forked from mindspore-Ecosystem/mindspore
60 lines
2.5 KiB
Python
60 lines
2.5 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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"""evaluate_imagenet"""
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import argparse
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import os
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import mindspore.nn as nn
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from mindspore import context
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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 mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from src.dataset import create_dataset
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from src.inceptionv4 import Inceptionv4
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from src.config import config_ascend as config
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def parse_args():
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'''parse_args'''
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parser = argparse.ArgumentParser(description='image classification evaluation')
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parser.add_argument('--platform', type=str, default='Ascend', choices=('Ascend', 'GPU'), help='run platform')
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parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
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parser.add_argument('--checkpoint_path', type=str, default='', help='checkpoint of inceptionV4')
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args_opt = parser.parse_args()
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return args_opt
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if __name__ == '__main__':
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args = parse_args()
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if args.platform == 'Ascend':
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(device_id=device_id)
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context.set_context(mode=context.GRAPH_MODE, device_target=args.platform)
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net = Inceptionv4(classes=config.num_classes)
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ckpt = load_checkpoint(args.checkpoint_path)
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load_param_into_net(net, ckpt)
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net.set_train(False)
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dataset = create_dataset(dataset_path=args.dataset_path, do_train=False,
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repeat_num=1, batch_size=config.batch_size)
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loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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eval_metrics = {'Loss': nn.Loss(),
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'Top1-Acc': nn.Top1CategoricalAccuracy(),
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'Top5-Acc': nn.Top5CategoricalAccuracy()}
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model = Model(net, loss, optimizer=None, metrics=eval_metrics)
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print('='*20, 'Evalute start', '='*20)
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metrics = model.eval(dataset)
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print("metric: ", metrics)
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