# 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 os
from ast import literal_eval as liter
import mindspore
from mindspore import context
from mindspore.context import ParallelMode
from mindspore.communication.management import init
from mindspore.train import Model
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
from mindspore.nn.optim import Adam, Momentum
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from src.dataset import create_dataset
from src.openposenet import OpenPoseNet
from src.loss import openpose_loss, BuildTrainNetwork, TrainOneStepWithClipGradientCell
from src.utils import get_lr, load_model, MyLossMonitor
from src.model_utils.moxing_adapter import moxing_wrapper
from src.model_utils.config import config
from src.model_utils.device_adapter import get_rank_id, get_device_num


mindspore.common.seed.set_seed(1)
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)


def modelarts_pre_process():
    pass


@moxing_wrapper(pre_process=modelarts_pre_process)
def train():
    """Train function."""
    config.lr = liter(config.lr)
    config.outputs_dir = config.save_model_path
    device_num = get_device_num()

    if device_num > 1:
        init()
        context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                          gradients_mean=True)
        config.rank = get_rank_id()
        config.outputs_dir = os.path.join(config.outputs_dir, "ckpt_{}/".format(config.rank))
    else:
        config.outputs_dir = os.path.join(config.outputs_dir, "ckpt_0/")
        config.rank = 0

    if device_num > 1:
        config.max_epoch = config.max_epoch_train_NP
        config.loss_scale = config.loss_scale / 2
        config.lr_steps = list(map(int, config.lr_steps_NP.split(',')))
        config.train_type = config.train_type_NP
        config.optimizer = config.optimizer_NP
        config.group_params = config.group_params_NP
    else:
        config.max_epoch = config.max_epoch_train
        config.loss_scale = config.loss_scale
        config.lr_steps = list(map(int, config.lr_steps.split(',')))

    # create network
    print('start create network')
    criterion = openpose_loss()
    criterion.add_flags_recursive(fp32=True)
    network = OpenPoseNet(vggpath=config.vgg_path, vgg_with_bn=config.vgg_with_bn)
    if config.load_pretrain:
        print("load pretrain model:", config.pretrained_model_path)
        load_model(network, config.pretrained_model_path)
    train_net = BuildTrainNetwork(network, criterion)

    # create dataset
    if os.path.exists(config.jsonpath_train) and os.path.exists(config.imgpath_train) \
            and os.path.exists(config.maskpath_train):
        print('start create dataset')
    else:
        print('Error: wrong data path')
        return 0

    num_worker = 20 if device_num > 1 else 48
    de_dataset_train = create_dataset(config.jsonpath_train, config.imgpath_train, config.maskpath_train,
                                      batch_size=config.batch_size,
                                      rank=config.rank,
                                      group_size=device_num,
                                      num_worker=num_worker,
                                      multiprocessing=True,
                                      shuffle=True,
                                      repeat_num=1)
    steps_per_epoch = de_dataset_train.get_dataset_size()
    print("steps_per_epoch: ", steps_per_epoch)

    # lr scheduler
    lr_stage, lr_base, lr_vgg = get_lr(config.lr * device_num,
                                       config.lr_gamma,
                                       steps_per_epoch,
                                       config.max_epoch,
                                       config.lr_steps,
                                       device_num,
                                       lr_type=config.lr_type,
                                       warmup_epoch=config.warmup_epoch)

    # optimizer
    if config.group_params:
        vgg19_base_params = list(filter(lambda x: 'base.vgg_base' in x.name, train_net.trainable_params()))
        base_params = list(filter(lambda x: 'base.conv' in x.name, train_net.trainable_params()))
        stages_params = list(filter(lambda x: 'base' not in x.name, train_net.trainable_params()))

        group_params = [{'params': vgg19_base_params, 'lr': lr_vgg},
                        {'params': base_params, 'lr': lr_base},
                        {'params': stages_params, 'lr': lr_stage}]

        if config.optimizer == "Momentum":
            opt = Momentum(group_params, learning_rate=lr_stage, momentum=0.9)
        elif config.optimizer == "Adam":
            opt = Adam(group_params)
        else:
            raise ValueError("optimizer not support.")
    else:
        if config.optimizer == "Momentum":
            opt = Momentum(train_net.trainable_params(), learning_rate=lr_stage, momentum=0.9)
        elif config.optimizer == "Adam":
            opt = Adam(train_net.trainable_params(), learning_rate=lr_stage)
        else:
            raise ValueError("optimizer not support.")

    # callback
    config_ck = CheckpointConfig(save_checkpoint_steps=config.ckpt_interval,
                                 keep_checkpoint_max=config.keep_checkpoint_max)
    ckpoint_cb = ModelCheckpoint(prefix='{}'.format(config.rank), directory=config.outputs_dir, config=config_ck)
    time_cb = TimeMonitor(data_size=de_dataset_train.get_dataset_size())
    if config.rank == 0:
        callback_list = [MyLossMonitor(), time_cb, ckpoint_cb]
    else:
        callback_list = [MyLossMonitor(), time_cb]

    # train
    if config.train_type == 'clip_grad':
        train_net = TrainOneStepWithClipGradientCell(train_net, opt, sens=config.loss_scale)
        train_net.set_train()
        model = Model(train_net)
    elif config.train_type == 'fix_loss_scale':
        loss_scale_manager = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
        train_net.set_train()
        model = Model(train_net, optimizer=opt, loss_scale_manager=loss_scale_manager)
    else:
        raise ValueError("Type {} is not support.".format(config.train_type))

    print("============== Starting Training ==============")
    model.train(config.max_epoch, de_dataset_train, callbacks=callback_list,
                dataset_sink_mode=False)
    return 0


if __name__ == "__main__":
    train()