# Copyright 2021 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.
# ============================================================================
"""use metrics
This sample code is applicable to Ascend, CPU and GPU.
"""
import mindspore.nn as nn
from mindspore.nn import Momentum, SoftmaxCrossEntropyWithLogits
from mindspore import Model, context
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor

from src.dataset import create_train_dataset, create_eval_dataset
from src.net import Net


if __name__ == "__main__":
    context.set_context(mode=context.GRAPH_MODE)
    ds_train = create_train_dataset()
    ds_eval = create_eval_dataset()
    net = Net()
    net_opt = Momentum(net.trainable_params(), 0.01, 0.9)
    net_loss = SoftmaxCrossEntropyWithLogits(reduction='mean')
    metrics = {
        'Accuracy': nn.Accuracy(),
        'Loss': nn.Loss(),
        'Precision': nn.Precision(),
        'Recall': nn.Recall(),
        'F1_score': nn.F1()
    }
    config_ck = CheckpointConfig(save_checkpoint_steps=1000, keep_checkpoint_max=10)
    ckpoint = ModelCheckpoint(prefix="CKPT", config=config_ck)
    model = Model(network=net, loss_fn=net_loss, optimizer=net_opt, metrics=metrics)
    model.train(epoch=2, train_dataset=ds_train, callbacks=[ckpoint, LossMonitor()])
    result = model.eval(ds_eval)
    print(result)