"].shape == (50, 50, 3)
assert len(keypoints) == len(transformed["keypoints"])
def test_random_resized_crop_size():
image = np.ones((100, 100, 3))
bboxes = [[0.2, 0.3, 0.6, 0.8], [0.3, 0.4, 0.7, 0.9, 99]]
aug = RandomResizedCrop(height=50, width=50, p=1.)
transformed = aug(image=image, bboxes=bboxes)
assert transformed['image'].shape == (50, 50, 3)
assert len(bboxes) == len(transformed['bboxes'])
def aug_preprocess(resize):
return Compose([
RandomResizedCrop(height=resize,
width=resize,
scale=(0.8, 1.0),
ratio=(4 / 5, 5 / 4)),
RandomBrightnessContrast(0.1, 0.1),
HorizontalFlip(p=0.5),
Lambda(image=preprocess_input)
],
p=1) # always apply augmentation
def transform(self, img: t.Any) -> t.Any:
max_hw = max(img.shape[0:2])
img = PadIfNeeded(max_hw, max_hw, border_mode=cv2.BORDER_REPLICATE)(image=img)[
"image"
]
img = Resize(self.resolution, self.resolution)(image=img)["image"]
img = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225],)(
image=img
)["image"]
if self.mode == "Train":
img = RandomResizedCrop(self.resolution, self.resolution)(image=img)[
"image"
]
img = Cutout(p=0.2)(image=img)["image"]
img = HorizontalFlip(p=0.5)(image=img)["image"]
img = ShiftScaleRotate(
shift_limit=0.1, scale_limit=0.0, rotate_limit=10, p=0.3
)(image=img)["image"]
img = ToTensorV2()(image=img)["image"]
return img
def train_dataloader(self):
augmentations = Compose([
RandomResizedCrop(
height=self.hparams.sz,
width=self.hparams.sz,
scale=(0.7, 1.0),
),
# ToGray(),
GridDistortion(),
RandomBrightnessContrast(),
ShiftScaleRotate(),
Flip(p=0.5),
CoarseDropout(
max_height=int(self.hparams.sz / 10),
max_width=int(self.hparams.sz / 10),
),
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=False,
)
def main():
global args, config, best_loss
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
for k, v in config['common'].items():
setattr(args, k, v)
config = EasyDict(config['common'])
rank, world_size, device_id = dist_init(
os.path.join(args.distributed_path, config.distributed_file))
args.save_path_dated = args.save_path + '/' + args.datetime
if args.run_tag != '':
args.save_path_dated += '-' + args.run_tag
# create model
model = model_entry(config.model)
model.cuda()
model = nn.parallel.DistributedDataParallel(model, device_ids=[device_id])
# create optimizer
opt_config = config.optimizer
opt_config.kwargs.lr = config.lr_scheduler.base_lr
opt_config.kwargs.params = model.parameters()
optimizer = optim_entry(opt_config)
# optionally resume from a checkpoint
last_iter = -1
best_loss = 1e9
if args.load_path:
if args.recover:
best_loss, last_iter = load_state(args.load_path,
model,
optimizer=optimizer)
else:
load_state(args.load_path, model)
cudnn.benchmark = True
# train augmentation
if config.augmentation.get('imgnet_mean', False):
model_mean = (0.485, 0.456, 0.406)
model_std = (0.229, 0.224, 0.225)
else:
model_mean = (0.5, 0.5, 0.5)
model_std = (0.5, 0.5, 0.5)
trans = albumentations.Compose([
RandomResizedCrop(config.augmentation.input_size,
config.augmentation.input_size,
scale=(config.augmentation.min_scale**2., 1.),
ratio=(1., 1.)),
HorizontalFlip(p=0.5),
RandomBrightnessContrast(brightness_limit=0.25,
contrast_limit=0.1,
p=0.5),
JpegCompression(p=.2, quality_lower=50),
MotionBlur(p=0.5),
Normalize(mean=model_mean, std=model_std),
ToTensorV2()
])
train_dataset = FaceDataset(config.train_root,
config.train_source,
transform=trans,
resize=config.augmentation.input_size,
image_format=config.get('image_format', None),
random_frame=config.get(
'train_random_frame', False),
bgr=config.augmentation.get('bgr', False))
train_sampler = DistributedGivenIterationSampler(
train_dataset,
config.lr_scheduler.max_iter,
config.batch_size,
last_iter=last_iter)
train_loader = DataLoader(train_dataset,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.workers,
pin_memory=True,
sampler=train_sampler)
# validation augmentation
trans = albumentations.Compose([
Resize(config.augmentation.input_size, config.augmentation.input_size),
Normalize(mean=model_mean, std=model_std),
ToTensorV2()
])
val_multi_loader = []
if args.val_source != '':
for dataset_idx in range(len(args.val_source)):
val_dataset = FaceDataset(
args.val_root[dataset_idx],
args.val_source[dataset_idx],
transform=trans,
output_index=True,
resize=config.augmentation.input_size,
image_format=config.get('image_format', None),
bgr=config.augmentation.get('bgr', False))
val_sampler = DistributedSampler(val_dataset, round_up=False)
val_loader = DataLoader(val_dataset,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.workers,
pin_memory=True,
sampler=val_sampler)
val_multi_loader.append(val_loader)
config.lr_scheduler['optimizer'] = optimizer
config.lr_scheduler['last_iter'] = last_iter
lr_scheduler = get_scheduler(config.lr_scheduler)
if rank == 0:
mkdir(args.save_path)
mkdir(args.save_path_dated)
tb_logger = SummaryWriter(args.save_path_dated)
logger = create_logger('global_logger',
args.save_path_dated + '-log.txt')
logger.info('{}'.format(args))
logger.info(model)
logger.info(parameters_string(model))
logger.info('len(train dataset) = %d' % len(train_loader.dataset))
for dataset_idx in range(len(val_multi_loader)):
logger.info(
'len(val%d dataset) = %d' %
(dataset_idx, len(val_multi_loader[dataset_idx].dataset)))
mkdir(args.save_path_dated + '/saves')
else:
tb_logger = None
positive_weight = config.get('positive_weight', 0.5)
weight = torch.tensor([1. - positive_weight, positive_weight]) * 2.
if rank == 0:
logger.info('using class weights: {}'.format(weight.tolist()))
criterion = nn.CrossEntropyLoss(weight=weight).cuda()
if args.evaluate:
if args.evaluate_path:
all_ckpt = get_all_checkpoint(args.evaluate_path, args.range_list,
rank)
for ckpt in all_ckpt:
if rank == 0:
logger.info('Testing ckpt: ' + ckpt)
last_iter = -1
_, last_iter = load_state(ckpt, model, optimizer=optimizer)
for dataset_idx in range(len(val_multi_loader)):
validate(dataset_idx,
val_multi_loader[dataset_idx],
model,
criterion,
tb_logger,
curr_step=last_iter,
save_softmax=True)
else:
for dataset_idx in range(len(val_multi_loader)):
validate(dataset_idx,
val_multi_loader[dataset_idx],
model,
criterion,
tb_logger,
curr_step=last_iter,
save_softmax=True)
return
train(train_loader, val_multi_loader, model, criterion, optimizer,
lr_scheduler, last_iter + 1, tb_logger)
return