# 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_imagenet."""
import argparse
import os
import random
import numpy as np

import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore import ParallelMode
from mindspore.communication.management import init, get_rank, get_group_size
from mindspore.nn.optim.rmsprop import RMSProp
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore import dataset as de
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from mindspore.common.initializer import XavierUniform, initializer

from src.config import config_gpu, config_ascend
from src.dataset import create_dataset
from src.inception_v3 import InceptionV3
from src.lr_generator import get_lr
from src.loss import CrossEntropy

random.seed(1)
np.random.seed(1)
de.config.set_seed(1)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='image classification training')
    parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
    parser.add_argument('--resume', type=str, default='', help='resume training with existed checkpoint')
    parser.add_argument('--is_distributed', action='store_true', default=False,
                        help='distributed training')
    parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU'), help='run platform')
    args_opt = parser.parse_args()

    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform, save_graphs=False)
    if os.getenv('DEVICE_ID', "not_set").isdigit():
        context.set_context(device_id=int(os.getenv('DEVICE_ID')))
    cfg = config_ascend if args_opt.platform == 'Ascend' else config_gpu
    # init distributed
    if args_opt.is_distributed:
        if args_opt.platform == "Ascend":
            init()
        else:
            init("nccl")
        cfg.rank = get_rank()
        cfg.group_size = get_group_size()
        parallel_mode = ParallelMode.DATA_PARALLEL
        context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=cfg.group_size,
                                          parameter_broadcast=True, mirror_mean=True)
    else:
        cfg.rank = 0
        cfg.group_size = 1

    # dataloader
    dataset = create_dataset(args_opt.dataset_path, True, cfg.rank, cfg.group_size)
    batches_per_epoch = dataset.get_dataset_size()

    # network
    net = InceptionV3(num_classes=cfg.num_classes, dropout_keep_prob=cfg.dropout_keep_prob, has_bias=cfg.has_bias)

    # loss
    loss = CrossEntropy(smooth_factor=cfg.smooth_factor, num_classes=cfg.num_classes, factor=cfg.aux_factor)

    # learning rate schedule
    lr = get_lr(lr_init=cfg.lr_init, lr_end=cfg.lr_end, lr_max=cfg.lr_max, warmup_epochs=cfg.warmup_epochs,
                total_epochs=cfg.epoch_size, steps_per_epoch=batches_per_epoch, lr_decay_mode=cfg.decay_method)
    lr = Tensor(lr)

    # optimizer
    decayed_params = []
    no_decayed_params = []
    for param in net.trainable_params():
        if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
            decayed_params.append(param)
        else:
            no_decayed_params.append(param)

    if args_opt.platform == "Ascend":
        for param in net.trainable_params():
            if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
                np.random.seed(seed=1)
                param.set_parameter_data(initializer(XavierUniform(), param.data.shape, param.data.dtype))
    group_params = [{'params': decayed_params, 'weight_decay': cfg.weight_decay},
                    {'params': no_decayed_params},
                    {'order_params': net.trainable_params()}]
    optimizer = RMSProp(group_params, lr, decay=0.9, weight_decay=cfg.weight_decay,
                        momentum=cfg.momentum, epsilon=cfg.opt_eps, loss_scale=cfg.loss_scale)
    eval_metrics = {'Loss': nn.Loss(),
                    'Top1-Acc': nn.Top1CategoricalAccuracy(),
                    'Top5-Acc': nn.Top5CategoricalAccuracy()}

    if args_opt.resume:
        ckpt = load_checkpoint(args_opt.resume)
        load_param_into_net(net, ckpt)
    if args_opt.platform == "Ascend":
        loss_scale_manager = FixedLossScaleManager(cfg.loss_scale, drop_overflow_update=False)
        model = Model(net, loss_fn=loss, optimizer=optimizer, metrics={'acc'}, amp_level=cfg.amp_level,
                      loss_scale_manager=loss_scale_manager)
    else:
        model = Model(net, loss_fn=loss, optimizer=optimizer, metrics={'acc'}, amp_level=cfg.amp_level)

    print("============== Starting Training ==============")
    loss_cb = LossMonitor(per_print_times=batches_per_epoch)
    time_cb = TimeMonitor(data_size=batches_per_epoch)
    callbacks = [loss_cb, time_cb]
    config_ck = CheckpointConfig(save_checkpoint_steps=batches_per_epoch, keep_checkpoint_max=cfg.keep_checkpoint_max)
    ckpoint_cb = ModelCheckpoint(prefix=f"inceptionv3-rank{cfg.rank}", directory=cfg.ckpt_path, config=config_ck)
    if args_opt.is_distributed & cfg.is_save_on_master:
        if cfg.rank == 0:
            callbacks.append(ckpoint_cb)
        model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True)
    else:
        callbacks.append(ckpoint_cb)
        model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True)
    print("train success")