fix 4 issue

mindspore/ops/_op_impl/tbe/broadcast_to.py

@@ -30,7 +30,7 @@ broadcast_to_op_info = TBERegOp("BroadcastTo") \
     .dtype_format(DataType.F32_Default, DataType.F32_Default) \
     .dtype_format(DataType.I32_Default, DataType.I32_Default) \
     .dtype_format(DataType.I8_Default, DataType.I8_Default) \
-    .dtype_format(DataType.U8_Default, DataType.U16_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
     .get_op_info()
 
 

mindspore/ops/_op_impl/tbe/transpose_d.py

@@ -26,6 +26,7 @@ transpose_d_op_info = TBERegOp("Transpose") \
     .attr("perm", "optional", "listInt", "all") \
     .input(0, "x", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
     .dtype_format(DataType.I8_Default, DataType.I8_Default) \
     .dtype_format(DataType.U8_Default, DataType.U8_Default) \
     .dtype_format(DataType.I16_Default, DataType.I16_Default) \

mindspore/ops/operations/nn_ops.py

@@ -1407,7 +1407,7 @@ class BiasAdd(PrimitiveWithInfer):
     except for the channel axis.
 
     Inputs:
-        - **input_x** (Tensor) - Input value, with shape :math:`(N, C)` or :math:`(N, C, H, W)`.
+        - **input_x** (Tensor) - Input value. The input shape can be 2-4 dimensions.
         - **bias** (Tensor) - Bias value, with shape :math:`(C)`.
 
     Outputs:
@@ -1433,8 +1433,7 @@ class BiasAdd(PrimitiveWithInfer):
 
     def infer_dtype(self, x_type, b_type):
         args = {"input_x": x_type, "bias": b_type}
-        valid_types = (mstype.int8, mstype.int32, mstype.float16, mstype.float32)
-        validator.check_tensor_type_same(args, valid_types, self.name)
+        validator.check_tensor_type_same(args, mstype.number_type, self.name)
         return x_type
 
 

mindspore/ops/operations/other_ops.py

@@ -93,8 +93,8 @@ class BoundingBoxEncode(PrimitiveWithInfer):
 
     @prim_attr_register
     def __init__(self, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)):
-        validator.check_value_type('means', means, [tuple, list], self.name)
-        validator.check_value_type('stds', stds, [tuple, list], self.name)
+        validator.check_value_type('means', means, (tuple), self.name)
+        validator.check_value_type('stds', stds, (tuple), self.name)
         for i, value in enumerate(means):
             validator.check_value_type("means[%d]" % i, value, [float], self.name)
         for i, value in enumerate(stds):
@@ -147,8 +147,8 @@ class BoundingBoxDecode(PrimitiveWithInfer):
 
     @prim_attr_register
     def __init__(self, max_shape, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0), wh_ratio_clip=0.016):
-        validator.check_value_type('means', means, [tuple, list], self.name)
-        validator.check_value_type('stds', stds, [tuple, list], self.name)
+        validator.check_value_type('means', means, (tuple), self.name)
+        validator.check_value_type('stds', stds, (tuple), self.name)
         for i, value in enumerate(means):
             validator.check_value_type("means[%d]" % i, value, [float], self.name)
         for i, value in enumerate(stds):