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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 dataset import create_dataset
- from config import config
- from mindspore import context
- from mindspore.model_zoo.mobilenet import mobilenet_v2
- from mindspore.train.model import Model
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
-
- parser = argparse.ArgumentParser(description='Image classification')
- 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", device_id=device_id, save_graphs=False)
- context.set_context(enable_task_sink=True)
- context.set_context(enable_loop_sink=True)
- context.set_context(enable_mem_reuse=True)
-
- if __name__ == '__main__':
- context.set_context(enable_hccl=False)
-
- loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
- net = mobilenet_v2()
-
- 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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