# 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 googlent example on cifar10########################
python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
"""
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.communication.management import init
from mindspore.model_zoo.googlenet import GooGLeNet
from mindspore.nn.optim.momentum import Momentum
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train.model import Model, ParallelMode


from dataset import create_dataset
from config import cifar_cfg as cfg

random.seed(1)
np.random.seed(1)


def lr_steps(global_step, lr_max=None, total_epochs=None, steps_per_epoch=None):
    """Set learning rate."""
    lr_each_step = []
    total_steps = steps_per_epoch * total_epochs
    decay_epoch_index = [0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps]
    for i in range(total_steps):
        if i < decay_epoch_index[0]:
            lr_each_step.append(lr_max)
        elif i < decay_epoch_index[1]:
            lr_each_step.append(lr_max * 0.1)
        elif i < decay_epoch_index[2]:
            lr_each_step.append(lr_max * 0.01)
        else:
            lr_each_step.append(lr_max * 0.001)
    current_step = global_step
    lr_each_step = np.array(lr_each_step).astype(np.float32)
    learning_rate = lr_each_step[current_step:]

    return learning_rate


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Cifar10 classification')
    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('--data_path', type=str, default='./cifar', help='path where the dataset is saved')
    parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
    args_opt = parser.parse_args()

    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
    context.set_context(device_id=args_opt.device_id)

    device_num = int(os.environ.get("DEVICE_NUM", 1))
    if device_num > 1:
        context.reset_auto_parallel_context()
        context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                          mirror_mean=True)
        init()

    dataset = create_dataset(args_opt.data_path, cfg.epoch_size)
    batch_num = dataset.get_dataset_size()

    net = GooGLeNet(num_classes=cfg.num_classes)
    lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, steps_per_epoch=batch_num)
    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), Tensor(lr), cfg.momentum,
                   weight_decay=cfg.weight_decay)
    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
    model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
                  amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)

    config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, keep_checkpoint_max=cfg.keep_checkpoint_max)
    time_cb = TimeMonitor(data_size=batch_num)
    ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_cifar10", directory="./", config=config_ck)
    loss_cb = LossMonitor()
    model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
    print("train success")