sedna/examples/incremental_learning/dog_croissants_classification/training/train.py

# 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

from sedna.common.config import Context, BaseConfig
from sedna.core.incremental_learning import IncrementalLearning

from interface import Estimator
from dataset import ImgDataset


def main():
    # base_model_url means the low accuracy model
    base_model_url=Context.get_parameters("base_model_url")
    # model_url means the checkpoint file that has been trained
    # model_url is used for estimator.save, not in train.
    trained_ckpt_url = Context.get_parameters("model_url")
    #read parameters from deployment config
    input_shape=int(Context.get_parameters("input_shape"))
    epochs=int(Context.get_parameters('epochs'))
    batch_size=int(Context.get_parameters("batch_size"))
    num_parallel_workers=int(Context.get_parameters("num_parallel_workers"))
    print("num_parallel_workers="+str(num_parallel_workers))
    # load dataset
    train_dataset_url=os.path.dirname(Context.get_parameters("ORIGINAL_DATASET_URL"))+"/train"
    valid_dataset_url=os.path.dirname(Context.get_parameters("ORIGINAL_DATASET_URL"))+"/val"
    if train_dataset_url:
        print("train_dataset_url " + train_dataset_url)
    else:
        print("train_dataset_url: NULL ")
    if valid_dataset_url:
        print("valid_dataset_urlL : " + valid_dataset_url)
    else:
        print("valid_dataset_url : NULL")


    train_data = ImgDataset(data_type="train").parse(path=train_dataset_url,
                                                     train=True,
                                                     image_shape=input_shape,
                                                     batch_size=batch_size,
                                                     num_parallel_workers=num_parallel_workers)
    valid_data=ImgDataset(data_type="eval").parse(path=valid_dataset_url,
                                                  train=False,
                                                  image_shape=input_shape,
                                                  batch_size=batch_size,
                                                  num_parallel_workers=num_parallel_workers)
    incremental_instance = IncrementalLearning(estimator=Estimator)
    return incremental_instance.train(train_data=train_data,
                                      base_model_url=base_model_url,
                                      trained_ckpt_url=trained_ckpt_url,
                                      valid_data=valid_data,
                                      epochs=1)

if __name__ == "__main__":
    main()
    print("train_phase_done")