check num_samples

mindspore/dataset/engine/validators.py

@@ -243,6 +243,8 @@ def check_param_type(param_list, param_dict, param_type):
         if param_dict.get(param_name) is not None:
             if param_name == 'num_parallel_workers':
                 check_num_parallel_workers(param_dict.get(param_name))
+            if param_name == 'num_samples':
+                check_num_samples(param_dict.get(param_name))
             else:
                 check_type(param_dict.get(param_name), param_name, param_type)
 
@@ -262,6 +264,12 @@ def check_num_parallel_workers(value):
         raise ValueError("num_parallel_workers exceeds the boundary between 0 and {}!".format(cpu_count()))
 
 
+def check_num_samples(value):
+    check_type(value, 'num_samples', int)
+    if value <= 0:
+        raise ValueError("num_samples must be greater than 0!")
+
+
 def check_dataset_dir(dataset_dir):
     if not os.path.isdir(dataset_dir) or not os.access(dataset_dir, os.R_OK):
         raise ValueError("The folder {} does not exist or permission denied!".format(dataset_dir))

tests/ut/python/dataset/test_datasets_sharding.py

@@ -33,14 +33,14 @@ def test_imagefolder_shardings(print_res=False):
     # total 44 rows in dataset
     assert (sharding_config(4, 0, 5, False, dict()) == [0, 0, 0, 1, 1])  # 5 rows
     assert (sharding_config(4, 0, 12, False, dict()) == [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3])  # 11 rows
-    assert (sharding_config(4, 3, 0, False, dict()) == [0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3])  # 11 rows
+    assert (sharding_config(4, 3, None, False, dict()) == [0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3])  # 11 rows
     # total 22 in dataset rows because of class indexing which takes only 2 folders
-    assert (len(sharding_config(4, 0, 0, True, {"class1": 111, "class2": 999})) == 6)
+    assert (len(sharding_config(4, 0, None, True, {"class1": 111, "class2": 999})) == 6)
     assert (len(sharding_config(4, 2, 3, True, {"class1": 111, "class2": 999})) == 3)
     # test with repeat
     assert (sharding_config(4, 0, 12, False, dict(), 3) == [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3] * 3)
     assert (sharding_config(4, 0, 5, False, dict(), 5) == [0, 0, 0, 1, 1] * 5)
-    assert (len(sharding_config(5, 1, 0, True, {"class1": 111, "class2": 999}, 4)) == 20)
+    assert (len(sharding_config(5, 1, None, True, {"class1": 111, "class2": 999}, 4)) == 20)
 
 
 def test_manifest_shardings(print_res=False):

tests/ut/python/dataset/test_exceptions.py

@@ -18,6 +18,7 @@ import pytest
 import mindspore.dataset as ds
 
 DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
+SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
 
 
 def skip_test_exception():
@@ -29,5 +30,23 @@ def skip_test_exception():
     assert "The shape size 1 of input tensor is invalid" in str(info.value)
 
 
+def test_sample_exception():
+    num_samples = 0
+    with pytest.raises(ValueError) as info:
+        data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], num_samples=num_samples)
+    assert "num_samples must be greater than 0" in str(info.value)
+    num_samples = -1
+    with pytest.raises(ValueError) as info:
+        data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], num_samples=num_samples)
+    assert "num_samples must be greater than 0" in str(info.value)
+    num_samples = 1
+    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], num_samples=num_samples)
+    data = data.map(input_columns=["image"], operations=vision.Decode())
+    data = data.map(input_columns=["image"], operations=vision.Resize((100, 100)))
+    num_iters = 0
+    for item in data.create_dict_iterator():
+        num_iters += 1
+    assert num_iters == 1
+
 if __name__ == '__main__':
     test_exception()