Fix sampler error messages

mindspore/dataset/core/validator_helpers.py

@@ -23,6 +23,7 @@ import numpy as np
 
 import mindspore._c_dataengine as cde
 from ..engine import samplers
+
 # POS_INT_MIN is used to limit values from starting from 0
 POS_INT_MIN = 1
 UINT8_MAX = 255
@@ -289,7 +290,6 @@ def check_sampler_shuffle_shard_options(param_dict):
     shuffle, sampler = param_dict.get('shuffle'), param_dict.get('sampler')
     num_shards, shard_id = param_dict.get('num_shards'), param_dict.get('shard_id')
     num_samples = param_dict.get('num_samples')
-    check_sampler(sampler)
 
     if sampler is not None:
         if shuffle is not None:
@@ -348,6 +348,7 @@ def check_num_samples(value):
         raise ValueError(
             "num_samples exceeds the boundary between {} and {}(INT64_MAX)!".format(0, INT64_MAX))
 
+
 def validate_dataset_param_value(param_list, param_dict, param_type):
     for param_name in param_list:
         if param_dict.get(param_name) is not None:
@@ -387,6 +388,7 @@ def check_tensor_op(param, param_name):
     if not isinstance(param, cde.TensorOp) and not callable(param) and not getattr(param, 'parse', None):
         raise TypeError("{0} is neither a c_transform op (TensorOperation) nor a callable pyfunc.".format(param_name))
 
+
 def check_sampler(sampler):
     """
     Check if the sampler is of valid input.
@@ -419,5 +421,6 @@ def check_sampler(sampler):
         if not (builtin or base_sampler or list_num):
             raise TypeError("Argument sampler is not of type Sampler, BuiltinSamplers, or list of numbers")
 
+
 def replace_none(value, default):
     return value if value is not None else default

mindspore/dataset/engine/samplers.py

@@ -73,11 +73,11 @@ def select_sampler(num_samples, input_sampler, shuffle, num_shards, shard_id):
                 ' shard_id: {}, shuffle: {}.'.format(num_samples, num_shards, shard_id, shuffle))
         if isinstance(input_sampler, BuiltinSampler):
             return input_sampler
-        if _is_iterable(input_sampler):
+        if not isinstance(input_sampler, str) and _is_iterable(input_sampler):
             return SubsetSampler(_get_sample_ids_as_list(input_sampler, num_samples))
         if isinstance(input_sampler, int):
-            return [input_sampler]
+            return SubsetSampler([input_sampler])
-        raise ValueError('Unsupported sampler object ({})'.format(input_sampler))
+        raise TypeError('Unsupported sampler object of type ({})'.format(type(input_sampler)))
     if shuffle is None:
         if num_shards is not None:
             # If shuffle is not specified, sharding enabled, use distributed random sampler
@@ -644,9 +644,9 @@ class SubsetSampler(BuiltinSampler):
             indices = [indices]
 
         for i, item in enumerate(indices):
-            if not isinstance(item, numbers.Number):
+            if not isinstance(item, int):
-                raise TypeError("type of indices element must be number, "
+                raise TypeError("SubsetSampler: Type of indices element must be int, "
-                                "but got w[{}]: {}, type: {}.".format(i, item, type(item)))
+                                "but got list[{}]: {}, type: {}.".format(i, item, type(item)))
 
         if num_samples is not None:
             if not isinstance(num_samples, int):

tests/ut/python/dataset/test_datasets_celeba.py

@@ -179,7 +179,7 @@ def test_celeba_sampler_exception():
             pass
         assert False
     except TypeError as e:
-        assert "Argument" in str(e)
+        assert "Unsupported sampler object of type (<class 'str'>)" in str(e)
 
 
 if __name__ == '__main__':

tests/ut/python/dataset/test_sampler.py

@@ -274,6 +274,26 @@ def test_sampler_list():
 
     dataset_equal(data1, data21 + data22 + data23, 0)
 
+    data3 = ds.ImageFolderDataset("../data/dataset/testPK/data", sampler=1)
+    dataset_equal(data3, data21, 0)
+
+    def bad_pipeline(sampler, msg):
+        with pytest.raises(Exception) as info:
+            data1 = ds.ImageFolderDataset("../data/dataset/testPK/data", sampler=sampler)
+            for _ in data1:
+                pass
+        assert msg in str(info.value)
+
+    bad_pipeline(sampler=[1.5, 7],
+                 msg="Type of indices element must be int, but got list[0]: 1.5, type: <class 'float'>")
+
+    bad_pipeline(sampler=["a", "b"],
+                 msg="Type of indices element must be int, but got list[0]: a, type: <class 'str'>.")
+    bad_pipeline(sampler="a", msg="Unsupported sampler object of type (<class 'str'>)")
+    bad_pipeline(sampler="", msg="Unsupported sampler object of type (<class 'str'>)")
+    bad_pipeline(sampler=np.array([1, 2]),
+                 msg="Type of indices element must be int, but got list[0]: 1, type: <class 'numpy.int64'>.")
+
 
 if __name__ == '__main__':
     test_sequential_sampler(True)