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66 lines
2.4 KiB
66 lines
2.4 KiB
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""eval resnet."""
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import argparse
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from mindspore import context
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from mindspore.common import set_seed
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.CrossEntropySmooth import CrossEntropySmooth
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from src.resnet import resnet152 as resnet
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from src.config import config5 as config
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from src.dataset import create_dataset2 as create_dataset
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
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parser.add_argument('--data_url', type=str, default=None, help='Dataset path')
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args_opt = parser.parse_args()
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set_seed(1)
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if __name__ == '__main__':
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target = "Ascend"
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# init context
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context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
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# create dataset
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local_data_path = args_opt.data_url
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print('Download data.')
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dataset = create_dataset(dataset_path=local_data_path, do_train=False, batch_size=config.batch_size,
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target=target)
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step_size = dataset.get_dataset_size()
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# define net
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net = resnet(class_num=config.class_num)
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ckpt_name = args_opt.checkpoint_path
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param_dict = load_checkpoint(ckpt_name)
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load_param_into_net(net, param_dict)
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net.set_train(False)
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# define loss, model
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if not config.use_label_smooth:
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config.label_smooth_factor = 0.0
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loss = CrossEntropySmooth(sparse=True, reduction='mean',
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smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
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# define model
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model = Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
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# eval model
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res = model.eval(dataset)
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print("result:", res, "ckpt=", ckpt_name)
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