args.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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 argparse


def launch_parse_args():

    launch_parser = argparse.ArgumentParser(description="mindspore distributed training launch helper utilty \
        that will spawn up multiple distributed processes")
    launch_parser.add_argument('--platform', type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"), \
        help='run platform, only support GPU, CPU and Ascend')
    launch_parser.add_argument("--nproc_per_node", type=int, default=1, choices=(0, 1, 2, 3, 4, 5, 6, 7), \
        help="The number of processes to launch on each node, for D training, this is recommended to be set \
            to the number of D in your system so that each process can be bound to a single D.")
    launch_parser.add_argument("--visible_devices", type=str, default="0,1,2,3,4,5,6,7", help="will use the \
        visible devices sequentially")
    launch_parser.add_argument("--training_script", type=str, default="./train.py", help="The full path to \
        the single D training program/script to be launched in parallel, followed by all the arguments for \
            the training script")

    launch_args, unknown = launch_parser.parse_known_args()
    launch_args.train_script_args = unknown
    launch_args.training_script_args += ["--platform", launch_args.platform]
    return launch_args

def train_parse_args():
    train_parser = argparse.ArgumentParser(description='Image classification trian')
    train_parser.add_argument('--dataset_path', type=str, required=True, help='Dataset path')
    train_parser.add_argument('--platform', type=str, default="Ascend", choices=("CPU", "GPU", "Ascend"), \
        help='run platform, only support CPU, GPU and Ascend')
    train_parser.add_argument('--pretrain_ckpt', type=str, default=None, help='Pretrained checkpoint path \
        for fine tune or incremental learning')
    train_parser.add_argument('--train_method', type=str, choices=("train", "fine_tune", "incremental_learn"), \
        help="\"fine_tune\"or \"incremental_learn\" if to fine tune the net  after loading the ckpt, \"train\" to \
        train from initialization model")

    train_args = train_parser.parse_args()
    return train_args

def eval_parse_args():
    eval_parser = argparse.ArgumentParser(description='Image classification eval')
    eval_parser.add_argument('--dataset_path', type=str, required=True, help='Dataset path')
    eval_parser.add_argument('--platform', type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"), \
        help='run platform, only support GPU, CPU and Ascend')
    eval_parser.add_argument('--pretrain_ckpt', type=str, default=None, help='Pretrained checkpoint path \
        for fine tune or incremental learning')
    eval_parser.add_argument('--head_ckpt', type=str, default=None, help='Pretrained checkpoint path \
        for fine tune or incremental learning')
    eval_args = eval_parser.parse_args()

    return eval_args