# 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.
# ============================================================================
"""
######################## train lenet example ########################
train lenet and get network model files(.ckpt) :
python train.py --data_path /YourDataPath
"""

import os
import argparse
import mindspore.nn as nn
from mindspore import context
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train import Model
from mindspore.nn.metrics import Accuracy
from src.dataset import create_dataset
from src.config import mnist_cfg as cfg
from src.lenet_fusion import LeNet5 as LeNet5Fusion

parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
parser.add_argument('--device_target', type=str, default="Ascend",
                    choices=['Ascend', 'GPU', 'CPU'],
                    help='device where the code will be implemented (default: Ascend)')
parser.add_argument('--data_path', type=str, default="./MNIST_Data",
                    help='path where the dataset is saved')
parser.add_argument('--ckpt_path', type=str, default="",
                    help='if mode is test, must provide path where the trained ckpt file')
parser.add_argument('--dataset_sink_mode', type=bool, default=True,
                    help='dataset_sink_mode is False or True')
args = parser.parse_args()

if __name__ == "__main__":
    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
    ds_train = create_dataset(os.path.join(args.data_path, "train"), cfg.batch_size, cfg.epoch_size)
    step_size = ds_train.get_dataset_size()

    # define fusion network
    network = LeNet5Fusion(cfg.num_classes)
    # define network loss
    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
    # define network optimization
    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)

    # call back and monitor
    time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
    config_ckpt = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size,
                                   keep_checkpoint_max=cfg.keep_checkpoint_max,
                                   model_type=network.type)
    ckpt_callback = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ckpt)

    # define model
    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})

    print("============== Starting Training ==============")
    model.train(cfg['epoch_size'], ds_train, callbacks=[time_cb, ckpt_callback, LossMonitor()],
                dataset_sink_mode=args.dataset_sink_mode)
    print("============== End Training ==============")