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

import os
import argparse
from src.config import cfg
from src.dataset import create_dataset
from src.generator_lr import get_lr
from src.alexnet import AlexNet
import mindspore.nn as nn
from mindspore import context
from mindspore import Tensor
from mindspore.train import Model
from mindspore.context import ParallelMode
from mindspore.nn.metrics import Accuracy
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.communication.management import init, get_rank, get_group_size
import mindspore.common.initializer as weight_init


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='MindSpore AlexNet Example')
    parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
    parser.add_argument('--device_num', type=int, default=1, help='Device num')
    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'],
                        help='device where the code will be implemented (default: Ascend)')
    parser.add_argument('--dataset_path', type=str, default="./", help='path where the dataset is saved')
    parser.add_argument('--pre_trained', type=str, default=None, help='Pre-trained checkpoint path')
    parser.add_argument('--dataset_sink_mode', type=str, default='True', choices = ['True', 'False'],
                        help='DataSet sink mode is True or False')
    args = parser.parse_args()

    target = args.device_target
    ckpt_save_dir = cfg.save_checkpoint_path
    dataset_sink_mode = args.dataset_sink_mode=='True'

    context.set_context(mode=context.GRAPH_MODE, device_target=target)
    if args.run_distribute:
        if target == "Ascend":
            device_id = int(os.getenv('DEVICE_ID'))
            context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
            context.set_auto_parallel_context(device_num=args.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                              gradients_mean=True)

            init()
        # GPU target
        else:
            init()
            context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
                                              gradients_mean=True)
            ckpt_save_dir = cfg.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"


    data_path = args.dataset_path
    do_train = True

    ds_train = create_dataset(data_path=data_path, batch_size=cfg.batch_size, do_train=do_train,
                              target=target, run_distribute=args.run_distribute)
    step_size = ds_train.get_dataset_size()

    # define net
    net = AlexNet(cfg.num_classes)

    # init weight
    if args.pre_trained:
        param_dict = load_checkpoint(args.pre_trained)
        load_param_into_net(net, param_dict)
    else:
        for _, cell in net.cells_and_names():
            if isinstance(cell, nn.Conv2d):
                cell.weight.set_data(weight_init.initializer(weight_init.XavierUniform(),
                                                             cell.weight.shape,
                                                             cell.weight.dtype))
            if isinstance(cell, nn.Dense):
                cell.weight.set_data(weight_init.initializer(weight_init.TruncatedNormal(),
                                                             cell.weight.shape,
                                                             cell.weight.dtype))
    # define learning rate
    lr = Tensor(get_lr(0, cfg.lr, cfg.epoch_size, ds_train.get_dataset_size()))

    # define loss, model
    {% if loss=='SoftmaxCrossEntropyWithLogits' %}
    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
    {% elif loss=='SoftmaxCrossEntropyExpand' %}
    loss = nn.SoftmaxCrossEntropyExpand(sparse=True)
    {% endif %}
    {% if optimizer=='Momentum' %}
    opt = nn.Momentum(net.trainable_params(), learning_rate=lr, momentum=cfg.momentum)
    {% else %}
    opt = nn.{{ optimizer }}(net.trainable_params(), learning_rate=lr)
    {% endif %}
    model = Model(net, loss, opt, metrics={"Accuracy": Accuracy()})

    # define callbacks
    time_cb = TimeMonitor(data_size=step_size)
    loss_cb = LossMonitor()
    cb = [time_cb, loss_cb]
    if cfg.save_checkpoint:
        cfg_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_epochs * step_size,
                                  keep_checkpoint_max=cfg.keep_checkpoint_max)
        ckpt_cb = ModelCheckpoint(prefix="alexnet", directory=ckpt_save_dir, config=cfg_ck)
        cb += [ckpt_cb]

    print("============== Starting Training ==============")
    model.train(cfg.epoch_size, ds_train, callbacks=cb, dataset_sink_mode=dataset_sink_mode)
