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 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)
if args.new_training:
model, optimizer, training_generator, val_generator, class_weight, Last_epoch = initialize_from_saved_model(args)
else:
model, optimizer, training_generator, val_generator, class_weight = initialize(args)
Last_epoch = 0
#print(model)
best_pred_loss = 0#lo cambie por balanced accuracy
scheduler = ReduceLROnPlateau(optimizer, factor=0.5, patience=3, min_lr=1e-5, verbose=True)
print('Checkpoint folder ', args.save)
# writer = SummaryWriter(log_dir='../runs/' + args.model, comment=args.model)
for epoch in range(1, args.nEpochs + 1):
train(args, model, training_generator, optimizer, Last_epoch+epoch, class_weight)
val_metrics, confusion_matrix = validation(args, model, val_generator, Last_epoch+epoch, class_weight)
BACC = BalancedAccuray(confusion_matrix.numpy())
val_metrics.replace({'bacc': BACC})
best_pred_loss = util.save_model(model, optimizer, args, val_metrics, Last_epoch+epoch, best_pred_loss, confusion_matrix)
print(confusion_matrix)
scheduler.step(val_metrics.avg_loss())
def objective(trial):
info = nvmlDeviceGetMemoryInfo(handle)
print("Total memory:", info.total)
print("Free memory:", info.free)
print("Used memory:", info.used)
model, training_generator, val_generator, test_generator = initialize(ARGS)
optim_name = trial.suggest_categorical("optimizer",
["Adam", "RMSprop", "SGD"])
weight_decay = trial.suggest_float("weight_decay", 1e-5, 1e-1, log=True)
lr = trial.suggest_float("learning_rate", 1e-7, 1e-5, log=True)
trial.set_user_attr("worker_id", WORKER_ID)
optimizer = util.select_optimizer(optim_name, model, lr, weight_decay)
scheduler = ReduceLROnPlateau(optimizer,
factor=0.5,
patience=2,
min_lr=1e-5,
verbose=True)
best_pred_loss = 1000.0
for epoch in range(1, EPOCHS + 1):
train(ARGS, model, training_generator, optimizer, epoch)
val_metrics, confusion_matrix = validation(ARGS, model, val_generator,
epoch)
scheduler.step(val_metrics._data.average.loss)
return val_metrics._data.average.recall_mean