!4734 fix error of bnn_layers

Merge pull request !4734 from byweng/add_test

mindspore/nn/probability/bnn_layers/conv_variational.py

@@ -61,6 +61,12 @@ class _ConvVariational(_Conv):
             raise ValueError('Attr \'pad_mode\' of \'Conv2d\' Op passed '
                              + str(pad_mode) + ', should be one of values in \'valid\', \'same\', \'pad\'.')
 
+        if not isinstance(stride, (int, tuple)):
+            raise TypeError('The type of `stride` should be `int` of `tuple`')
+
+        if not isinstance(dilation, (int, tuple)):
+            raise TypeError('The type of `dilation` should be `int` of `tuple`')
+
         # convolution args
         self.in_channels = in_channels
         self.out_channels = out_channels
@@ -87,13 +93,10 @@ class _ConvVariational(_Conv):
                 raise TypeError('The type of `weight_prior_fn` should be `NormalPrior`')
             self.weight_prior = weight_prior_fn()
 
-        if isinstance(weight_posterior_fn, Cell):
-            if weight_posterior_fn.__class__.__name__ != 'NormalPosterior':
-                raise TypeError('The type of `weight_posterior_fn` should be `NormalPosterior`')
-        else:
-            if weight_posterior_fn.__name__ != 'NormalPosterior':
-                raise TypeError('The type of `weight_posterior_fn` should be `NormalPosterior`')
-        self.weight_posterior = weight_posterior_fn(shape=self.shape, name='bnn_weight')
+        try:
+            self.weight_posterior = weight_posterior_fn(shape=self.shape, name='bnn_weight')
+        except TypeError:
+            raise TypeError('The type of `weight_posterior_fn` should be `NormalPosterior`')
 
         if self.has_bias:
             self.bias.requires_grad = False
@@ -107,13 +110,10 @@ class _ConvVariational(_Conv):
                     raise TypeError('The type of `bias_prior_fn` should be `NormalPrior`')
                 self.bias_prior = bias_prior_fn()
 
-            if isinstance(bias_posterior_fn, Cell):
-                if bias_posterior_fn.__class__.__name__ != 'NormalPosterior':
-                    raise TypeError('The type of `bias_posterior_fn` should be `NormalPosterior`')
-            else:
-                if bias_posterior_fn.__name__ != 'NormalPosterior':
-                    raise TypeError('The type of `bias_posterior_fn` should be `NormalPosterior`')
-            self.bias_posterior = bias_posterior_fn(shape=[self.out_channels], name='bnn_bias')
+            try:
+                self.bias_posterior = bias_posterior_fn(shape=[self.out_channels], name='bnn_bias')
+            except TypeError:
+                raise TypeError('The type of `bias_posterior_fn` should be `NormalPosterior`')
 
         # mindspore operations
         self.bias_add = P.BiasAdd()

mindspore/nn/probability/bnn_layers/dense_variational.py

@@ -51,13 +51,10 @@ class _DenseVariational(Cell):
                 raise TypeError('The type of `weight_prior_fn` should be `NormalPrior`')
             self.weight_prior = weight_prior_fn()
 
-        if isinstance(weight_posterior_fn, Cell):
-            if weight_posterior_fn.__class__.__name__ != 'NormalPosterior':
-                raise TypeError('The type of `weight_posterior_fn` should be `NormalPosterior`')
-        else:
-            if weight_posterior_fn.__name__ != 'NormalPosterior':
-                raise TypeError('The type of `weight_posterior_fn` should be `NormalPosterior`')
-        self.weight_posterior = weight_posterior_fn(shape=[self.out_channels, self.in_channels], name='bnn_weight')
+        try:
+            self.weight_posterior = weight_posterior_fn(shape=[self.out_channels, self.in_channels], name='bnn_weight')
+        except TypeError:
+            raise TypeError('The type of `weight_posterior_fn` should be `NormalPosterior`')
 
         if self.has_bias:
             if isinstance(bias_prior_fn, Cell):
@@ -69,13 +66,10 @@ class _DenseVariational(Cell):
                     raise TypeError('The type of `bias_prior_fn` should be `NormalPrior`')
                 self.bias_prior = bias_prior_fn()
 
-            if isinstance(bias_posterior_fn, Cell):
-                if bias_posterior_fn.__class__.__name__ != 'NormalPosterior':
-                    raise TypeError('The type of `bias_posterior_fn` should be `NormalPosterior`')
-            else:
-                if bias_posterior_fn.__name__ != 'NormalPosterior':
-                    raise TypeError('The type of `bias_posterior_fn` should be `NormalPosterior`')
-            self.bias_posterior = bias_posterior_fn(shape=[self.out_channels], name='bnn_bias')
+            try:
+                self.bias_posterior = bias_posterior_fn(shape=[self.out_channels], name='bnn_bias')
+            except TypeError:
+                raise TypeError('The type of `bias_posterior_fn` should be `NormalPosterior`')
 
         self.activation = activation
         if isinstance(self.activation, str):

mindspore/nn/probability/bnn_layers/layer_distribution.py

@@ -51,15 +51,16 @@ class NormalPosterior(Cell):
 
     Args:
         name (str): Name prepended to trainable parameter.
-        shape (list): Shape of the mean and standard deviation.
+        shape (list, tuple): Shape of the mean and standard deviation.
         dtype (class `mindspore.dtype`): The argument is used to define the data type of the output tensor.
             Default: mindspore.float32.
-        loc_mean ( float, array_like of floats): Mean of distribution to initialize trainable parameters. Default: 0.
-        loc_std ( float, array_like of floats): Standard deviation of distribution to initialize trainable parameters.
-            Default: 0.1.
-        untransformed_scale_mean ( float, array_like of floats): Mean of distribution to initialize trainable
+        loc_mean (int, float, array_like of floats): Mean of distribution to initialize trainable parameters.
+            Default: 0.
+        loc_std (int, float, array_like of floats): Standard deviation of distribution to initialize trainable
+            parameters. Default: 0.1.
+        untransformed_scale_mean (int, float, array_like of floats): Mean of distribution to initialize trainable
             parameters. Default: -5.
-        untransformed_scale_std ( float, array_like of floats): Standard deviation of distribution to initialize
+        untransformed_scale_std (int, float, array_like of floats): Standard deviation of distribution to initialize
             trainable parameters. Default: 0.1.
 
     Returns:
@@ -80,20 +81,25 @@ class NormalPosterior(Cell):
         if not isinstance(shape, (tuple, list)):
             raise TypeError('The type of `shape` should be `tuple` or `list`')
 
-        if not (np.array(shape) > 0).all():
-            raise ValueError('Negative dimensions are not allowed')
+        try:
+            mean_arr = np.random.normal(loc_mean, loc_std, shape)
+        except ValueError as msg:
+            raise ValueError(msg)
+        except TypeError as msg:
+            raise TypeError(msg)
 
-        if not (np.array(loc_std) >= 0).all():
-            raise ValueError('The value of `loc_std` < 0')
-        if not (np.array(untransformed_scale_std) >= 0).all():
-            raise ValueError('The value of `untransformed_scale_std` < 0')
+        try:
+            untransformed_scale_arr = np.random.normal(untransformed_scale_mean, untransformed_scale_std, shape)
+        except ValueError as msg:
+            raise ValueError(msg)
+        except TypeError as msg:
+            raise TypeError(msg)
 
         self.mean = Parameter(
-            Tensor(np.random.normal(loc_mean, loc_std, shape), dtype=dtype), name=name + '_mean')
+            Tensor(mean_arr, dtype=dtype), name=name + '_mean')
 
         self.untransformed_std = Parameter(
-            Tensor(np.random.normal(untransformed_scale_mean, untransformed_scale_std, shape), dtype=dtype),
-            name=name + '_untransformed_std')
+            Tensor(untransformed_scale_arr, dtype=dtype), name=name + '_untransformed_std')
 
         self.normal = Normal()