def init_model(transform):
args = parse_args()
if args.config is not None:
print(args.config)
set_cfg(args.config)
cfg.mask_proto_debug = False
if args.trained_model == 'interrupt':
args.trained_model = SavePath.get_interrupt('weights/')
elif args.trained_model == 'latest':
args.trained_model = SavePath.get_latest('weights/', cfg.name)
if args.config is None:
model_path = SavePath.from_str(args.trained_model)
# TODO: Bad practice? Probably want to do a name lookup instead.
args.config = model_path.model_name + '_config'
print('Config not specified. Parsed %s from the file name.\n' %
args.config)
set_cfg(args.config)
if args.detect:
cfg.eval_mask_branch = False
if args.dataset is not None:
set_dataset(args.dataset)
with torch.no_grad():
if args.cuda:
cudnn.fastest = True
torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
torch.set_default_tensor_type('torch.FloatTensor')
print('Loading model...', end='')
net = Yolact()
net.load_weights(args.trained_model)
net.eval()
print(' Done.')
net = net.cuda()
net = CustomDataParallel(net).cuda()
transform = torch.nn.DataParallel(FastBaseTransform()).cuda()
return net, args
if args.trained_model == 'interrupt':
args.trained_model = SavePath.get_interrupt('weights/')
elif args.trained_model == 'latest':
args.trained_model = SavePath.get_latest('weights/', cfg.name)
if args.config is None:
model_path = SavePath.from_str(args.trained_model)
args.config = model_path.model_name + '_config'
print('Config not specified. Parsed %s from the file name.\n' % args.config)
set_cfg(args.config)
if args.detect:
cfg.eval_mask_branch = False
if args.dataset is not None:
set_dataset(args.dataset)
with torch.no_grad():
if not os.path.exists('results'):
os.makedirs('results')
if args.cuda:
cudnn.fastest = True
torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
torch.set_default_tensor_type('torch.FloatTensor')
if args.resume and not args.display:
with open(args.ap_data_file, 'rb') as f:
ap_data = pickle.load(f)
calc_map(ap_data)
