forked from mindspore-Ecosystem/mindspore
54 lines
2.6 KiB
Python
54 lines
2.6 KiB
Python
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# Copyright 2020 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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"""
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##############test vgg16 example on cifar10#################
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python eval.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
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"""
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import argparse
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import mindspore.nn as nn
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from mindspore.nn.optim.momentum import Momentum
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from mindspore.train.model import Model
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from mindspore import context
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.model_zoo.vgg import vgg16
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from config import cifar_cfg as cfg
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import dataset
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Cifar10 classification')
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parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
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help='device where the code will be implemented. (Default: Ascend)')
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parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved')
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parser.add_argument('--checkpoint_path', type=str, default=None, help='checkpoint file path.')
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parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
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context.set_context(device_id=args_opt.device_id)
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context.set_context(enable_mem_reuse=True, enable_hccl=False)
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net = vgg16(batch_size=cfg.batch_size, num_classes=cfg.num_classes)
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opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
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weight_decay=cfg.weight_decay)
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
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model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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load_param_into_net(net, param_dict)
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net.set_train(False)
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dataset = dataset.create_dataset(args_opt.data_path, 1, False)
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res = model.eval(dataset)
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print("result: ", res)
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