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- # Copyright 2020 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 lenet example ########################
- eval lenet according to model file:
- python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
- """
-
- # python eval.py --device_target=CPU --ckpt_path=./ckpt/checkpoint_lenet-1_1875.ckpt > log_eval.txt 2>&1
- # python eval.py --device_target=CPU --ckpt_path=./ckpt/checkpoint_lenet-10_1875.ckpt > log_eval2.txt 2>&1
-
- import os
- import argparse
- import mindspore.nn as nn
- from mindspore import context
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore.train import Model
- from mindspore.nn.metrics import Accuracy
- from dataset import create_dataset
- from config import mnist_cfg as cfg
- from lenet import LeNet5
-
- if __name__ == "__main__":
- parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
- parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
- help='device where the code will be implemented (default: Ascend)')
- parser.add_argument('--data_path', type=str, default="./Data",
- help='path where the dataset is saved')
- parser.add_argument('--ckpt_path', type=str, default="", help='if mode is test, must provide\
- path where the trained ckpt file')
-
- args = parser.parse_args()
-
- context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
-
- network = LeNet5(cfg.num_classes)
- net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
- repeat_size = cfg.epoch_size
- net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
- model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
-
- print("============== Starting Testing ==============")
- param_dict = load_checkpoint(args.ckpt_path)
- load_param_into_net(network, param_dict)
- ds_eval = create_dataset(os.path.join(args.data_path, "test"),
- cfg.batch_size,
- 1)
- if ds_eval.get_dataset_size() == 0:
- raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
-
- acc = model.eval(ds_eval)
- print("============== {} ==============".format(acc))
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