p.requires_grad = False
model = DataParallel(model).cuda() if NUM_GPUS > 1 else model.cuda()
optimizer = Optimizer.from_params(
[x for x in model.named_parameters() if x[1].requires_grad],
params['trainer']['optimizer'])
lr_scheduler_params = params['trainer'].pop("learning_rate_scheduler", None)
scheduler = LearningRateScheduler.from_params(
optimizer, lr_scheduler_params) if lr_scheduler_params else None
if os.path.exists(args.folder):
print("Found folder! restoring", flush=True)
start_epoch, val_metric_per_epoch = restore_checkpoint(
model,
optimizer,
serialization_dir=args.folder,
learning_rate_scheduler=scheduler)
else:
print("Making directories")
os.makedirs(args.folder, exist_ok=True)
start_epoch, val_metric_per_epoch = 0, []
shutil.copy2(args.params, args.folder)
param_shapes = print_para(model)
num_batches = 0
for epoch_num in range(start_epoch,
params['trainer']['num_epochs'] + start_epoch):
train_results = []
norms = []
model.train()
def restore_checkpoint(self, serialization_dir, epoch_to_load):
# Restore from a training dir
return restore_checkpoint(self.model, self.optimizer,
serialization_dir, epoch_to_load)