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- # 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.
- # ============================================================================
- """
- eval.
- """
- import os
- import argparse
-
- from mindspore import context
- from mindspore.train.model import Model
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
- from src.dataset_imagenet import create_dataset
- from src.config import config
- from src.crossentropy import CrossEntropy
- from src.resnet50 import resnet50
-
- parser = argparse.ArgumentParser(description='Image classification')
- parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
- parser.add_argument('--device_num', type=int, default=1, help='Device num.')
- parser.add_argument('--do_train', type=bool, default=False, help='Do train or not.')
- parser.add_argument('--do_eval', type=bool, default=True, help='Do eval or not.')
- parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
- parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
- args_opt = parser.parse_args()
-
- device_id = int(os.getenv('DEVICE_ID'))
-
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
- context.set_context(device_id=device_id)
-
- if __name__ == '__main__':
-
- net = resnet50(class_num=config.class_num)
- if not config.label_smooth:
- config.label_smooth_factor = 0.0
- loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
-
- if args_opt.do_eval:
- dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size)
- step_size = dataset.get_dataset_size()
-
- if args_opt.checkpoint_path:
- param_dict = load_checkpoint(args_opt.checkpoint_path)
- load_param_into_net(net, param_dict)
- net.set_train(False)
-
- model = Model(net, loss_fn=loss, metrics={'acc'})
- res = model.eval(dataset)
- print("result:", res, "ckpt=", args_opt.checkpoint_path)
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