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- # Copyright 2021 The KubeEdge Authors.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- import os.path
-
- from sedna.common.config import Context
- from sedna.core.incremental_learning import IncrementalLearning
-
- from interface import Estimator
- from dataset import ImgDataset
-
- def main():
-
- class_names=Context.get_parameters("class_name")
- print(Context.get_parameters("model_path"))
- #read parameters from deployment config
- input_shape=int(Context.get_parameters("input_shape"))
- batch_size=int(Context.get_parameters("batch_size"))
- original_dataset_url=Context.get_parameters("ORIGINAL_DATASET_URL")
- num_parallel_workers=int(Context.get_parameters("num_parallel_workers"))
- if original_dataset_url:
- print("ORIGINAL_DATASET_URL"+ original_dataset_url)
- else:
- print("ORIGINAL_DATASET_URL: NULL" )
- eval_dataset_path=os.path.dirname(original_dataset_url)+r"/eval"
- test_data=ImgDataset(data_type="eval").parse(path=eval_dataset_path,
- train=False,
- image_shape=input_shape,
- batch_size=batch_size,
- num_parallel_workers=num_parallel_workers)
- incremental_instance = IncrementalLearning(estimator=Estimator)
- return incremental_instance.evaluate(test_data,
- class_names=class_names,
- input_shape=input_shape)
-
- if __name__ == "__main__":
- main()
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