diff --git a/examples/reliability/concept_drift_check_images_lenet.py b/examples/reliability/concept_drift_check_images_lenet.py
index 5d77c10..1bdf3d5 100644
--- a/examples/reliability/concept_drift_check_images_lenet.py
+++ b/examples/reliability/concept_drift_check_images_lenet.py
@@ -17,9 +17,8 @@ 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.summary.summary_record import _get_summary_tensor_data
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindarmour.reliability.concept_drift.concept_drift_check_images import OodDetector, result_eval
+from mindarmour.reliability.concept_drift.concept_drift_check_images import OodDetectorFeatureCluster
 
 
 """
@@ -27,23 +26,6 @@ Examples for Lenet.
 """
 
 
-def feature_extract(data, feature_model, layer='output[:Tensor]'):
-    """
-    Extract features.
-    Args:
-        data (numpy.ndarray): Input data.
-        feature_model (Model): The model for extracting features.
-        layer (str): The feature layer. The layer name could be 'output[:Tensor]',
-                    '1[:Tensor]', '2[:Tensor]',...'10[:Tensor]'.
-
-    Returns:
-        numpy.ndarray, the feature of input data.
-    """
-    feature_model.predict(Tensor(data))
-    layer_out = _get_summary_tensor_data()
-    return layer_out[layer].asnumpy()
-
-
 if __name__ == '__main__':
     # load model
     ckpt_path = '../../tests/ut/python/dataset/trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
@@ -53,13 +35,13 @@ if __name__ == '__main__':
     model = Model(net)
     # load data
     ds_train = np.load('../../tests/ut/python/dataset/concept_train_lenet.npy')
-    ds_test = np.load('../../tests/ut/python/dataset/concept_test_lenet.npy')
-    ds_train = feature_extract(ds_train, model, layer='output[:Tensor]')
-    ds_test = feature_extract(ds_test, model, layer='output[:Tensor]')
-    # ood detect
-    detector = OodDetector(ds_train, ds_test, n_cluster=10)
-    score = detector.ood_detector()
-    # Evaluation
-    num = int(len(ds_test)/2)
+    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
-    dec_acc = result_eval(score, label, threshold=0.5)
+    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)
diff --git a/examples/reliability/concept_drift_check_images_resnet.py b/examples/reliability/concept_drift_check_images_resnet.py
index f97045b..5457e14 100644
--- a/examples/reliability/concept_drift_check_images_resnet.py
+++ b/examples/reliability/concept_drift_check_images_resnet.py
@@ -17,9 +17,8 @@ from mindspore import Tensor
 from mindspore.train.model import Model
 from mindspore import Model, nn, context
 from examples.common.networks.resnet.resnet import resnet50
-from mindspore.train.summary.summary_record import _get_summary_tensor_data
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindarmour.reliability.concept_drift.concept_drift_check_images import OodDetector, result_eval
+from mindarmour.reliability.concept_drift.concept_drift_check_images import OodDetectorFeatureCluster
 
 
 """
@@ -27,23 +26,6 @@ Examples for Resnet.
 """
 
 
-def feature_extract(data, feature_model, layer='output[:Tensor]'):
-    """
-    Extract features.
-    Args:
-        data (numpy.ndarray): Input data.
-        feature_model (Model): The model for extracting features.
-        layer (str): The feature layer. The layer name could be 'output[:Tensor]',
-                    '1[:Tensor]', '2[:Tensor]',...'10[:Tensor]'.
-
-    Returns:
-        numpy.ndarray, the feature of input data.
-    """
-    feature_model.predict(Tensor(data))
-    layer_out = _get_summary_tensor_data()
-    return layer_out[layer].asnumpy()
-
-
 if __name__ == '__main__':
     # load model
     ckpt_path = '../../tests/ut/python/dataset/trained_ckpt_file/resnet_1-20_1875.ckpt'
@@ -52,14 +34,14 @@ if __name__ == '__main__':
     load_param_into_net(net, load_dict)
     model = Model(net)
     # load data
-    ds_train = np.load('./train.npy')
-    ds_test = np.load('./test.npy')
-    ds_train = feature_extract(ds_train, model, layer='output[:Tensor]')
-    ds_test = feature_extract(ds_test, model, layer='output[:Tensor]')
-    # ood detect
-    detector = OodDetector(ds_train, ds_test, n_cluster=10)
-    score = detector.ood_detector()
-    # Evaluation
-    num = int(len(ds_test)/2)
+    ds_train = np.load('train.npy')
+    ds_eval = np.load('test1.npy')
+    ds_test = np.load('test2.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
-    dec_acc = result_eval(score, label, threshold=0.5)
+    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)
diff --git a/mindarmour/reliability/concept_drift/README.md b/mindarmour/reliability/concept_drift/README.md
index f9e7300..f80239b 100644
--- a/mindarmour/reliability/concept_drift/README.md
+++ b/mindarmour/reliability/concept_drift/README.md
@@ -109,8 +109,8 @@ For ResNet, the training data is Cifar10 as ID data. The testing data is Cifar10
  
 ### Environment Requirements
  
-- Hardware(Ascend)
-    - Prepare hardware environment with Ascend.
+- Hardware
+    - Prepare hardware environment with Ascend, CPU and GPU.
 - Framework
     - MindSpore
 - For more information, please check the resources below：
@@ -122,76 +122,176 @@ For ResNet, the training data is Cifar10 as ID data. The testing data is Cifar10
 #### Import
 
 ```python
-import logging
-import pytest
 import numpy as np
 from mindspore import Tensor
 from mindspore.train.model import Model
-from mindarmour.utils.logger import LogUtil
 from mindspore import Model, nn, context
 from examples.common.networks.lenet5.lenet5_net_for_fuzzing import LeNet5
-from mindspore.train.summary.summary_record import _get_summary_tensor_data
-from mindspore.train.serializaton import load_checkpoint, load_pram_into_net
-from mindarmour.reliability.concept_drift.concept_drift_check_images import OodDetector, result_eval
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindarmour.reliability.concept_drift.concept_drift_check_images import OodDetectorFeatureCluster
 ```
 
 #### Load Classification Model
 
+For convenience, we use a pre-trained model file `checkpoint_lenet-10_1875.ckpt` 
+in 'mindarmour/tests/ut/python/dataset/trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'. 
+
 ```python
-ckpt_path = '../../dataset/trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
+ckpt_path = 'checkpoint_lenet-10_1875.ckpt'
 net = LeNet5()
 load_dict = load_checkpoint(ckpt_path)
-load_pram_into_net(net, load_dict)
+load_param_into_net(net, load_dict)
 model = Model(net)
 ```
 
->`ckpt_path(str)`:the model path.  
+>`ckpt_path(str)`: the model path.  
 
-#### Data processing
 
-We extract the data features by the Lenet network.
+We can also use self-constructed model.  
+It is important that we need to name the model layer, and get the layer outputs.  
+Take LeNet as an example.
+Firstly, we import `TensorSummary` module.   
+Secondly, we initialize it as `self.summary = TensorSummary()`.  
+Finally, we add `self.summary('name', x)` after each layer we pay attention to. Here,  `name` of each layer is given by users.  
+After the above process, we can train the model and load it.  
 
 
 ```python
+from mindspore import nn
+from mindspore.common.initializer import TruncatedNormal
+from mindspore.ops import TensorSummary
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
+    """Wrap conv."""
+    weight = weight_variable()
+    return nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding,
+                     weight_init=weight, has_bias=False, pad_mode="valid")
+
+def fc_with_initialize(input_channels, out_channels):
+    """Wrap initialize method of full connection layer."""
+    weight = weight_variable()
+    bias = weight_variable()
+    return nn.Dense(input_channels, out_channels, weight, bias)
+
+def weight_variable():
+    """Wrap initialize variable."""
+    return TruncatedNormal(0.05)
+
+class LeNet5(nn.Cell):
+    """
+    Lenet network
+    """
+    def __init__(self):
+        super(LeNet5, self).__init__()
+        self.conv1 = conv(1, 6, 5)
+        self.conv2 = conv(6, 16, 5)
+        self.fc1 = fc_with_initialize(16*5*5, 120)
+        self.fc2 = fc_with_initialize(120, 84)
+        self.fc3 = fc_with_initialize(84, 10)
+        self.relu = nn.ReLU()
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flatten = nn.Flatten()
+        self.summary = TensorSummary()
+
+    def construct(self, x):
+        """
+        construct the network architecture
+        Returns:
+            x (tensor): network output
+        """
+        x = self.conv1(x)
+        self.summary('1', x)
+
+        x = self.relu(x)
+        self.summary('2', x)
+
+        x = self.max_pool2d(x)
+        self.summary('3', x)
+
+        x = self.conv2(x)
+        self.summary('4', x)
+
+        x = self.relu(x)
+        self.summary('5', x)
+
+        x = self.max_pool2d(x)
+        self.summary('6', x)
+
+        x = self.flatten(x)
+        self.summary('7', x)
+
+        x = self.fc1(x)
+        self.summary('8', x)
+
+        x = self.relu(x)
+        self.summary('9', x)
+
+        x = self.fc2(x)
+        self.summary('10', x)
+
+        x = self.relu(x)
+        self.summary('11', x)
+
+        x = self.fc3(x)
+        self.summary('output', x)
+        return x
+
+```
+#### Load Data
+
+We prepare three datasets. The training dataset, that is the same as the dataset to train the Lenet. Two testing datasets, the first testing dataset is with OOD label(0 for non-ood, and 1 for ood) for finding an optimal threshold for ood detection.
+The second testing dataset is for ood validation. The first testing dataset is not necessary if we would like to set threshold by ourselves
+
+```python
 ds_train = np.load('../../dataset/concept_train_lenet.npy')
-ds_test = np.load('../../dataset/concept_test_lenet.npy')
-ds_train = feature_extract(ds_train, model, layer='9[:Tensor]')
-ds_test = feature_extract(ds_test, model, layer='9[:Tensor]')
+ds_eval = np.load('../../dataset/concept_test_lenet1.npy')
+ds_test = np.load('../../dataset/concept_test_lenet2.npy')
 ```
 
 > `ds_train(numpy.ndarray)`: the train data.  
-> `ds_test(numpy.ndarray)`: the test data.  
-> `model(Model)`: the Lenet model.  
+> `ds_eval(numpy.ndarray)`: the data for finding an optimal threshold. This dataset is not necessary.  
+> `ds_test(numpy.ndarray)`: the test data for ood detection.  
 
 
-#### Train the concept drift detector
+#### OOD detector initialization
 
 OOD detector for Lenet.
 
 
 ```python
-detector = OodDetector(ds_train, ds_test, n_cluster=10)
-score = detector.ood_detector()
+# ood detector initialization
+detector = OodDetectorFeatureCluster(model, ds_train, n_cluster=10, layer='output[:Tensor]')
 ```
-
-> `ds_train(numpy.ndarray)`: the train data.  
-> `ds_test(numpy.ndarray)`: the test data.  
+> `model(Model)`: the model trained by the `ds_train`.  
+> `ds_train(numpy.ndarray)`: the training data.  
 > `n_cluster(int)`: the feature cluster number.  
+> `layer(str)`: the name of the feature extraction layer.
+
+In our example, we input the layer name `output[:Tensor]`, which can also be`9[:Tensor]`, `10[:Tensor]`, `11[:Tensor]` for LeNet. 
 
 
-#### Evaluation
+#### Optimal Threshold
+
+This step is optional. If we have a labeled dataset, named `ds_eval`, we can use the following code to find the optimal detection threshold.
 
 ```python
-num = int(len(ds_test)/2)
+# 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
-dec_acc = result_eval(score, label, threshold=0.5)
+optimal_threshold = detector.get_optimal_threshold(label, ds_eval)
 ```
 
-> `ds_test(numpy.ndarray)`: the test data.  
-> `score(numpy.ndarray)`: the concept drift score.  
-> `label(numpy.ndarray)`: the drift label.  
-> `threshold(float)`: the threshold to judge out-of-distribution.
+> `ds_eval(numpy.ndarray)`: the data for finding an optimal threshold.  
+> `label(numpy.ndarray)`: the ood label of ds_eval. 0 means non-ood data, and 1 means ood data.
+
+#### Detection result
+
+```python
+result = detector.ood_predict(optimal_threshold, ds_test)
+```
 
+> `ds_test(numpy.ndarray)`: the testing data for ood detection.  
+> `optimal_threshold(float)`: the optimal threshold to judge out-of-distribution data. We can also set the threshold value by ourselves.
 
 ## Script Description
 
diff --git a/mindarmour/reliability/concept_drift/concept_drift_check_images.py b/mindarmour/reliability/concept_drift/concept_drift_check_images.py
index 18f6436..10c3daa 100644
--- a/mindarmour/reliability/concept_drift/concept_drift_check_images.py
+++ b/mindarmour/reliability/concept_drift/concept_drift_check_images.py
@@ -16,12 +16,14 @@
 
 import heapq
 import numpy as np
+from mindspore import Tensor
 from sklearn.cluster import KMeans
 from mindarmour.utils._check_param import check_param_type, check_param_in_range
+from mindspore.train.summary.summary_record import _get_summary_tensor_data
+
 
 """
 Out-of-Distribution detection for images.
-The sample can be run on Ascend 910 AI processor.
 """
 
 
@@ -30,58 +32,166 @@ class OodDetector:
     Train the OOD detector.
 
     Args:
+        model (Model):The training model.
         ds_train (numpy.ndarray): The training dataset.
-        ds_test (numpy.ndarray): The testing dataset.
     """
+    def __init__(self, model, ds_train):
+        self.model = model
+        self.ds_train = check_param_type('ds_train', ds_train, np.ndarray)
+
+    def _feature_extract(self, model, data, layer='output[:Tensor]'):
+        """
+        Extract features.
+        Args:
+            model (Model): The model for extracting features.
+            data (numpy.ndarray): Input data.
+            layer (str): The name of the feature layer. layer (str) is represented as
+                'name[:Tensor]', where 'name' is given by users when training the model.
+                Please see more details about how to name the model layer in 'README.md'.
+
+        Returns:
+            numpy.ndarray, the data feature extracted by a certain neural layer.
+        """
+        model.predict(Tensor(data))
+        layer_out = _get_summary_tensor_data()
+        return layer_out[layer].asnumpy()
+
+    def get_optimal_threshold(self, label, ds_eval):
+        """
+        Get the optimal threshold.
 
-    def __init__(self, ds_train, ds_test, n_cluster=10):
+        Args:
+            label (numpy.ndarray): The label whether an image is in-distribution and out-of-distribution.
+            ds_eval (numpy.ndarray): The testing dataset to help find the threshold.
+
+        Returns:
+            - float, the optimal threshold.
+        """
+        pass
+
+    def ood_predict(self, threshold, ds_test):
+        """
+        The out-of-distribution detection.
+        Args:
+            threshold (float): the threshold to judge ood data. One can set value by experience
+                or use function get_optimal_threshold.
+            ds_test (numpy.ndarray): The testing dataset.
+
+        Returns:
+           - numpy.ndarray, the detection result. 0 means the data is not ood, 1 means the data is ood.
+        """
+        pass
+
+
+class OodDetectorFeatureCluster(OodDetector):
+    """
+    Train the OOD detector. Extract the training data features, and obtain the clustering centers. The distance between
+    the testing data features and the clustering centers determines whether an image is an out-of-distribution(OOD)
+    image or not.
+
+    Args:
+        model (Model):The training model.
+        ds_train (numpy.ndarray): The training dataset.
+        n_cluster (int): The cluster number. Belonging to [2,100].
+            Usually, n_cluster equals to the class number of the training dataset.
+            If the OOD detector performs poor in the testing dataset, we can increase the value of n_cluster
+            appropriately.
+        layer (str): The name of the feature layer. layer (str) is represented by
+            'name[:Tensor]', where 'name' is given by users when training the model.
+            Please see more details about how to name the model layer in 'README.md'.
+    """
+
+    def __init__(self, model, ds_train, n_cluster, layer):
+        self.model = model
         self.ds_train = check_param_type('ds_train', ds_train, np.ndarray)
-        self.ds_test = check_param_type('ds_test', ds_test, np.ndarray)
         self.n_cluster = check_param_type('n_cluster', n_cluster, int)
         self.n_cluster = check_param_in_range('n_cluster', n_cluster, 2, 100)
+        self.layer = check_param_type('layer', layer, str)
+        self.feature = self._feature_extract(model, ds_train, layer=self.layer)
 
-    def ood_detector(self):
+    def _feature_cluster(self):
         """
-        The out-of-distribution detection.
+        Get the feature cluster.
 
         Returns:
-           - numpy.ndarray, the detection score of images.
+            - numpy.ndarray, the feature cluster.
         """
-
         clf = KMeans(n_clusters=self.n_cluster)
-        clf.fit_predict(self.ds_train)
+        clf.fit_predict(self.feature)
         feature_cluster = clf.cluster_centers_
+        return feature_cluster
+
+    def _get_ood_score(self, ds_test):
+        """
+        Get the ood score.
+
+        Args:
+            ds_test (numpy.ndarray): The testing dataset.
+
+        Returns:
+            - float, the optimal threshold.
+        """
+        feature_cluster = self._feature_cluster()
+        ds_test = self._feature_extract(self.model, ds_test, layer=self.layer)
         score = []
-        for i in range(len(self.ds_test)):
+        for i in range(len(ds_test)):
             dis = []
             for j in range(len(feature_cluster)):
-                loc = list(map(list(feature_cluster[j]).index, heapq.nlargest(self.n_cluster, list(feature_cluster[j]))))
-                diff = sum(abs((feature_cluster[j][loc] - self.ds_test[i][loc]))) / sum(abs((feature_cluster[j][loc])))
+                loc = list(
+                    map(list(feature_cluster[j]).index, heapq.nlargest(self.n_cluster, list(feature_cluster[j]))))
+                diff = sum(abs((feature_cluster[j][loc] - ds_test[i][loc]))) / sum(abs((feature_cluster[j][loc])))
                 dis.append(diff)
             score.append(min(dis))
         score = np.array(score)
         return score
 
+    def get_optimal_threshold(self, label, ds_eval):
+        """
+        Get the optimal threshold.
 
-def result_eval(score, label, threshold):
-    """
-    Evaluate the detection results.
+        Args:
+            label (numpy.ndarray): The label whether an image is in-distribution and out-of-distribution.
+            ds_eval (numpy.ndarray): The testing dataset to help find the threshold.
 
-    Args:
-        score (numpy.ndarray): The detection score of images.
-        label (numpy.ndarray): The label whether an image is in-ditribution and out-of-distribution.
-        threshold (float): The threshold to judge out-of-distribution distance.
+        Returns:
+            - float, the optimal threshold.
+        """
+        check_param_type('label', label, np.ndarray)
+        check_param_type('ds_eval', ds_eval, np.ndarray)
+        score = self._get_ood_score(ds_eval)
+        acc = []
+        threshold = []
+        for threshold_change in np.arange(0.0, 1.0, 0.01):
+            count = 0
+            for i in range(len(score)):
+                if score[i] < threshold_change and label[i] == 0:
+                    count = count + 1
+                if score[i] >= threshold_change and label[i] == 1:
+                    count = count + 1
+            acc.append(count / len(score))
+            threshold.append(threshold_change)
+        acc = np.array(acc)
+        threshold = np.array(threshold)
+        optimal_threshold = threshold[np.where(acc==np.max(acc))[0]][0]
+        return optimal_threshold
 
-    Returns:
-        - float, the detection accuracy.
-    """
-    check_param_type('label', label, np.ndarray)
-    check_param_type('threshold', threshold, float)
-    check_param_in_range('threshold', threshold, 0, 1)
-    count = 0
-    for i in range(len(score)):
-        if score[i] < threshold and label[i] == 0:
-            count = count + 1
-        if score[i] >= threshold and label[i] == 1:
-            count = count + 1
-    return count / len(score)
+    def ood_predict(self, threshold, ds_test):
+        """
+        The out-of-distribution detection.
+        Args:
+            threshold (float): the threshold to judge ood data. One can set value by experience
+                or use function get_optimal_threshold.
+            ds_test (numpy.ndarray): The testing dataset.
+
+        Returns:
+           - numpy.ndarray, the detection result. 0 means the data is not ood, 1 means the data is ood.
+        """
+        score = self._get_ood_score(ds_test)
+        result = []
+        for i in range(len(score)):
+            if score[i] < threshold:
+                result.append(0)
+            if score[i] >= threshold:
+                result.append(1)
+        result = np.array(result)
+        return result
diff --git a/tests/ut/python/dataset/concept_test_lenet1.npy b/tests/ut/python/dataset/concept_test_lenet1.npy
new file mode 100644
index 0000000..ae87bcc
Binary files /dev/null and b/tests/ut/python/dataset/concept_test_lenet1.npy differ
diff --git a/tests/ut/python/dataset/concept_test_lenet2.npy b/tests/ut/python/dataset/concept_test_lenet2.npy
new file mode 100644
index 0000000..1fe447e
Binary files /dev/null and b/tests/ut/python/dataset/concept_test_lenet2.npy differ
diff --git a/tests/ut/python/reliability/concept_drift/test_concept_drift_images.py b/tests/ut/python/reliability/concept_drift/test_concept_drift_images.py
index ef25c79..21010b9 100644
--- a/tests/ut/python/reliability/concept_drift/test_concept_drift_images.py
+++ b/tests/ut/python/reliability/concept_drift/test_concept_drift_images.py
@@ -24,30 +24,13 @@ from mindspore.train.model import Model
 from mindarmour.utils.logger import LogUtil
 from mindspore import Model, nn, context
 from examples.common.networks.lenet5.lenet5_net_for_fuzzing import LeNet5
-from mindspore.train.summary.summary_record import _get_summary_tensor_data
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindarmour.reliability.concept_drift.concept_drift_check_images import OodDetector, result_eval
+from mindarmour.reliability.concept_drift.concept_drift_check_images import OodDetectorFeatureCluster
 
 LOGGER = LogUtil.get_instance()
 TAG = 'Concept_Test'
 
 
-def feature_extract(data, feature_model, layer='output[:Tensor]'):
-    """
-    Extract features.
-    Args:
-        data (numpy.ndarray): Input data.
-        feature_model (Model): The model for extracting features.
-        layer (str): The feature layer. The layer name could be 'output[:Tensor]',
-                    '1[:Tensor]', '2[:Tensor]',...'10[:Tensor]'.
-
-    Returns:
-        numpy.ndarray, the feature of input data.
-    """
-    feature_model.predict(Tensor(data))
-    layer_out = _get_summary_tensor_data()
-    return layer_out[layer].asnumpy()
-
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -66,20 +49,20 @@ def test_cp():
     model = Model(net)
     # load data
     ds_train = np.load('../../dataset/concept_train_lenet.npy')
-    ds_test = np.load('../../dataset/concept_test_lenet.npy')
-    ds_train = feature_extract(ds_train, model, layer='9[:Tensor]')
-    ds_test = feature_extract(ds_test, model, layer='9[:Tensor]')
-    # ood detect
-    detector = OodDetector(ds_train, ds_test, n_cluster=10)
-    score = detector.ood_detector()
-    # Evaluation
-    num = int(len(ds_test)/2)
+    ds_eval = np.load('../../dataset/concept_test_lenet1.npy')
+    ds_test = np.load('../../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
-    dec_acc = result_eval(score, label, threshold=0.5)
+    optimal_threshold = detector.get_optimal_threshold(label, ds_eval)
+    # get result of ds_test. We can also set threshold by ourselves.
+    result = detector.ood_predict(optimal_threshold, ds_test)
     # result log
     LOGGER.set_level(logging.DEBUG)
-    LOGGER.debug(TAG, '--start concept drift test--')
-    LOGGER.debug(score, '--concept drift check score--')
-    LOGGER.debug(dec_acc, '--concept drift check accuracy--')
-    LOGGER.debug(TAG, '--end concept drift test--')
-    assert np.any(score >= 0.0)
+    LOGGER.debug(TAG, '--start ood test--')
+    LOGGER.debug(result, '--ood result--')
+    LOGGER.debug(optimal_threshold, '--the optimal threshold--')
+    LOGGER.debug(TAG, '--end ood test--')
+    assert np.any(result >= 0.0)