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72 lines
3.0 KiB
72 lines
3.0 KiB
# 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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"""test ShuffleNetV1"""
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import argparse
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import time
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from mindspore import context, nn
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from mindspore.train.model import Model
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from mindspore.common import set_seed
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.shufflenetv1 import ShuffleNetV1 as shufflenetv1
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from src.config import config
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from src.dataset import create_dataset
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from src.crossentropysmooth import CrossEntropySmooth
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set_seed(1)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
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parser.add_argument('--device_id', type=int, default=0, help='Device id')
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parser.add_argument('--checkpoint_path', type=str, default='', help='Checkpoint file path')
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parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
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parser.add_argument('--model_size', type=str, default='2.0x', help='ShuffleNetV1 model size',
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choices=['2.0x', '1.5x', '1.0x', '0.5x'])
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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, save_graphs=False,
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device_id=args_opt.device_id)
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# create dataset
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dataset = create_dataset(args_opt.dataset_path, do_train=False, device_num=1, rank=0)
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step_size = dataset.get_dataset_size()
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# define net
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net = shufflenetv1(model_size=args_opt.model_size)
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# load checkpoint
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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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# define loss
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loss = CrossEntropySmooth(sparse=True, reduction="mean", smooth_factor=config.label_smooth_factor,
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num_classes=config.num_classes)
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# define model
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eval_metrics = {'Loss': nn.Loss(), 'Top_1_Acc': nn.Top1CategoricalAccuracy(),
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'Top_5_Acc': nn.Top5CategoricalAccuracy()}
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model = Model(net, loss_fn=loss, metrics=eval_metrics)
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# eval model
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start_time = time.time()
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res = model.eval(dataset, dataset_sink_mode=True)
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log = "result:" + str(res) + ", ckpt:'" + args_opt.checkpoint_path + "', time: " + str(
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(time.time() - start_time) * 1000)
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print(log)
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filename = './eval_log.txt'
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with open(filename, 'a') as file_object:
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file_object.write(log + '\n')
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