args.env_name = args.env_name + "_" + now.strftime("%Y-%m-%d%H:%M:%S")
if args.use_wandb:
wandb = initialize_wandb(args)
writer = SummaryWriter(os.path.join(args.logdir, args.env_name))
pprint.pprint(vars(args))
sys.stdout.flush()
print("Current date and time : ")
print(now.strftime("%Y-%m-%d %H:%M:%S"))
print(f"Holdout dir is {holdout_dir}")
#############################################################################
# Load data from all the models
all_archs, models_filter, models_lst = get_data_info(args, meta_data)
all_data = load_data(feature_dir, meta_data, models_lst)
if holdout_dir is not None:
_, models_filter_holdout, models_lst_holdout = get_data_info(
args, holdout_metadata)
holdout_data = load_data(holdout_dir, holdout_metadata,
models_lst_holdout)
print("Dataset: ", args.dataset)
print("Running experiments on archs: ", all_archs)
#############################################################################
# Create splits
partitions = []
for _ in range(nfolds):
partitions.append(
get_splits_random(models_filter, all_archs, args.test_split,
"densenet169",
]
# models_filter = meta_data[meta_data.model_architecture.isin(all_archs)]
models_filter = meta_data[meta_data.trigger_type.isin(trigger_type)]
models_lst = models_filter.model_name.to_list()
print("Running experiments on", all_archs)
partitions = []
for _ in range(nfolds):
partitions.append(
get_spits(models_filter, all_archs, args.test_split, args.val_split)
)
#############################################################################
# Load data from all the models
all_data = load_data(feature_dir, meta_data, models_lst)
#############################################################################
# Setup Cross-validation and model
np.random.seed(0)
auc_all = []
scores_all = {}
# Trainer and model
# specify loss function
"""
device = check_for_cuda()
model = TrojNet()
model.to(device)
criterion = nn.CrossEntropyLoss()
# specify optimizer