del model
torch.cuda.empty_cache()
return probs, preds, labels
if __name__ == '__main__':
'''
Example:
python test.py --tta True --csv_out results/submission_UW_galdran_11Mar.csv
'''
data_path = 'data'
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
# reproducibility
seed_value = 0
set_seeds(seed_value, use_cuda)
# gather parser parameters
args = parser.parse_args()
model_name = args.model_name
load_path_od = args.load_path_od
load_path_mac = args.load_path_mac
pretrained = args.pretrained
bs = args.batch_size
csv_test_od = args.csv_test_od
csv_test_mac = args.csv_test_mac
n_classes = args.n_classes
tta = args.tta
csv_out = args.csv_out
####################################################################################################################
if args.device.startswith("cuda"):
# In case one has multiple devices, we must first set the one
# we would like to use so pytorch can find it.
os.environ['CUDA_VISIBLE_DEVICES'] = args.device.split(":", 1)[1]
if not torch.cuda.is_available():
raise RuntimeError("cuda is not currently available!")
print(f"* Training on device '{args.device}'...")
device = torch.device("cuda")
else: #cpu
device = torch.device(args.device)
# reproducibility
seed_value = 0
set_seeds(seed_value, args.device.startswith("cuda"))
# gather parser parameters
model_name = args.model_name
max_lr, min_lr, bs, grad_acc_steps = args.max_lr, args.min_lr, args.batch_size, args.grad_acc_steps
cycle_lens, metric = args.cycle_lens.split('/'), args.metric
cycle_lens = list(map(int, cycle_lens))
if len(
cycle_lens
) == 2: # handles option of specifying cycles as pair (n_cycles, cycle_len)
cycle_lens = cycle_lens[0] * [cycle_lens[1]]
im_size = tuple([int(item) for item in args.im_size.split(',')])
if isinstance(im_size, tuple) and len(im_size) == 1:
tg_size = (im_size[0], im_size[0])
