fix: concat get_dataset_size error

mindspore/dataset/engine/datasets.py

@@ -2343,8 +2343,10 @@ class ConcatDataset(DatasetOp):
             Number, number of batches.
         """
         if self.dataset_size is None:
-            children_sizes = [c.get_dataset_size() for c in self.children]
-            self.dataset_size = sum(children_sizes)
+            num_rows = 0
+            for _ in self.create_dict_iterator():
+                num_rows += 1
+            self.dataset_size = num_rows
         return self.dataset_size
 
     def use_sampler(self, sampler):

mindspore/dataset/transforms/vision/py_transforms.py

@@ -1115,7 +1115,8 @@ class RandomAffine:
             - Inter.BICUBIC, means resample method is bicubic interpolation.
 
         fill_value (Union[tuple, int], optional): Optional fill_value to fill the area outside the transform
-            in the output image. Used only in Pillow versions > 5.0.0 (default=0, filling is performed).
+            in the output image. There must be three elements in tuple and the value of single element is [0, 255].
+            Used only in Pillow versions > 5.0.0 (default=0, filling is performed).
 
     Raises:
         ValueError: If degrees is negative.
@@ -1127,6 +1128,7 @@ class RandomAffine:
         TypeError: If translate is specified but is not list or a tuple of length 2.
         TypeError: If scale is not a list or tuple of length 2.
         TypeError: If shear is not a list or tuple of length 2 or 4.
+        TypeError: If fill_value is not a single integer or a 3-tuple.
 
     Examples:
         >>> py_transforms.ComposeOp([py_transforms.Decode(),

tests/ut/python/dataset/test_paddeddataset.py

@@ -225,31 +225,63 @@ def test_imagefolder_padded():
     assert verify_list[9] == 6
 
 def test_imagefolder_padded_with_decode():
-    DATA_DIR = "../data/dataset/testPK/data"
-    data = ds.ImageFolderDatasetV2(DATA_DIR)
+    num_shards = 5
+    count = 0
+    for shard_id in range(num_shards):
+        DATA_DIR = "../data/dataset/testPK/data"
+        data = ds.ImageFolderDatasetV2(DATA_DIR)
 
-    white_io = BytesIO()
-    Image.new('RGB', (224, 224), (255, 255, 255)).save(white_io, 'JPEG')
-    padded_sample = {}
-    padded_sample['image'] = np.array(bytearray(white_io), dtype='uint8')
-    padded_sample['label'] = np.array(-1, np.int32)
+        white_io = BytesIO()
+        Image.new('RGB', (224, 224), (255, 255, 255)).save(white_io, 'JPEG')
+        padded_sample = {}
+        padded_sample['image'] = np.array(bytearray(white_io.getvalue()), dtype='uint8')
+        padded_sample['label'] = np.array(-1, np.int32)
 
-    white_samples = [padded_sample, padded_sample, padded_sample, padded_sample]
-    data2 = ds.PaddedDataset(white_samples)
-    data3 = data + data2
+        white_samples = [padded_sample, padded_sample, padded_sample, padded_sample]
+        data2 = ds.PaddedDataset(white_samples)
+        data3 = data + data2
 
+        testsampler = ds.DistributedSampler(num_shards=num_shards, shard_id=shard_id, shuffle=False, num_samples=None)
+        data3.use_sampler(testsampler)
+        data3 = data3.map(input_columns="image", operations=V_C.Decode())
+        shard_sample_count = 0
+        for ele in data3.create_dict_iterator():
+            print("label: {}".format(ele['label']))
+            count += 1
+            shard_sample_count += 1
+        assert shard_sample_count in (9, 10)
+    assert count == 48
+
+def test_imagefolder_padded_with_decode_and_get_dataset_size():
     num_shards = 5
     count = 0
     for shard_id in range(num_shards):
+        DATA_DIR = "../data/dataset/testPK/data"
+        data = ds.ImageFolderDatasetV2(DATA_DIR)
+
+        white_io = BytesIO()
+        Image.new('RGB', (224, 224), (255, 255, 255)).save(white_io, 'JPEG')
+        padded_sample = {}
+        padded_sample['image'] = np.array(bytearray(white_io.getvalue()), dtype='uint8')
+        padded_sample['label'] = np.array(-1, np.int32)
+
+        white_samples = [padded_sample, padded_sample, padded_sample, padded_sample]
+        data2 = ds.PaddedDataset(white_samples)
+        data3 = data + data2
+
         testsampler = ds.DistributedSampler(num_shards=num_shards, shard_id=shard_id, shuffle=False, num_samples=None)
         data3.use_sampler(testsampler)
-        data3.map(input_columns="image", operations=V_C.Decode())
+        shard_dataset_size = data3.get_dataset_size()
+        data3 = data3.map(input_columns="image", operations=V_C.Decode())
+        shard_sample_count = 0
         for ele in data3.create_dict_iterator():
             print("label: {}".format(ele['label']))
             count += 1
+            shard_sample_count += 1
+        assert shard_sample_count in (9, 10)
+        assert shard_dataset_size == shard_sample_count
     assert count == 48
 
-
 def test_more_shard_padded():
     result_list = []
     for i in range(8):