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.
# ============================================================================
"""MindSpore Vision Video eval script."""

from subprocess import call
from mindspore import context, nn, load_checkpoint, load_param_into_net
from mindspore.train import Model
from mindspore.nn.metrics import Accuracy

from src.utils.check_param import Validator, Rel
from src.utils.config import parse_args, Config
from src.utils.callbacks import EvalLossMonitor
from src.loss.builder import build_loss
from src.data.builder import build_dataset, build_transforms
from src.models import build_model


def eval(pargs):
    # set config context
    config = Config(pargs.config)
    context.set_context(**config.context)

    # perpare dataset
    transforms = build_transforms(config.data_loader.eval.map.operations)
    data_set = build_dataset(config.data_loader.eval.dataset)
    data_set.transform = transforms
    dataset_eval = data_set.run()
    Validator.check_int(dataset_eval.get_dataset_size(), 0, Rel.GT)

    # set network
    network = build_model(config.model)

    # set loss
    network_loss = build_loss(config.loss)

    # load pretrain model
    param_dict = load_checkpoint(config.eval.pretrained_model)
    load_param_into_net(network, param_dict)

    # Define eval_metrics.
    eval_metrics = {'Top_1_Accuracy': nn.Top1CategoricalAccuracy(),
                    'Top_5_Accuracy': nn.Top5CategoricalAccuracy()}
    # init the whole Model
    model = Model(network,
                  network_loss,
                  metrics={"Accuracy": Accuracy()})

    # Begin to eval.
    result = model.eval(dataset_eval,
                        callbacks=[EvalLossMonitor(model)])

    return result


if __name__ == '__main__':
    args = parse_args()
    result = eval(args)
    print(result)