add bool typecheck for conv param

mindspore/_checkparam.py

@@ -565,20 +565,20 @@ def check_input_format(input_param):
 
 
 def _expand_tuple(n_dimensions):
-    """To expand a number to tuple."""
+    """To expand a int number to tuple."""
 
     def convert(m):
         if not isinstance(m, tuple):
-            if isinstance(m, int):
+            if isinstance(m, int) and not isinstance(m, bool):
                 return tuple(repeat(m, n_dimensions))
-            raise TypeError("Input type must be int or tuple.")
+            raise TypeError("Input type must be int or tuple[int].")
 
         if not len(m) is n_dimensions:
-            raise TypeError("Input dimension is incorrect.")
+            raise TypeError("Input tuple dimension is incorrect.")
 
         for i in m:
-            if not isinstance(i, int):
-                raise TypeError("Incorrect type inside of a tuple!")
+            if not isinstance(i, int) or isinstance(i, bool):
+                raise TypeError("Incorrect type inside of a tuple, must be int!")
         return m
 
     return convert

mindspore/compression/quant/qat.py

@@ -410,12 +410,12 @@ class QuantizationAwareTraining(Quantizer):
         """
         act_class = activation.__class__
         act_list = [nn.ReLU, nn.ReLU6, nn.Sigmoid]
-        act_list_withfakebefore = [nn.LeakyReLU, nn.HSigmoid, nn.HSwish]
+        act_list_with_fake_before = [nn.LeakyReLU, nn.HSigmoid, nn.HSwish]
         if act_class in act_list:
             return quant.ActQuant(activation=activation,
                                   quant_config=self.quant_config,
                                   quant_dtype=self.act_dtype)
-        if act_class in act_list_withfakebefore:
+        if act_class in act_list_with_fake_before:
             return quant.ActQuant(activation=activation,
                                   ema=True,
                                   fake_before=True,

mindspore/nn/layer/quant.py

@@ -762,13 +762,10 @@ class Conv2dBnWithoutFoldQuant(Cell):
                  quant_config=quant_config_default,
                  quant_dtype=QuantDtype.INT8):
         super(Conv2dBnWithoutFoldQuant, self).__init__()
-        if isinstance(kernel_size, int):
-            self.kernel_size = (kernel_size, kernel_size)
-        else:
-            self.kernel_size = kernel_size
         self.in_channels = Validator.check_positive_int(in_channels)
         self.out_channels = Validator.check_positive_int(out_channels)
         self.has_bias = has_bias
+        self.kernel_size = twice(kernel_size)
         self.stride = twice(stride)
         self.dilation = twice(dilation)
         self.pad_mode = pad_mode
@@ -884,13 +881,10 @@ class Conv2dQuant(Cell):
                  quant_config=quant_config_default,
                  quant_dtype=QuantDtype.INT8):
         super(Conv2dQuant, self).__init__()
-        if isinstance(kernel_size, int):
-            self.kernel_size = (kernel_size, kernel_size)
-        else:
-            self.kernel_size = kernel_size
         self.in_channels = Validator.check_positive_int(in_channels)
         self.out_channels = Validator.check_positive_int(out_channels)
         self.has_bias = has_bias
+        self.kernel_size = twice(kernel_size)
         self.stride = twice(stride)
         self.dilation = twice(dilation)
         self.pad_mode = pad_mode

mindspore/ops/operations/nn_ops.py

@@ -52,7 +52,7 @@ def _check_positive_int_or_tuple(arg_name, arg_value, prim_name, allow_four=Fals
     validator.check_value_type(arg_name, arg_value, (int, tuple), prim_name)
     ret_value = _get_return_value()
     for item in ret_value:
-        if isinstance(item, int) and item > 0:
+        if isinstance(item, int) and not isinstance(item, bool) and item > 0:
             continue
         _raise_message()
     return ret_value