config = imp.load_source("", exp_config_file).config
config[
'exp_dir'] = exp_directory # the place where logs, models, and other stuff will be stored
print("Loading experiment %s from file: %s" % (args_opt.exp, exp_config_file))
print("Generated logs, snapshots, and model files will be stored on %s" %
(config['exp_dir']))
# Set train and test datasets and the corresponding data loaders
data_train_opt = config['data_train_opt']
data_test_opt = config['data_test_opt']
num_imgs_per_cat = data_train_opt['num_imgs_per_cat'] if (
'num_imgs_per_cat' in data_train_opt) else None
dataset_train = GenericDataset(
dataset_name=data_train_opt['dataset_name'],
split=data_train_opt['split'],
random_sized_crop=data_train_opt['random_sized_crop'],
num_imgs_per_cat=num_imgs_per_cat)
dataset_test = GenericDataset(
dataset_name=data_test_opt['dataset_name'],
split=data_test_opt['split'],
random_sized_crop=data_test_opt['random_sized_crop'])
dloader_train = DataLoader(dataset=dataset_train,
batch_size=data_train_opt['batch_size'],
unsupervised=data_train_opt['unsupervised'],
epoch_size=data_train_opt['epoch_size'],
num_workers=args_opt.num_workers,
shuffle=True)
dloader_test = DataLoader(dataset=dataset_test,
def main():
set_random_seed(0)
parser = argparse.ArgumentParser()
parser.add_argument('--exp',
type=str,
default='GenericFastAdapt',
help='config file with parameters of the experiment')
parser.add_argument('--num_workers',
type=int,
default=4,
help='number of data loading workers')
parser.add_argument('--cuda', type=bool, default=True, help='enables cuda')
parser.add_argument(
'--imagedir',
type=str,
default='',
help='path to image directory containing client images')
parser.add_argument('--experts_dir',
type=str,
default='',
help='path to directory containing experts')
args_opt = parser.parse_args()
exp_config_file = os.path.join('.', 'config', args_opt.exp + '.py')
exp_directory = os.path.join(
'.', 'experiments',
args_opt.exp + '-' + args_opt.experts_dir.replace('/', '-'))
# Load the configuration params of the experiment
print('Launching experiment: %s' % exp_config_file)
config = imp.load_source("", exp_config_file).config
config[
'exp_dir'] = exp_directory # the place where logs, models, and other stuff will be stored
config['image_directory'] = args_opt.imagedir
data_test_opt = config['data_test_opt']
dataset_test = GenericDataset(dataset_name=data_test_opt['dataset_name'],
split=data_test_opt['split'],
config=config)
dloader_test = DataLoader(dataset=dataset_test,
batch_size=data_test_opt['batch_size'],
num_workers=args_opt.num_workers,
shuffle=False)
z = {}
algorithm = ClassificationModel(config)
for expert_fname in os.listdir(args_opt.experts_dir):
expert_path = os.path.join(args_opt.experts_dir, expert_fname)
if not os.path.isfile(expert_path):
continue
algorithm.init_network()
algorithm.load_pretrained(expert_path)
if args_opt.cuda:
algorithm.load_to_gpu()
eval_stats = algorithm.evaluate(dloader_test)
z[expert_fname] = eval_stats['prec1']
print(z)
with open(os.path.join(exp_directory, 'z.pickle'), 'wb') as f:
pickle.dump(z, f, protocol=pickle.HIGHEST_PROTOCOL)