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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.
- # ============================================================================
- import os
- from mindspore.train.callback import Callback
- from mindspore.train.serialization import save_checkpoint
-
-
- class SaveCallback(Callback):
- """
- Evaluating on eval_dataset after each epoch.
- And it will save the parameters if the accuracy is better.
- """
-
- def __init__(self, model, eval_dataset, ckpt_path):
- super(SaveCallback, self).__init__()
- self.model = model
- self.eval_dataset = eval_dataset
- self.acc = 0.2
- self.ckpt_path = ckpt_path
-
- def step_end(self, run_context):
- cb_params = run_context.original_args()
- epoch_num = cb_params.cur_epoch_num
- result = self.model.eval(self.eval_dataset)
- print("epoch", epoch_num, " top_1_accuracy:", result['top_1_accuracy'])
- if result['top_1_accuracy'] > self.acc:
- self.acc = result['top_1_accuracy']
- file_name = "max.ckpt"
- file_name = os.path.join(self.ckpt_path, file_name)
- save_checkpoint(save_obj=cb_params.train_network, ckpt_file_name=file_name)
- print("Save the maximum accuracy checkpoint,the accuracy is", self.acc)
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