def get_all_trained_models_info(models_path, use_profiler=False, device='gpu'):
print('Testing all models in: {}'.format(models_path))
for model_name in sorted(os.listdir(models_path)):
try:
model_params = arcs.load_params(models_path, model_name, -1)
train_time = model_params['total_time']
num_epochs = model_params['epochs']
architecture = model_params['architecture']
print(model_name)
task = model_params['task']
print(task)
net_type = model_params['network_type']
print(net_type)
top1_test = model_params['test_top1_acc']
top1_train = model_params['train_top1_acc']
top5_test = model_params['test_top5_acc']
top5_train = model_params['train_top5_acc']
print('Top1 Test accuracy: {}'.format(top1_test[-1]))
print('Top5 Test accuracy: {}'.format(top5_test[-1]))
print('\nTop1 Train accuracy: {}'.format(top1_train[-1]))
print('Top5 Train accuracy: {}'.format(top5_train[-1]))
print('Training time: {}, in {} epochs'.format(
train_time, num_epochs))
if use_profiler:
model, _ = arcs.load_model(models_path, model_name, epoch=-1)
model.to(device)
input_size = model_params['input_size']
if architecture == 'dsn':
total_ops, total_params = profile_dsn(
model, input_size, device)
print("#Ops (GOps): {}".format(total_ops))
print("#Params (mil): {}".format(total_params))
else:
total_ops, total_params = profile(model, input_size,
device)
print("#Ops: %f GOps" % (total_ops / 1e9))
print("#Parameters: %f M" % (total_params / 1e6))
print('------------------------')
except:
print('FAIL: {}'.format(model_name))
continue
def train_sdns(models_path, networks, ic_only=False, device='cpu'):
if ic_only: # if we only train the ICs, we load a pre-trained CNN
load_epoch = -1
else: # if we train both ICs and the orig network, we load an untrained CNN
load_epoch = 0
for sdn_name in networks:
cnn_to_tune = sdn_name.replace('sdn', 'cnn')
sdn_params = arcs.load_params(models_path, sdn_name)
sdn_params = arcs.get_net_params(sdn_params['network_type'],
sdn_params['task'])
sdn_model, _ = af.cnn_to_sdn(
models_path, cnn_to_tune, sdn_params,
load_epoch) # load the CNN and convert it to a SDN
arcs.save_model(sdn_model, sdn_params, models_path, sdn_name,
epoch=0) # save the resulting SDN
train(models_path, networks, sdn=True, ic_only_sdn=ic_only, device=device)