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- # Copyright 2022 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.
- # ============================================================================
- """Inference Interface"""
- import sys
- import argparse
- import logging
-
- from mindspore import context
- from mindspore.nn import Loss, Top1CategoricalAccuracy, Top5CategoricalAccuracy
- from mindspore.train.model import Model
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
- from src.loss import LabelSmoothingCrossEntropy
- from src.dataset import create_dataset_cifar10
- from src.resnet import resnet20
-
- from easydict import EasyDict as edict
-
- root = logging.getLogger()
- root.setLevel(logging.DEBUG)
-
- parser = argparse.ArgumentParser(description='Evaluation')
- parser.add_argument('--data_path', type=str, default='/data/',
- metavar='DIR', help='path to dataset')
- parser.add_argument('--num-classes', type=int, default=10, metavar='N',
- help='number of label classes (default: 10)')
- parser.add_argument('-b', '--batch-size', type=int, default=32, metavar='N',
- help='input batch size for training (default: 32)')
- parser.add_argument('--smoothing', type=float, default=0.1,
- help='label smoothing (default: 0.1)')
- parser.add_argument('--platform', type=str, default='CPU',
- help='platform to run')
- parser.add_argument('--ckpt', type=str, default='./fdabnn.ckpt',
- help='model checkpoint to load')
- parser.add_argument('--image_size', type=int, default=32,
- help='input image size')
-
- def main():
- """Main entrance for training"""
- args = parser.parse_args()
- print(sys.argv)
-
- context.set_context(mode=context.PYNATIVE_MODE, device_target=args.platform, save_graphs=False)
-
- net = resnet20()
- cfg = edict({
- 'image_height': args.image_size,
- 'image_width': args.image_size,
- })
- cfg.batch_size = args.batch_size
- val_data_url = args.data_path
- val_dataset = create_dataset_cifar10(val_data_url, repeat_num=1, training=False, cifar_cfg=cfg)
- loss = LabelSmoothingCrossEntropy(smooth_factor=args.smoothing,
- num_classes=args.num_classes)
-
- loss.add_flags_recursive(fp32=True, fp16=False)
- eval_metrics = {'Validation-Loss': Loss(),
- 'Top1-Acc': Top1CategoricalAccuracy(),
- 'Top5-Acc': Top5CategoricalAccuracy()}
- ckpt = load_checkpoint(args.ckpt)
-
- load_param_into_net(net, ckpt)
-
- net.set_train(False)
-
- model = Model(net, loss, metrics=eval_metrics)
- metrics = model.eval(val_dataset, dataset_sink_mode=False)
- print(metrics)
-
- if __name__ == '__main__':
- main()
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