def __init__(self, weights: str):
"""
:weights : weights file path
"""
self.weights = weights
self.model_path = SavePath.from_str(weights)
config = self.model_path.model_name + '_config'
set_cfg(config)
def __init__(self,
checkpoint_path:
Path = "../../checkpoints/yolact_darknet53_9999_120000.pth",
config: str = "yolact_darknet53_config",
output_dir_path: Optional[Path] = None,
use_gpu: bool = True,
verbose: bool = False,
show_timing_perf: bool = False):
"""
Args:
checkpoint_path (Path): Path to the checkpoint to use.
config (str): The config to use
output_dir_path (Path, Optional):
use_gpu (bool): Controls wether to use a gpu or not
verbose (bool): If true then prints information useful for debugging
show_timing_perf (bool): If true then prints then time each operation takes
"""
if output_dir_path:
output_dir_path: Path = output_dir_path
output_dir_path.mkdir(parents=True, exist_ok=True)
self.output_dir_path = output_dir_path
self.verbose = verbose
self.show_timing_perf = show_timing_perf
# Set the yolact config and the default tensor type
set_cfg(config)
torch.set_default_tensor_type(
"torch.cuda.FloatTensor" if use_gpu else "torch.FloatTensor")
print("Loading yolact model...", end="\r")
self.net = Yolact()
self.net.load_weights(str(checkpoint_path))
self.net.eval()
clean_print("Yolact model loaded")
if use_gpu:
self.net = self.net.cuda()
# Use the default values
self.net.detect.use_fast_nms = True # Whether to use a faster, but not entirely correct version of NMS
self.net.detect.use_cross_class_nms = False # Whether compute NMS cross-class or per-class
cfg.mask_proto_debug = False # Outputs stuff for scripts/compute_mask.py
print(make_sep(len(all_maps['box']) + 1))
for iou_type in ('box', 'mask'):
print(
make_row([iou_type] + [
'%.2f' % x if x < 100 else '%.1f' % x
for x in all_maps[iou_type].values()
]))
print(make_sep(len(all_maps['box']) + 1))
print()
if __name__ == '__main__':
parse_args()
if args.config is not None:
set_cfg(args.config)
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: