def get_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=4, help='batch size for training')
parser.add_argument('--log_interval', type=int, default=1000, help='steps to print metrics and loss')
parser.add_argument('--dataset_name', type=str, default="COVID_CT", help='dataset name COVIDx or COVID_CT')
parser.add_argument('--nEpochs', type=int, default=250, help='total number of epochs')
parser.add_argument('--device', type=int, default=0, help='gpu device')
parser.add_argument('--seed', type=int, default=123, help='select seed number for reproducibility')
parser.add_argument('--classes', type=int, default=3, help='dataset classes')
parser.add_argument('--lr', default=2e-5, type=float,
help='learning rate (default: 1e-3)')
parser.add_argument('--weight_decay', default=1e-7, type=float,
help='weight decay (default: 1e-6)')
parser.add_argument('--cuda', action='store_true', default=False, help='use gpu for speed-up')
parser.add_argument('--tensorboard', action='store_true', default=True,
help='use tensorboard for loggging and visualization')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--model', type=str, default='COVIDNet_large',
choices=('COVIDNet_small', 'resnet18', 'mobilenet_v2', 'densenet169', 'COVIDNet_large'))
parser.add_argument('--opt', type=str, default='adam',
choices=('sgd', 'adam', 'rmsprop'))
parser.add_argument('--root_path', type=str, default='./data',
help='path to dataset ')
parser.add_argument('--save', type=str, default='/saved/COVIDNet' + util.datestr(),
help='path to checkpoint save directory ')
args = parser.parse_args()
return args
def main():
args = get_arguments()
SEED = args.seed
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(SEED)
if (args.cuda):
torch.cuda.manual_seed(SEED)
model, optimizer, training_generator, val_generator, test_generator = initialize(
args)
print(model)
best_pred_loss = 1000.0
scheduler = ReduceLROnPlateau(optimizer,
factor=0.5,
patience=2,
min_lr=1e-5,
verbose=True)
print('Checkpoint folder ', args.save)
if args.tensorboard:
writer = SummaryWriter('./runs/' + util.datestr())
else:
writer = None
for epoch in range(1, args.nEpochs + 1):
train(args, model, training_generator, optimizer, epoch, writer)
val_metrics, confusion_matrix = validation(args, model, val_generator,
epoch, writer)
best_pred_loss = util.save_model(model, optimizer, args, val_metrics,
epoch, best_pred_loss,
confusion_matrix)
scheduler.step(val_metrics.avg_loss())
def get_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size',
type=int,
default=12,
help='batch size for training')
parser.add_argument('--log_interval',
type=int,
default=200,
help='steps to print metrics and loss')
parser.add_argument('--cuda', type=int, default=0, help='use gpu support')
parser.add_argument('--device', type=int, default=0, help='gpu device')
parser.add_argument('--dataset_name',
type=str,
default='COVIDx',
help='dataset name COVIDx')
parser.add_argument('--seed',
type=int,
default=123,
help='select seed number for reproducibility')
parser.add_argument('--classes',
type=int,
default=3,
help='dataset classes')
parser.add_argument('--resume',
default='',
type=str,
metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--model',
type=str,
default='COVIDNet_small',
choices=('COVIDNet_small', 'resnet18',
'COVIDNet_large'))
parser.add_argument('--root_path',
type=str,
default='./data',
help='path to dataset ')
parser.add_argument('--save',
type=str,
default='./saved/COVIDNet' + util.datestr(),
help='path to checkpoint save directory ')
parser.add_argument('--epochs',
type=int,
default=1,
help="number of training epochs")
parser.add_argument('--trials',
type=int,
default=10,
help="number of HPO trials")
parser.add_argument('--worker_id', type=int, default=0, help="worker id")
parser.add_argument('--ex_rate',
type=int,
default=100,
help="info exchange rate in HPO")
args = parser.parse_args()
return args
def get_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('--new_training', action='store_true', default=False,
help='load saved_model as initial model')
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--log_interval', type=int, default=1000)
parser.add_argument('--dataset_name', type=str, default="COVIDx")
parser.add_argument('--nEpochs', type=int, default=20)
parser.add_argument('--device', type=str, default='0')
parser.add_argument('--seed', type=int, default=123)
parser.add_argument('--classes', type=int, default=3)
parser.add_argument('--inChannels', type=int, default=1)
parser.add_argument('--lr', default=2e-5, type=float,
help='learning rate (default: 1e-3)')
parser.add_argument('--weight_decay', default=1e-7, type=float,
help='weight decay (default: 1e-6)')
parser.add_argument('--cuda', action='store_true', default=True)
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--model', type=str, default='CovidNet_Grad_CAM',
choices=('COVIDNet','CovidNet_ResNet50', 'CovidNet_DenseNet', 'CovidNet_Grad_CAM','CovidNet_DE'))
parser.add_argument('--opt', type=str, default='adam',
choices=('sgd', 'adam', 'rmsprop'))
parser.add_argument('--dataset', type=str, default='/content/covid-chestxray-dataset/data',
help='path to dataset ')
parser.add_argument('--saved_model', type=str, default='/home/julian/Documents/PythonExperiments/COVIDNet/ModelSavedCoviNet/COVIDNet20200406_0412/COVIDNet_best_checkpoint.pth.tar',
help='path to save_model ')
parser.add_argument('--save', type=str, default='/home/julian/Documents/PythonExperiments/COVIDNet/ModelSavedCoviNet/COVIDNet' + util.datestr(),
help='path to checkpoint ')
args = parser.parse_args()
return args
def main():
args = get_arguments()
myargs = [] # getopts(sys.argv)
now = datetime.datetime.now()
cwd = os.getcwd()
if len(myargs) > 0:
if 'c' in myargs:
config_file = myargs['c']
else:
config_file = 'config/trainer_config.yml'
config = OmegaConf.load(os.path.join(cwd, config_file))['trainer']
config.cwd = str(cwd)
reproducibility(config)
dt_string = now.strftime("%d_%m_%Y_%H.%M.%S")
cpkt_fol_name = os.path.join(
config.cwd,
f'checkpoints/model_{config.model.name}/dataset_{config.dataset.name}/date_{dt_string}'
)
log = Logger(path=cpkt_fol_name, name='LOG').get_logger()
best_pred_loss = 1000.0
log.info(f"Checkpoint folder {cpkt_fol_name}")
log.info(f"date and time = {dt_string}")
log.info(f'pyTorch VERSION:{torch.__version__}', )
log.info(f'CUDA VERSION')
log.info(f'CUDNN VERSION:{torch.backends.cudnn.version()}')
log.info(f'Number CUDA Devices: {torch.cuda.device_count()}')
if args.tensorboard:
writer_path = os.path.join(cpkt_fol_name + 'runs/')
writer = SummaryWriter(writer_path + util.datestr())
else:
writer = None
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
log.info(f'device: {device}')
training_generator, val_generator, test_generator, class_dict = select_dataset(
config)
n_classes = len(class_dict)
model = select_model(config, n_classes)
log.info(f"{model}")
if (config.load):
pth_file, _ = load_checkpoint(config.pretrained_cpkt,
model,
strict=True,
load_seperate_layers=False)
else:
pth_file = None
if (config.cuda and use_cuda):
if torch.cuda.device_count() > 1:
log.info(f"Let's use {torch.cuda.device_count()} GPUs!")
model = torch.nn.DataParallel(model)
model.to(device)
optimizer, scheduler = select_optimizer(model, config['model'], None)
log.info(f'{model}')
log.info(f"Checkpoint Folder {cpkt_fol_name} ")
shutil.copy(os.path.join(config.cwd, config_file), cpkt_fol_name)
trainer = Trainer(config,
model=model,
optimizer=optimizer,
data_loader=training_generator,
writer=writer,
logger=log,
valid_data_loader=val_generator,
test_data_loader=test_generator,
class_dict=class_dict,
lr_scheduler=scheduler,
checkpoint_dir=cpkt_fol_name)
trainer.train()
