sedna/examples/incremental_learning/helmet_detection/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

import tensorflow as tf

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

from interface import Estimator


def _load_txt_dataset(dataset_url):
    # use original dataset url,
    # see https://github.com/kubeedge/sedna/issues/35
    original_dataset_url = Context.get_parameters('original_dataset_url')
    return os.path.join(os.path.dirname(original_dataset_url), dataset_url)


def main():
    tf.set_random_seed(22)

    class_names = Context.get_parameters("class_names")

    # load dataset.
    train_dataset_url = BaseConfig.train_dataset_url
    train_data = TxtDataParse(data_type="train", func=_load_txt_dataset)
    train_data.parse(train_dataset_url, use_raw=True)

    # read parameters from deployment config.
    obj_threshold = Context.get_parameters("obj_threshold")
    nms_threshold = Context.get_parameters("nms_threshold")
    input_shape = Context.get_parameters("input_shape")
    epochs = Context.get_parameters('epochs')
    batch_size = Context.get_parameters('batch_size')

    tf.flags.DEFINE_string('train_url', default=BaseConfig.model_url,
                           help='train url for model')
    tf.flags.DEFINE_string('log_url', default=None, help='log url for model')
    tf.flags.DEFINE_string('checkpoint_url', default=None,
                           help='checkpoint url for model')
    tf.flags.DEFINE_string('model_name', default=None,
                           help='url for train annotation files')
    tf.flags.DEFINE_list('class_names', default=class_names.split(','),
                         # 'helmet,helmet-on,person,helmet-off'
                         help='label names for the training datasets')
    tf.flags.DEFINE_list('input_shape',
                         default=[int(x) for x in input_shape.split(',')],
                         help='input_shape')  # [352, 640]
    tf.flags.DEFINE_integer('max_epochs', default=epochs,
                            help='training number of epochs')
    tf.flags.DEFINE_integer('batch_size', default=batch_size,
                            help='training batch size')
    tf.flags.DEFINE_boolean('load_imagenet_weights', default=False,
                            help='if load imagenet weights or not')
    tf.flags.DEFINE_string('inference_device',
                           default='GPU',
                           help='which type of device is used to do inference,'
                                ' only CPU, GPU or 310D')
    tf.flags.DEFINE_boolean('copy_to_local', default=True,
                            help='if load imagenet weights or not')
    tf.flags.DEFINE_integer('num_gpus', default=1, help='use number of gpus')
    tf.flags.DEFINE_boolean('finetuning', default=False,
                            help='use number of gpus')
    tf.flags.DEFINE_boolean('label_changed', default=False,
                            help='whether number of labels is changed or not')
    tf.flags.DEFINE_string('learning_rate', default='0.001',
                           help='learning rate to used for the optimizer')
    tf.flags.DEFINE_string('obj_threshold', default=obj_threshold,
                           help='obj threshold')
    tf.flags.DEFINE_string('nms_threshold', default=nms_threshold,
                           help='nms threshold')
    tf.flags.DEFINE_string('net_type', default='resnet18',
                           help='resnet18 or resnet18_nas')
    tf.flags.DEFINE_string('nas_sequence', default='64_1-2111-2-1112',
                           help='resnet18 or resnet18_nas')
    tf.flags.DEFINE_string('deploy_model_format', default=None,
                           help='the format for the converted model')
    tf.flags.DEFINE_string('result_url', default=None,
                           help='result url for training')

    incremental_instance = IncrementalLearning(estimator=Estimator)
    return incremental_instance.train(train_data=train_data, epochs=epochs,
                                      batch_size=batch_size,
                                      class_names=class_names,
                                      input_shape=input_shape,
                                      obj_threshold=obj_threshold,
                                      nms_threshold=nms_threshold)


if __name__ == '__main__':
    main()