!170 Add prim name to error message for operators in nn_ops.py

Merge pull request !170 from fary86/add_prim_name_to_error_message_for_nn_ops

mindspore/_checkparam.py

@@ -118,10 +118,12 @@ class Validator:
         """Integer value judgment."""
         rel_fn = Rel.get_fns(rel)
         type_mismatch = not isinstance(arg_value, int) or isinstance(arg_value, bool)
+        excp_cls = TypeError if type_mismatch else ValueError
         if type_mismatch or not rel_fn(arg_value, value):
             rel_str = Rel.get_strs(rel).format(value)
             msg_prefix = f'For \'{prim_name}\' the' if prim_name else "The"
-            raise ValueError(f'{msg_prefix} `{arg_name}` should be an int and must {rel_str}, but got {arg_value}.')
+            raise excp_cls(f'{msg_prefix} `{arg_name}` should be an int and must {rel_str}, but got `{arg_value}`'
+                           f' with type `{type(arg_value).__name__}`.')
         return arg_value
 
     @staticmethod
@@ -138,10 +140,11 @@ class Validator:
         """Method for checking whether an int value is in some range."""
         rel_fn = Rel.get_fns(rel)
         type_mismatch = not isinstance(arg_value, int)
+        excp_cls = TypeError if type_mismatch else ValueError
         if type_mismatch or not rel_fn(arg_value, lower_limit, upper_limit):
             rel_str = Rel.get_strs(rel).format(lower_limit, upper_limit)
-            raise ValueError(f'For \'{prim_name}\' the `{arg_name}` should be an int in range {rel_str},'
-                             f' but got {arg_value}.')
+            raise excp_cls(f'For \'{prim_name}\' the `{arg_name}` should be an int in range {rel_str},'
+                           f' but got `{arg_value}` with type `{type(arg_value).__name__}`.')
         return arg_value
 
     @staticmethod
@@ -193,19 +196,23 @@ class Validator:
 
     @staticmethod
     def check_const_input(arg_name, arg_value, prim_name):
-        """Check valid value."""
+        """Checks valid value."""
         if arg_value is None:
             raise ValueError(f'For \'{prim_name}\' the `{arg_name}` must be a const input, but got {arg_value}.')
 
     @staticmethod
-    def check_scalar_type_same(args, valid_values, prim_name):
-        """check whether the types of inputs are the same."""
+    def check_type_same(args, valid_values, prim_name):
+        """Checks whether the types of inputs are the same."""
         def _check_tensor_type(arg):
             arg_key, arg_val = arg
             elem_type = arg_val
+            type_names = []
             if not elem_type in valid_values:
-                raise TypeError(f'For \'{prim_name}\' type of `{arg_key}` should be in {valid_values},'
-                                f' but `{arg_key}` is {elem_type}.')
+                for t in valid_values:
+                    type_names.append(str(t))
+                types_info = '[' + ", ".join(type_names) + ']'
+                raise TypeError(f'For \'{prim_name}\' type of `{arg_key}` should be in {types_info},'
+                                f' but got {elem_type}.')
             return (arg_key, elem_type)
 
         def _check_types_same(arg1, arg2):
@@ -213,7 +220,7 @@ class Validator:
             arg2_name, arg2_type = arg2
             if arg1_type != arg2_type:
                 raise TypeError(f'For \'{prim_name}\' type of `{arg2_name}` should be same as `{arg1_name}`,'
-                                f' but `{arg1_name}` is {arg1_type} and `{arg2_name}` is {arg2_type}.')
+                                f' but `{arg1_name}` with type {arg1_type} and `{arg2_name}` with type {arg2_type}.')
             return arg1
 
         elem_types = map(_check_tensor_type, args.items())
@@ -222,25 +229,8 @@ class Validator:
     @staticmethod
     def check_tensor_type_same(args, valid_values, prim_name):
         """Checks whether the element types of input tensors are the same."""
-        def _check_tensor_type(arg):
-            arg_key, arg_val = arg
-            Validator.check_subclass(arg_key, arg_val, mstype.tensor, prim_name)
-            elem_type = arg_val.element_type()
-            if not elem_type in valid_values:
-                raise TypeError(f'For \'{prim_name}\' element type of `{arg_key}` should be in {valid_values},'
-                                f' but element type of `{arg_key}` is {elem_type}.')
-            return (arg_key, elem_type)
-
-        def _check_types_same(arg1, arg2):
-            arg1_name, arg1_type = arg1
-            arg2_name, arg2_type = arg2
-            if arg1_type != arg2_type:
-                raise TypeError(f'For \'{prim_name}\' element type of `{arg2_name}` should be same as `{arg1_name}`,'
-                                f' but `{arg1_name}` is {arg1_type} and `{arg2_name}` is {arg2_type}.')
-            return arg1
-
-        elem_types = map(_check_tensor_type, args.items())
-        reduce(_check_types_same, elem_types)
+        tensor_types = [mstype.tensor_type(t) for t in valid_values]
+        Validator.check_type_same(args, tensor_types, prim_name)
 
     @staticmethod
     def check_scalar_or_tensor_type_same(args, valid_values, prim_name, allow_mix=False):

mindspore/context.py

@@ -34,7 +34,7 @@ GRAPH_MODE = 0
 PYNATIVE_MODE = 1
 
 
-def _make_directory(path: str):
+def _make_directory(path):
     """Make directory."""
     real_path = None
     if path is None or not isinstance(path, str) or path.strip() == "":

tests/ut/python/nn/test_dynamic_lr.py

@@ -41,7 +41,7 @@ class TestInputs:
             dr.piecewise_constant_lr(milestone1, learning_rates)
 
         milestone2 = [1.0, 2.0, True]
-        with pytest.raises(ValueError):
+        with pytest.raises(TypeError):
             dr.piecewise_constant_lr(milestone2, learning_rates)
 
     def test_learning_rates1(self):
@@ -92,13 +92,13 @@ class TestInputs:
 
     def test_total_step1(self):
         total_step1 = 2.0
-        with pytest.raises(ValueError):
+        with pytest.raises(TypeError):
             dr.exponential_decay_lr(learning_rate, decay_rate, total_step1, step_per_epoch, decay_epoch)
 
-        with pytest.raises(ValueError):
+        with pytest.raises(TypeError):
             dr.cosine_decay_lr(min_lr, max_lr, total_step1, step_per_epoch, decay_epoch)
 
-        with pytest.raises(ValueError):
+        with pytest.raises(TypeError):
             dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step1, step_per_epoch, decay_epoch, power)
 
     def test_total_step2(self):
@@ -114,13 +114,13 @@ class TestInputs:
 
     def test_step_per_epoch1(self):
         step_per_epoch1 = True
-        with pytest.raises(ValueError):
+        with pytest.raises(TypeError):
             dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch1, decay_epoch)
 
-        with pytest.raises(ValueError):
+        with pytest.raises(TypeError):
             dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch1, decay_epoch)
 
-        with pytest.raises(ValueError):
+        with pytest.raises(TypeError):
             dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch1, decay_epoch, power)
 
     def test_step_per_epoch2(self):
@@ -136,13 +136,13 @@ class TestInputs:
 
     def test_decay_epoch1(self):
         decay_epoch1 = 'm'
-        with pytest.raises(ValueError):
+        with pytest.raises(TypeError):
             dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch1)
 
-        with pytest.raises(ValueError):
+        with pytest.raises(TypeError):
             dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch1)
 
-        with pytest.raises(ValueError):
+        with pytest.raises(TypeError):
             dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch1, power)
 
     def test_decay_epoch2(self):

tests/ut/python/nn/test_ssim.py

@@ -60,7 +60,7 @@ def test_ssim_max_val_zero():
         net = SSIMNet(max_val)
 
 def test_ssim_filter_size_float():
-    with pytest.raises(ValueError):
+    with pytest.raises(TypeError):
         net = SSIMNet(filter_size=1.1)
 
 def test_ssim_filter_size_zero():

tests/ut/python/ops/test_nn_ops.py

@@ -516,7 +516,7 @@ test_cases = [
 
 test_cases_for_verify_exception = [
     ('Conv2d_ValueError_1', {
-        'block': (lambda _: P.Conv2D(3, 4, mode=-2.0), {'exception': ValueError}),
+        'block': (lambda _: P.Conv2D(3, 4, mode=-2.0), {'exception': TypeError}),
         'desc_inputs': [0],
     }),
     ('Conv2d_ValueError_2', {
@@ -528,7 +528,7 @@ test_cases_for_verify_exception = [
         'desc_inputs': [0],
     }),
     ('MaxPoolWithArgmax_ValueError_2', {
-        'block': (lambda _: P.MaxPoolWithArgmax(ksize='1'), {'exception': ValueError}),
+        'block': (lambda _: P.MaxPoolWithArgmax(ksize='1'), {'exception': TypeError}),
         'desc_inputs': [0],
     }),
     ('MaxPoolWithArgmax_ValueError_3', {
@@ -540,7 +540,7 @@ test_cases_for_verify_exception = [
         'desc_inputs': [0],
     }),
     ('FusedBatchNorm_ValueError_1', {
-        'block': (lambda _: P.FusedBatchNorm(mode="1", epsilon=1e-5, momentum=0.1), {'exception': ValueError}),
+        'block': (lambda _: P.FusedBatchNorm(mode="1", epsilon=1e-5, momentum=0.1), {'exception': TypeError}),
         'desc_inputs': [0],
     }),
     ('FusedBatchNorm_ValueError_2', {
@@ -560,31 +560,31 @@ test_cases_for_verify_exception = [
         'desc_inputs': [0],
     }),
     ('Softmax_ValueError_1', {
-        'block': (lambda _: P.Softmax("1"), {'exception': ValueError}),
+        'block': (lambda _: P.Softmax("1"), {'exception': TypeError}),
         'desc_inputs': [0],
     }),
     ('Softmax_ValueError_2', {
-        'block': (lambda _: P.Softmax(1.1), {'exception': ValueError}),
+        'block': (lambda _: P.Softmax(1.1), {'exception': TypeError}),
         'desc_inputs': [0],
     }),
     ('Softmax_ValueError_3', {
-        'block': (lambda _: P.Softmax(axis="1"), {'exception': ValueError}),
+        'block': (lambda _: P.Softmax(axis="1"), {'exception': TypeError}),
         'desc_inputs': [0],
     }),
     ('DropoutGenMask_ValueError_1', {
-        'block': (lambda _: P.DropoutGenMask(Seed0="seed0"), {'exception': ValueError}),
+        'block': (lambda _: P.DropoutGenMask(Seed0="seed0"), {'exception': TypeError}),
         'desc_inputs': [0],
     }),
     ('DropoutGenMask_ValueError_2', {
-        'block': (lambda _: P.DropoutGenMask(Seed0=1.0), {'exception': ValueError}),
+        'block': (lambda _: P.DropoutGenMask(Seed0=1.0), {'exception': TypeError}),
         'desc_inputs': [0],
     }),
     ('DropoutGenMask_ValueError_3', {
-        'block': (lambda _: P.DropoutGenMask(Seed1="seed1"), {'exception': ValueError}),
+        'block': (lambda _: P.DropoutGenMask(Seed1="seed1"), {'exception': TypeError}),
         'desc_inputs': [0],
     }),
     ('DropoutGenMask_ValueError_4', {
-        'block': (lambda _: P.DropoutGenMask(Seed1=2.0), {'exception': ValueError}),
+        'block': (lambda _: P.DropoutGenMask(Seed1=2.0), {'exception': TypeError}),
         'desc_inputs': [0],
     }),
     ('MaxPool2d_ValueError_1', {