eval.py

# Copyright 2022 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.
# ============================================================================
"""eval"""

from mindspore import Model
from mindspore import context
from mindspore import nn
from mindspore.common import set_seed

from src.args import args
from src.tools.cell import cast_amp
from src.tools.criterion import get_criterion, NetWithLoss
from src.tools.get_misc import get_dataset, set_device, get_model, pretrained, get_train_one_step
from src.tools.optimizer import get_optimizer

set_seed(args.seed)


def main():
    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
    context.set_context(enable_graph_kernel=False)
    if args.device_target == "Ascend":
        context.set_context(enable_auto_mixed_precision=True)
    set_device(args)

    # get model
    net = get_model(args)
    net.update_parameters_name("model.")
    cast_amp(net)
    criterion = get_criterion(args)

    net_with_loss = NetWithLoss(net, criterion)
    if args.pretrained:
        pretrained(args, net)
    data = get_dataset(args, training=False)
    batch_num = data.val_dataset.get_dataset_size()
    optimizer = get_optimizer(args, net, batch_num)
    # save a yaml file to read to record parameters

    net_with_loss = get_train_one_step(args, net_with_loss, optimizer)
    eval_network = nn.WithEvalCell(net, criterion, args.amp_level in ["O2", "O3", "auto"])
    eval_indexes = [0, 1, 2]
    eval_metrics = {'Loss': nn.Loss(),
                    'Top1-Acc': nn.Top1CategoricalAccuracy(),
                    'Top5-Acc': nn.Top5CategoricalAccuracy()}
    model = Model(net_with_loss, metrics=eval_metrics,
                  eval_network=eval_network,
                  eval_indexes=eval_indexes)
    print(f"=> begin eval")
    results = model.eval(data.val_dataset)
    print(f"=> eval results:{results}")
    print(f"=> eval success")


if __name__ == '__main__':
    main()