|
- import time
-
- from sedna.datasources import BaseDataSource
- from sedna.core.lifelong_learning import LifelongLearning
-
- from basemodel import Model
-
-
- def preprocess(samples):
- data = BaseDataSource(data_type="test")
- data.x = [samples]
- return data
-
-
- def postprocess(samples):
- image_names, imgs = [], []
- for sample in samples:
- img = sample.get("image")
- image_names.append("{}.png".format(str(time.time())))
- imgs.append(img)
-
- return image_names, imgs
-
-
- def init_ll_job():
- estimator = Model(num_class=31,
- save_predicted_image=True,
- merge=True)
-
- task_allocation = {
- "method": "TaskAllocationDefault"
- }
- unseen_task_allocation = {
- "method": "UnseenTaskAllocationDefault"
- }
-
- ll_job = LifelongLearning(
- estimator,
- unseen_estimator=unseen_task_processing,
- task_definition=None,
- task_relationship_discovery=None,
- task_allocation=task_allocation,
- task_remodeling=None,
- inference_integrate=None,
- task_update_decision=None,
- unseen_task_allocation=unseen_task_allocation,
- unseen_sample_recognition=None,
- unseen_sample_re_recognition=None)
- return ll_job
-
-
- def unseen_task_processing():
- return "Warning: unseen sample detected."
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