print(network_repr)
writer.add_text('general', network_repr)
if args.model == 'unconstrained':
for module in network.modules():
if hasattr(module, 'relax'):
module.relax()
print(f'relaxing {repr(module)}')
network_repr = repr(network)
print(network_repr)
writer.add_text('general', network_repr)
n_params = 0
for param in network.parameters():
n_params += param.numel()
print(n_params, 'learnable parameters')
if cuda:
network = network.cuda()
checkpoint = framework.load_checkpoint(args.model_path)
start_epoch, best_score, model_dict, optim_dict = checkpoint
network.load_state_dict(model_dict)
framework.inspect(network,
val_loader,
args.artifacts,
early_stop=args.early_stop)
loss_fn.name = 'BCE'
optim = torch.optim.Adam([
{
'params': network.l2_params(),
'weight_decay': args.l2
},
{
'params': network.nr_params(),
'weight_decay': 0.
},
],
lr=args.lr)
if args.load:
checkpoint = framework.load_checkpoint(args.load)
start_epoch, best_score, model_dict, optim_dict = checkpoint
network.load_state_dict(model_dict)
optim.load_state_dict(optim_dict)
fmt = 'Starting at epoch {}, best score {}. Loaded from {}'
start_epoch += 1 # skip to the next after loaded
msg = fmt.format(start_epoch, best_score, args.load)
else:
print('Set start epoch and best score to 0')
best_score = 0.
start_epoch = 0
if args.model == 'unconstrained':
for module in network.modules():
if hasattr(module, 'relax'):