include explainer in cmake and modify __init__ in explanation classes.

cmake/package.cmake

@@ -263,6 +263,7 @@ install(
         ${CMAKE_SOURCE_DIR}/mindspore/ops
         ${CMAKE_SOURCE_DIR}/mindspore/communication
         ${CMAKE_SOURCE_DIR}/mindspore/profiler
+        ${CMAKE_SOURCE_DIR}/mindspore/explainer
         ${CMAKE_SOURCE_DIR}/mindspore/compression
     DESTINATION ${INSTALL_PY_DIR}
     COMPONENT mindspore

mindspore/explainer/explanation/_attribution/_attribution.py

@@ -32,6 +32,8 @@ class Attribution:
     def __init__(self, network):
         self._verify_model(network)
         self._model = network
+        self._model.set_train(False)
+        self._model.set_grad(False)
 
     @staticmethod
     def _verify_model(model):

mindspore/explainer/explanation/_attribution/_backprop/gradcam.py

@@ -55,6 +55,11 @@ class GradCAM(IntermediateLayerAttribution):
         layer (str): The layer name to generate the explanation at. Default: ''.
             If default, the explantion will be generated at the input layer.
 
+    Notes:
+        The parsed `network` will be set to eval mode through `network.set_grad(False)` and `network.set_train(False)`.
+        If you want to train the `network` afterwards, please reset it back to training mode through the opposite
+        operations.
+
     Examples:
         >>> net = resnet50(10)
         >>> param_dict = load_checkpoint("resnet50.ckpt")

mindspore/explainer/explanation/_attribution/_backprop/gradient.py

@@ -64,6 +64,11 @@ class Gradient(Attribution):
     Args:
         network (Cell): The black-box model to be explained.
 
+    Notes:
+        The parsed `network` will be set to eval mode through `network.set_grad(False)` and `network.set_train(False)`.
+        If you want to train the `network` afterwards, please reset it back to training mode through the opposite
+        operations.
+
     Examples:
         >>> net = resnet50(10)
         >>> param_dict = load_checkpoint("resnet50.ckpt")

mindspore/explainer/explanation/_attribution/_backprop/modified_relu.py

@@ -69,6 +69,11 @@ class Deconvolution(ModifiedReLU):
     Args:
         network (Cell): The black-box model to be explained.
 
+    Notes:
+        The parsed `network` will be set to eval mode through `network.set_grad(False)` and `network.set_train(False)`.
+        If you want to train the `network` afterwards, please reset it back to training mode through the opposite
+        operations.
+
     Examples:
         >>> net = resnet50(10)
         >>> param_dict = load_checkpoint("resnet50.ckpt")
@@ -98,6 +103,11 @@ class GuidedBackprop(ModifiedReLU):
     Args:
         network (Cell): The black-box model to be explained.
 
+    Notes:
+        The parsed `network` will be set to eval mode through `network.set_grad(False)` and `network.set_train(False)`.
+        If you want to train the `network` afterwards, please reset it back to training mode through the opposite
+        operations.
+
     Examples:
         >>> net = resnet50(10)
         >>> param_dict = load_checkpoint("resnet50.ckpt")