# 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
#
# less 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 Retinaface_resnet50."""
from __future__ import print_function
import random
import math
import numpy as np

import mindspore.nn as nn
import mindspore.dataset as de
from mindspore import context
from mindspore.context import ParallelMode
from mindspore.train import Model
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.communication.management import init, get_rank, get_group_size
from mindspore.train.serialization import load_checkpoint, load_param_into_net

from src.config import cfg_res50
from src.network import RetinaFace, RetinaFaceWithLossCell, TrainingWrapper, resnet50
from src.loss import MultiBoxLoss
from src.dataset import create_dataset

def setup_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    de.config.set_seed(seed)

def adjust_learning_rate(initial_lr, gamma, stepvalues, steps_per_epoch, total_epochs, warmup_epoch=5):
    lr_each_step = []
    for epoch in range(1, total_epochs+1):
        for step in range(steps_per_epoch):
            if epoch <= warmup_epoch:
                lr = 1e-6 + (initial_lr - 1e-6) * ((epoch - 1) * steps_per_epoch + step) / \
                     (steps_per_epoch * warmup_epoch)
            else:
                if stepvalues[0] <= epoch <= stepvalues[1]:
                    lr = initial_lr * (gamma ** (1))
                elif epoch > stepvalues[1]:
                    lr = initial_lr * (gamma ** (2))
                else:
                    lr = initial_lr
            lr_each_step.append(lr)
    return lr_each_step

def train(cfg):

    context.set_context(mode=context.GRAPH_MODE, device_target='GPU', save_graphs=False)
    if cfg['ngpu'] > 1:
        init("nccl")
        context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
                                          gradients_mean=True)
        cfg['ckpt_path'] = cfg['ckpt_path'] + "ckpt_" + str(get_rank()) + "/"
    else:
        raise ValueError('cfg_num_gpu <= 1')

    batch_size = cfg['batch_size']
    max_epoch = cfg['epoch']

    momentum = cfg['momentum']
    weight_decay = cfg['weight_decay']
    initial_lr = cfg['initial_lr']
    gamma = cfg['gamma']
    training_dataset = cfg['training_dataset']
    num_classes = 2
    negative_ratio = 7
    stepvalues = (cfg['decay1'], cfg['decay2'])

    ds_train = create_dataset(training_dataset, cfg, batch_size, multiprocessing=True, num_worker=cfg['num_workers'])
    print('dataset size is : \n', ds_train.get_dataset_size())

    steps_per_epoch = math.ceil(ds_train.get_dataset_size())

    multibox_loss = MultiBoxLoss(num_classes, cfg['num_anchor'], negative_ratio, cfg['batch_size'])
    backbone = resnet50(1001)
    backbone.set_train(True)

    if cfg['pretrain'] and cfg['resume_net'] is None:
        pretrained_res50 = cfg['pretrain_path']
        param_dict_res50 = load_checkpoint(pretrained_res50)
        load_param_into_net(backbone, param_dict_res50)
        print('Load resnet50 from [{}] done.'.format(pretrained_res50))

    net = RetinaFace(phase='train', backbone=backbone)
    net.set_train(True)

    if cfg['resume_net'] is not None:
        pretrain_model_path = cfg['resume_net']
        param_dict_retinaface = load_checkpoint(pretrain_model_path)
        load_param_into_net(net, param_dict_retinaface)
        print('Resume Model from [{}] Done.'.format(cfg['resume_net']))

    net = RetinaFaceWithLossCell(net, multibox_loss, cfg)

    lr = adjust_learning_rate(initial_lr, gamma, stepvalues, steps_per_epoch, max_epoch,
                              warmup_epoch=cfg['warmup_epoch'])

    if cfg['optim'] == 'momentum':
        opt = nn.Momentum(net.trainable_params(), lr, momentum)
    elif cfg['optim'] == 'sgd':
        opt = nn.SGD(params=net.trainable_params(), learning_rate=lr, momentum=momentum,
                     weight_decay=weight_decay, loss_scale=1)
    else:
        raise ValueError('optim is not define.')

    net = TrainingWrapper(net, opt)

    model = Model(net)

    config_ck = CheckpointConfig(save_checkpoint_steps=cfg['save_checkpoint_steps'],
                                 keep_checkpoint_max=cfg['keep_checkpoint_max'])
    ckpoint_cb = ModelCheckpoint(prefix="RetinaFace", directory=cfg['ckpt_path'], config=config_ck)

    time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
    callback_list = [LossMonitor(), time_cb, ckpoint_cb]

    print("============== Starting Training ==============")
    model.train(max_epoch, ds_train, callbacks=callback_list,
                dataset_sink_mode=False)



if __name__ == '__main__':

    setup_seed(1)
    config = cfg_res50
    print('train config:\n', config)

    train(cfg=config)