!4881 Fix param check

Merge pull request !4881 from byweng/fix_param_check

mindspore/nn/probability/bnn_layers/bnn_cell_wrapper.py

@@ -65,9 +65,9 @@ class WithBNNLossCell:
     """
 
     def __init__(self, backbone, loss_fn, dnn_factor=1, bnn_factor=1):
-        if not isinstance(dnn_factor, (int, float)):
+        if isinstance(dnn_factor, bool) or not isinstance(dnn_factor, (int, float)):
             raise TypeError('The type of `dnn_factor` should be `int` or `float`')
-        if not isinstance(bnn_factor, (int, float)):
+        if isinstance(bnn_factor, bool) or not isinstance(bnn_factor, (int, float)):
             raise TypeError('The type of `bnn_factor` should be `int` or `float`')
 
         self.backbone = backbone

mindspore/nn/probability/bnn_layers/conv_variational.py

@@ -173,8 +173,7 @@ class ConvReparam(_ConvVariational):
     r"""
     Convolutional variational layers with Reparameterization.
 
-    See more details in paper `Auto-Encoding Variational Bayes
-    <https://arxiv.org/abs/1312.6114>`
+    See more details in paper `Auto-Encoding Variational Bayes <https://arxiv.org/abs/1312.6114>`_.
 
     Args:
         in_channels (int): The number of input channel :math:`C_{in}`.

mindspore/nn/probability/bnn_layers/dense_variational.py

@@ -132,8 +132,7 @@ class DenseReparam(_DenseVariational):
     r"""
     Dense variational layers with Reparameterization.
 
-    See more details in paper `Auto-Encoding Variational Bayes
-    <https://arxiv.org/abs/1312.6114>`
+    See more details in paper `Auto-Encoding Variational Bayes <https://arxiv.org/abs/1312.6114>`_.
 
     Applies dense-connected layer for the input. This layer implements the operation as:
 

mindspore/nn/probability/bnn_layers/layer_distribution.py

@@ -78,16 +78,17 @@ class NormalPosterior(Cell):
         if not isinstance(shape, (tuple, list)):
             raise TypeError('The type of `shape` should be `tuple` or `list`')
 
-        if not isinstance(loc_mean, (int, float)):
+        if isinstance(loc_mean, bool) or not isinstance(loc_mean, (int, float)):
             raise TypeError('The type of `loc_mean` should be `int` or `float`')
 
-        if not isinstance(untransformed_scale_mean, (int, float)):
+        if isinstance(untransformed_scale_mean, bool) or not isinstance(untransformed_scale_mean, (int, float)):
             raise TypeError('The type of `untransformed_scale_mean` should be `int` or `float`')
 
-        if not (isinstance(loc_std, (int, float)) and loc_std >= 0):
+        if isinstance(loc_std, bool) or not (isinstance(loc_std, (int, float)) and loc_std >= 0):
             raise TypeError('The type of `loc_std` should be `int` or `float` and its value should > 0')
 
-        if not (isinstance(untransformed_scale_std, (int, float)) and untransformed_scale_std >= 0):
+        if isinstance(loc_std, bool) or not (isinstance(untransformed_scale_std, (int, float)) and
+                                             untransformed_scale_std >= 0):
             raise TypeError('The type of `untransformed_scale_std` should be `int` or `float` and '
                             'its value should > 0')
 

mindspore/nn/probability/transforms/transform_bnn.py

@@ -61,9 +61,9 @@ class TransformToBNN:
     """
 
     def __init__(self, trainable_dnn, dnn_factor=1, bnn_factor=1):
-        if not isinstance(dnn_factor, (int, float)):
+        if isinstance(dnn_factor, bool) or not isinstance(dnn_factor, (int, float)):
             raise TypeError('The type of `dnn_factor` should be `int` or `float`')
-        if not isinstance(bnn_factor, (int, float)):
+        if isinstance(bnn_factor, bool) or not isinstance(bnn_factor, (int, float)):
             raise TypeError('The type of `bnn_factor` should be `int` or `float`')
 
         net_with_loss = trainable_dnn.network