mindspore/model_zoo/vgg16/eval.py

# 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.
# ============================================================================
"""
##############test vgg16 example on cifar10#################
python eval.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
"""
import argparse

import mindspore.nn as nn
from mindspore import context
from mindspore.nn.optim.momentum import Momentum
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from src.config import cifar_cfg as cfg
from src.dataset import vgg_create_dataset
from src.vgg import vgg16

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('--checkpoint_path', type=str, default=None, help='checkpoint file path.')
    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)

    net = vgg16(num_classes=cfg.num_classes)
    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 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'})

    param_dict = load_checkpoint(args_opt.checkpoint_path)
    load_param_into_net(net, param_dict)
    net.set_train(False)
    dataset = vgg_create_dataset(args_opt.data_path, 1, False)
    res = model.eval(dataset)
    print("result: ", res)