def _make_pred_func(self, load):
from train import ResNetFPNTrackModel
pred_model = ResNetFPNTrackModel()
predcfg = PredictConfig(
model=pred_model,
session_init=get_model_loader(load),
input_names=pred_model.get_inference_tensor_names()[0],
output_names=pred_model.get_inference_tensor_names()[1])
return OfflinePredictor(predcfg)
def _init_model(self):
logger.set_logger_dir("/tmp/test_log/", 'd')
from dataset import DetectionDataset
from train import ResNetFPNTrackModel
# init tensorpack model
cfg.freeze(False)
model = ResNetFPNTrackModel()
DetectionDataset(
) # initialize the config with information from our dataset
finalize_configs(is_training=False)
return model