assert (
cfg.test_imagedir is not None
), "Imagedir needs to be specified either via commandline argument (--imagedir) or config (test_imagedir)."
if not hasattr(cfg, "num_classes"): # infer number of classes
with open(cfg.annotations) as f:
NUM_CLASSES = len(json.load(f)["categories"])
cfg.num_classes = NUM_CLASSES
pathlib.Path(args.outdir).mkdir(exist_ok=True, parents=True)
save_config(cfg, args.outdir)
print(cfg)
print("Initializing model from checkpoint {}".format(args.checkpoint))
checkpoint = torch.load(args.checkpoint, map_location="cpu")
model = Model.from_config(cfg)
missing_keys, unexpected_keys = model.load_state_dict(
checkpoint['model_state_dict'], strict=False)
assert not missing_keys, "Checkpoint is missing keys required to initialize the model: {}".format(
missing_keys)
if len(unexpected_keys):
print(
"Checkpoint contains unexpected keys that were not used to initialize the model: "
)
print(unexpected_keys)
model.to(torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
test(model,
cfg.test_annotations,
assert (
cfg.test_imagedir is not None
), "Imagedir needs to be specified either via commandline argument (--imagedir) or config (test_imagedir)."
if not hasattr(cfg, "num_classes"): # infer number of classes
with open(cfg.annotations) as f:
NUM_CLASSES = len(json.load(f)["categories"])
cfg.num_classes = NUM_CLASSES
pathlib.Path(args.outdir).mkdir(exist_ok=True, parents=True)
save_config(cfg, args.outdir)
print(cfg)
print("Initializing model from checkpoint {}".format(args.checkpoint))
checkpoint = torch.load(args.checkpoint, map_location="cpu")
model = Model.from_config(cfg, extended_output=True)
missing_keys, unexpected_keys = model.load_state_dict(
checkpoint['model_state_dict'], strict=False)
assert not missing_keys, "Checkpoint is missing keys required to initialize the model: {}".format(
missing_keys)
if len(unexpected_keys):
print(
"Checkpoint contains unexpected keys that were not used to initialize the model: "
)
print(unexpected_keys)
model.to(torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
evaluate_uncertainty(model,
cfg.test_annotations,
