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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.
-
- import numpy as np
- from mindspore import Tensor
- from mindspore.train.model import Model
- from mindspore import Model, nn, context
- from examples.common.networks.lenet5.lenet5_net_for_fuzzing import LeNet5
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindarmour.reliability.concept_drift.concept_drift_check_images import OodDetectorFeatureCluster
-
-
- """
- Examples for Lenet.
- """
-
-
- if __name__ == '__main__':
- # load model
- ckpt_path = '../../tests/ut/python/dataset/trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
- net = LeNet5()
- load_dict = load_checkpoint(ckpt_path)
- load_param_into_net(net, load_dict)
- model = Model(net)
- # load data
- ds_train = np.load('../../tests/ut/python/dataset/concept_train_lenet.npy')
- ds_eval = np.load('../../tests/ut/python/dataset/concept_test_lenet1.npy')
- ds_test = np.load('../../tests/ut/python/dataset/concept_test_lenet2.npy')
- # ood detector initialization
- detector = OodDetectorFeatureCluster(model, ds_train, n_cluster=10, layer='output[:Tensor]')
- # get optimal threshold with ds_eval
- num = int(len(ds_eval) / 2)
- label = np.concatenate((np.zeros(num), np.ones(num)), axis=0) # ID data = 0, OOD data = 1
- optimal_threshold = detector.get_optimal_threshold(label, ds_eval)
- # get result of ds_test2. We can also set threshold by ourselves.
- result = detector.ood_predict(optimal_threshold, ds_test)
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