forked from mindspore-Ecosystem/mindspore
51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
# 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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eval.
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"""
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from mindspore import nn
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from mindspore.train.model import Model
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from mindspore.common import dtype as mstype
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from src.dataset import create_dataset
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from src.config import set_config
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from src.args import eval_parse_args
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from src.models import define_net, load_ckpt
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from src.utils import switch_precision, set_context
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if __name__ == '__main__':
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args_opt = eval_parse_args()
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config = set_config(args_opt)
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set_context(config)
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backbone_net, head_net, net = define_net(config, args_opt.is_training)
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load_ckpt(net, args_opt.pretrain_ckpt)
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switch_precision(net, mstype.float16, config)
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dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, config=config)
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step_size = dataset.get_dataset_size()
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if step_size == 0:
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raise ValueError("The step_size of dataset is zero. Check if the images count of eval dataset is more \
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than batch_size in config.py")
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net.set_train(False)
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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model = Model(net, loss_fn=loss, metrics={'acc'})
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res = model.eval(dataset)
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print(f"result:{res}\npretrain_ckpt={args_opt.pretrain_ckpt}")
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