def load_G(self):
ckpt_path = self._get_ckpt_path(
ckpt_dir=self.trained_supernet_ckpt_dir,
ckpt_epoch=self.ckpt_epoch,
iter_every_epoch=self.supernet_iter_every_epoch)
saved_model = self.get_saved_model()
checkpointer = Checkpointer(saved_model, save_to_disk=False)
loaded_checkpoint = checkpointer.resume_or_load(ckpt_path,
resume=False)
pass
def load_model_weights(self,
ckpt_dir,
ckpt_epoch,
ckpt_iter_every_epoch,
ckpt_path=None):
if ckpt_path is None:
ckpt_path = self._get_ckpt_path(ckpt_dir, ckpt_epoch,
ckpt_iter_every_epoch)
model = self.get_saved_model()
checkpointer = Checkpointer(model, save_to_disk=False)
checkpointer.resume_or_load(ckpt_path, resume=False)
pass
def _load_G(self, eval_ckpt):
checkpointer = Checkpointer(self.get_saved_model(), save_to_disk=False)
# strip .module.
checkpoint = checkpointer._load_file(eval_ckpt)
checkpoint_state_dict = checkpoint['model']
checkpointer._convert_ndarray_to_tensor(checkpoint_state_dict)
self._strip_module_if_present(checkpoint_state_dict)
self.logger.info(f"Load model from {eval_ckpt}")
checkpointer._load_model(checkpoint)
pass
def get_d2_checkpointer(model_dict, optim_dict, ckptdir):
ckpt_model = DumpModule(model_dict)
checkpointer = Checkpointer(ckpt_model, ckptdir, **optim_dict)
return checkpointer
def _load_G(self, eval_ckpt):
checkpointer = Checkpointer(self.get_saved_model(), save_to_disk=False)
checkpointer.resume_or_load(eval_ckpt)
pass
return imgs_anns
for d in ["train", "test"]:
DatasetCatalog.register("caltech_" + d, lambda d=d: get_caltech_dicts(d))
MetadataCatalog.get("caltech_" + d).set(thing_classes=["person"])
caltech_metadata = MetadataCatalog.get("caltech_train")
cfg = get_cfg()
cfg.merge_from_file("./configs/frcn_dt.yaml")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = .0 # set to 0 to achieve smaller miss rate
predictor = DefaultPredictor(cfg)
evaluator = COCOEvaluator("caltech_test", cfg, False, output_dir='./output/')
val_loader = build_detection_test_loader(cfg, "caltech_test")
model = build_model(cfg)
ckpt = Checkpointer(model)
ckpt.load(os.path.join(cfg.OUTPUT_DIR, "model_0049999.pth"))
# inference_on_dataset(model, val_loader, evaluator) # compute map value
# convert to caltech data eval format
res = {}
with torch.no_grad():
model.eval()
for inputs in tqdm(val_loader):
outputs = model(inputs)
for i, o in zip(inputs, outputs):
fn = i['file_name']
idx = fn.rfind('/') + 1
fn = fn[idx:-4].split('_')
sid, vid, frame = fn[0], fn[1], int(fn[2]) + 1
if sid not in res:
if not logger.isEnabledFor(logging.INFO): # setup_logger is not called for d2
setup_logger()
model = build_model(cfg)
logger.info("Model:\n{}".format(model))
model.train()
optimizer = build_optimizer(cfg, model)
scheduler = build_lr_scheduler(cfg, optimizer)
checkpointer = DetectionCheckpointer(model,
cfg.OUTPUT_DIR,
optimizer=optimizer,
scheduler=scheduler)
start_iter = (checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=False).get(
"iteration", -1) + 1)
ckpt = Checkpointer(model)
ckpt.load("./frcn_attn_0/model_0044999.pth")
max_iter = cfg.SOLVER.MAX_ITER
periodic_checkpointer = PeriodicCheckpointer(checkpointer,
cfg.SOLVER.CHECKPOINT_PERIOD,
max_iter=max_iter)
writers = ([
CommonMetricPrinter(max_iter),
JSONWriter(os.path.join(cfg.OUTPUT_DIR, "metrics.json")),
TensorboardXWriter(cfg.OUTPUT_DIR),
] if comm.is_main_process() else [])
# compared to "train_net.py", we do not support accurate timing and
# precise BN here, because they are not trivial to implement