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- # Copyright 2021 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.
- # ============================================================================
- """eval net"""
- import argparse
- from mindspore import context
- from mindspore.common import set_seed
- from mindspore.train.model import Model
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from src.CrossEntropySmooth import CrossEntropySmooth
-
- parser = argparse.ArgumentParser(description='Image classification')
- parser.add_argument('--net', type=str, default="sknet50", help='Resnet Model, either sknet50')
- parser.add_argument('--dataset', type=str, default="cifar10", help='Dataset, either cifar10 or imagenet2012')
- parser.add_argument('--checkpoint_path', type=str, default="/path/to/sknet-90_195.ckpt", help='Checkpoint file path')
- parser.add_argument('--dataset_path', type=str, default="/path/to/cifar10", help='Dataset path')
- parser.add_argument('--device_target', type=str, default='Ascend',
- help="Device target, support Ascend")
- parser.add_argument('--device_id', type=int, default=0, help='Device num.')
- args_opt = parser.parse_args()
- set_seed(1)
-
-
- if __name__ == '__main__':
- if args_opt.net == "sknet50":
- from src.sknet50 import sknet50 as sknet
- if args_opt.dataset == "cifar10":
- from src.config import config1 as config
- from src.dataset import create_dataset1 as create_dataset
-
- target = args_opt.device_target
- # init context
- context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
- if target == "Ascend":
- device_id = int(args_opt.device_id)
- context.set_context(device_id=device_id)
-
- # create dataset
- dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size,
- target=target)
- step_size = dataset.get_dataset_size()
- print(step_size)
- # define net
- net = sknet(class_num=config.class_num)
-
- # load checkpoint
- param_dict = load_checkpoint(args_opt.checkpoint_path)
- load_param_into_net(net, param_dict)
- net.set_train(False)
- config.label_smooth_factor = 0.0
- loss = CrossEntropySmooth(sparse=True, reduction='mean',
- smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
-
- model = Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
-
- # eval model
- res = model.eval(dataset)
- print("result:", res, "ckpt=", args_opt.checkpoint_path)
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