!5387 Set num_epochs=1 for MindData python testcases

Merge pull request !5387 from lixiachen/testcase_epoch

mindspore/ccsrc/minddata/dataset/engine/datasetops/map_op/map_op.cc

@@ -308,6 +308,7 @@ Status MapOp::WorkerCompute(DataBuffer *in_buffer, TensorQTable *new_tensor_tabl
   std::vector<TensorRow> result_table;
   // Executing the list of jobs.
   for (size_t i = 0; i < job_list.size(); i++) {
+    RETURN_IF_INTERRUPTED();
     // Execute MapWorkerJob.
     RETURN_IF_NOT_OK(job_list[i]->Run(job_input_table, &result_table));
     // Assign the processed data as an input for the next job processing, except for the last TensorOp in the list.

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/tf_reader_op.cc

@@ -581,6 +581,7 @@ Status TFReaderOp::LoadFile(const std::string &filename, const int64_t start_off
     if (!load_jagged_connector_) {
       break;
     }
+    RETURN_IF_INTERRUPTED();
 
     // read length
     int64_t record_length = 0;

mindspore/dataset/engine/datasets.py

@@ -1181,7 +1181,7 @@ class Dataset:
 
     def __iter__(self):
         """Create an Iterator over the dataset."""
-        return self.create_tuple_iterator()
+        return self.create_tuple_iterator(num_epochs=1)
 
     @property
     def input_indexs(self):
@@ -1598,7 +1598,7 @@ class BucketBatchByLengthDataset(DatasetOp):
         """
         if self.dataset_size is None:
             num_rows = 0
-            for _ in self.create_dict_iterator():
+            for _ in self.create_dict_iterator(num_epochs=1):
                 num_rows += 1
             self.dataset_size = num_rows
         return self.dataset_size
@@ -2130,7 +2130,7 @@ class FilterDataset(DatasetOp):
         """
         if self.dataset_size is None:
             num_rows = 0
-            for _ in self.create_dict_iterator():
+            for _ in self.create_dict_iterator(num_epochs=1):
                 num_rows += 1
             self.dataset_size = num_rows
         return self.dataset_size
@@ -2367,7 +2367,7 @@ class ConcatDataset(DatasetOp):
         """
         if self.dataset_size is None:
             num_rows = 0
-            for _ in self.create_dict_iterator():
+            for _ in self.create_dict_iterator(num_epochs=1):
                 num_rows += 1
             self.dataset_size = num_rows
         return self.dataset_size
@@ -3463,7 +3463,7 @@ class GeneratorDataset(MappableDataset):
                     self.dataset_size = rows_from_sampler
             else:
                 num_rows = 0
-                for _ in self.create_dict_iterator():
+                for _ in self.create_dict_iterator(num_epochs=1):
                     num_rows += 1
                 self.dataset_size = num_rows
         return self.dataset_size

mindspore/dataset/text/utils.py

@@ -80,7 +80,7 @@ class Vocab(cde.Vocab):
         if special_tokens is None:
             special_tokens = []
         root = copy.deepcopy(dataset).build_vocab(vocab, columns, freq_range, top_k, special_tokens, special_first)
-        for d in root.create_dict_iterator():
+        for d in root.create_dict_iterator(num_epochs=1):
             if d is not None:
                 raise ValueError("from_dataset should receive data other than None.")
         return vocab
@@ -167,7 +167,7 @@ class SentencePieceVocab(cde.SentencePieceVocab):
         vocab = SentencePieceVocab()
         root = copy.deepcopy(dataset).build_sentencepiece_vocab(vocab, col_names, vocab_size, character_coverage,
                                                                 model_type, params)
-        for d in root.create_dict_iterator():
+        for d in root.create_dict_iterator(num_epochs=1):
             if d is None:
                 raise ValueError("from_dataset should receive data other than None.")
         return vocab

tests/ut/python/dataset/test_HWC2CHW.py

@@ -47,7 +47,7 @@ def test_HWC2CHW(plot=False):
 
     image_transposed = []
     image = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         transposed_item = item1["image"].copy()
         original_item = item2["image"].copy()
         image_transposed.append(transposed_item.transpose(1, 2, 0))
@@ -104,7 +104,7 @@ def test_HWC2CHW_comp(plot=False):
 
     image_c_transposed = []
     image_py_transposed = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         c_image = item1["image"]
         py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
 

tests/ut/python/dataset/test_apply.py

@@ -40,7 +40,7 @@ def test_apply_generator_case():
     data2 = data2.repeat(2)
     data2 = data2.batch(4)
 
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         np.testing.assert_array_equal(item1["data"], item2["data"])
 
 
@@ -63,7 +63,7 @@ def test_apply_imagefolder_case():
     data2 = data2.map(operations=normalize_op)
     data2 = data2.repeat(2)
 
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         np.testing.assert_array_equal(item1["image"], item2["image"])
 
 
@@ -85,7 +85,7 @@ def test_apply_flow_case_0(id_=0):
 
     data1 = data1.apply(dataset_fn)
     num_iter = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_iter = num_iter + 1
 
     if id_ == 0:
@@ -116,7 +116,7 @@ def test_apply_flow_case_1(id_=1):
 
     data1 = data1.apply(dataset_fn)
     num_iter = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_iter = num_iter + 1
 
     if id_ == 0:
@@ -147,7 +147,7 @@ def test_apply_flow_case_2(id_=2):
 
     data1 = data1.apply(dataset_fn)
     num_iter = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_iter = num_iter + 1
 
     if id_ == 0:
@@ -178,7 +178,7 @@ def test_apply_flow_case_3(id_=3):
 
     data1 = data1.apply(dataset_fn)
     num_iter = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_iter = num_iter + 1
 
     if id_ == 0:
@@ -204,7 +204,7 @@ def test_apply_exception_case():
 
     try:
         data1 = data1.apply("123")
-        for _ in data1.create_dict_iterator():
+        for _ in data1.create_dict_iterator(num_epochs=1):
             pass
         assert False
     except TypeError:
@@ -212,7 +212,7 @@ def test_apply_exception_case():
 
     try:
         data1 = data1.apply(exception_fn)
-        for _ in data1.create_dict_iterator():
+        for _ in data1.create_dict_iterator(num_epochs=1):
             pass
         assert False
     except TypeError:
@@ -221,7 +221,7 @@ def test_apply_exception_case():
     try:
         data2 = data1.apply(dataset_fn)
         _ = data1.apply(dataset_fn)
-        for _, _ in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        for _, _ in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
             pass
         assert False
     except ValueError as e:

tests/ut/python/dataset/test_bounding_box_augment.py

@@ -59,7 +59,7 @@ def test_bounding_box_augment_with_rotation_op(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -98,7 +98,7 @@ def test_bounding_box_augment_with_crop_op(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -136,7 +136,7 @@ def test_bounding_box_augment_valid_ratio_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -170,7 +170,7 @@ def test_bounding_box_augment_op_coco_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()):
+    for unAug, Aug in zip(dataCoco1.create_dict_iterator(num_epochs=1), dataCoco2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -214,7 +214,7 @@ def test_bounding_box_augment_valid_edge_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 

tests/ut/python/dataset/test_bucket_batch_by_length.py

@@ -130,7 +130,7 @@ def test_bucket_batch_multi_bucket_no_padding():
                        [[1], [5], [9]]]
 
     output = []
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         output.append(data["col1"].tolist())
 
     assert output == expected_output
@@ -161,7 +161,7 @@ def test_bucket_batch_multi_bucket_with_padding():
                         [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]]
 
     output = []
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         output.append(data["col1"].tolist())
 
     assert output == expected_output
@@ -182,7 +182,7 @@ def test_bucket_batch_single_bucket_no_padding():
                        [[5], [6], [7], [8], [9]]]
 
     output = []
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         output.append(data["col1"].tolist())
 
     assert output == expected_output
@@ -212,7 +212,7 @@ def test_bucket_batch_single_bucket_with_padding():
                         [0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0]]]
 
     output = []
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         output.append(data["col1"].tolist())
 
     assert output == expected_output
@@ -243,7 +243,7 @@ def test_bucket_batch_pad_to_bucket_boundary():
                         [0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0, 0]]]
 
     output = []
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         output.append(data["col1"].tolist())
 
     assert output == expected_output
@@ -279,7 +279,7 @@ def test_bucket_batch_default_pad():
                         [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]]]
 
     output = []
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         output.append(data["col1"].tolist())
 
     assert output == expected_output
@@ -310,7 +310,7 @@ def test_bucket_batch_drop_remainder():
                        [[19], [22], [25]]]
 
     output = []
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         output.append(data["col1"].tolist())
 
     assert output == expected_output
@@ -340,7 +340,7 @@ def test_bucket_batch_default_length_function():
                         [0, 1, 2, 3, 4, 5, 6, 7, 8]]]
 
     output = []
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         output.append(data["col1"].tolist())
 
     assert output == expected_output
@@ -375,7 +375,7 @@ def test_bucket_batch_multi_column():
 
     same_shape_output = []
     variable_shape_output = []
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         same_shape_output.append(data["same_shape"].tolist())
         variable_shape_output.append(data["variable_shape"].tolist())
 
@@ -396,7 +396,7 @@ def test_bucket_batch_get_dataset_size():
     data_size = dataset.get_dataset_size()
 
     num_rows = 0
-    for _ in dataset.create_dict_iterator():
+    for _ in dataset.create_dict_iterator(num_epochs=1):
         num_rows += 1
 
     assert data_size == num_rows

tests/ut/python/dataset/test_c_compose.py

@@ -27,7 +27,7 @@ def test_compose():
             data = ds.NumpySlicesDataset(arr, column_names="col", shuffle=False)
             data = data.map(input_columns=["col"], operations=ops.Compose(op_list))
             res = []
-            for i in data.create_dict_iterator():
+            for i in data.create_dict_iterator(num_epochs=1):
                 res.append(i["col"].tolist())
             return res
         except (TypeError, ValueError) as e:

tests/ut/python/dataset/test_c_random_apply.py

@@ -26,7 +26,7 @@ def test_random_apply():
             data = ds.NumpySlicesDataset(arr, column_names="col", shuffle=False)
             data = data.map(input_columns=["col"], operations=ops.RandomApply(op_list, prob))
             res = []
-            for i in data.create_dict_iterator():
+            for i in data.create_dict_iterator(num_epochs=1):
                 res.append(i["col"].tolist())
             return res
         except (TypeError, ValueError) as e:

tests/ut/python/dataset/test_c_random_choice.py

@@ -26,7 +26,7 @@ def test_random_choice():
             data = ds.NumpySlicesDataset(arr, column_names="col", shuffle=False)
             data = data.map(input_columns=["col"], operations=ops.RandomChoice(op_list))
             res = []
-            for i in data.create_dict_iterator():
+            for i in data.create_dict_iterator(num_epochs=1):
                 res.append(i["col"].tolist())
             return res
         except (TypeError, ValueError) as e:

tests/ut/python/dataset/test_cache_map.py

@@ -111,7 +111,7 @@ def test_cache_map_basic3():
     logger.info("ds1.dataset_size is ", ds1.get_dataset_size())
 
     num_iter = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         logger.info("get data from dataset")
         num_iter += 1
 
@@ -136,7 +136,7 @@ def test_cache_map_basic4():
     shape = ds1.output_shapes()
     logger.info(shape)
     num_iter = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         logger.info("get data from dataset")
         num_iter += 1
 
@@ -172,7 +172,7 @@ def test_cache_map_failure1():
 
     try:
         num_iter = 0
-        for _ in ds1.create_dict_iterator():
+        for _ in ds1.create_dict_iterator(num_epochs=1):
             num_iter += 1
     except RuntimeError as e:
         logger.info("Got an exception in DE: {}".format(str(e)))

tests/ut/python/dataset/test_cache_nomap.py

@@ -48,7 +48,7 @@ def test_cache_nomap_basic1():
     ds1 = ds1.repeat(4)
 
     num_iter = 0
-    for data in ds1.create_dict_iterator():
+    for data in ds1.create_dict_iterator(num_epochs=1):
         logger.info("printing the label: {}".format(data["label"]))
         num_iter += 1
 
@@ -80,7 +80,7 @@ def test_cache_nomap_basic2():
     ds1 = ds1.repeat(2)
 
     num_iter = 0
-    for data in ds1.create_dict_iterator():
+    for data in ds1.create_dict_iterator(num_epochs=1):
         logger.info("printing the label: {}".format(data["label"]))
         num_iter += 1
 
@@ -112,7 +112,7 @@ def test_cache_nomap_basic3():
     ds1 = ds1.repeat(4)
 
     num_iter = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         num_iter += 1
 
     logger.info("Number of data in ds1: {} ".format(num_iter))
@@ -164,7 +164,7 @@ def test_cache_nomap_basic4():
     ds1 = ds1.repeat(4)
 
     num_iter = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         num_iter += 1
 
     logger.info("Number of data in ds1: {} ".format(num_iter))
@@ -201,7 +201,7 @@ def test_cache_nomap_basic5():
     ds1 = ds1.repeat(4)
 
     num_iter = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         num_iter += 1
 
     logger.info("Number of data in ds1: {} ".format(num_iter))
@@ -241,7 +241,7 @@ def test_cache_nomap_basic6():
     ds1 = ds1.repeat(4)
 
     num_iter = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         num_iter += 1
 
     logger.info("Number of data in ds1: {} ".format(num_iter))
@@ -277,7 +277,7 @@ def test_cache_nomap_basic7():
     ds1 = ds1.repeat(4)
 
     num_iter = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         num_iter += 1
 
     logger.info("Number of data in ds1: {} ".format(num_iter))
@@ -309,13 +309,13 @@ def test_cache_nomap_allowed_share1():
     ds2 = ds2.shuffle(buffer_size=2)
 
     num_iter = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 12
     logger.info("Number of data in ds1: {} ".format(num_iter))
 
     num_iter = 0
-    for _ in ds2.create_dict_iterator():
+    for _ in ds2.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 3
     logger.info("test_cache_nomap_allowed_share1 Ended.\n")
@@ -351,13 +351,13 @@ def test_cache_nomap_allowed_share2():
     ds2 = ds2.shuffle(buffer_size=2)
 
     num_iter = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         num_iter += 1
     logger.info("Number of data in ds1: {} ".format(num_iter))
     assert num_iter == 12
 
     num_iter = 0
-    for _ in ds2.create_dict_iterator():
+    for _ in ds2.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 3
     logger.info("test_cache_nomap_allowed_share2 Ended.\n")
@@ -387,13 +387,13 @@ def test_cache_nomap_allowed_share3():
     ds2 = ds2.repeat(4)
 
     num_iter = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         num_iter += 1
     logger.info("Number of data in ds1: {} ".format(num_iter))
     assert num_iter == 12
 
     num_iter = 0
-    for _ in ds2.create_dict_iterator():
+    for _ in ds2.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 12
     logger.info("test_cache_nomap_allowed_share3 Ended.\n")
@@ -424,13 +424,13 @@ def test_cache_nomap_allowed_share4():
     ds2 = ds2.map(input_columns=["image"], operations=decode_op, cache=some_cache, num_parallel_workers=2)
 
     num_iter = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         num_iter += 1
     logger.info("Number of data in ds1: {} ".format(num_iter))
     assert num_iter == 3
 
     num_iter = 0
-    for _ in ds2.create_dict_iterator():
+    for _ in ds2.create_dict_iterator(num_epochs=1):
         num_iter += 1
     logger.info("Number of data in ds2: {} ".format(num_iter))
     assert num_iter == 3
@@ -464,7 +464,7 @@ def test_cache_nomap_disallowed_share1():
     ds2 = ds2.map(input_columns=["image"], operations=rescale_op, cache=some_cache)
 
     num_iter = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         num_iter += 1
     logger.info("Number of data in ds1: {} ".format(num_iter))
     assert num_iter == 3

tests/ut/python/dataset/test_callbacks.py

@@ -279,7 +279,7 @@ def test_callbacks_non_sink():
                               'ms_step_end_2_6', 'ds_step_begin_2_7', 'ms_step_end_2_7', 'ds_step_begin_2_8',
                               'ms_step_end_2_8', 'ms_epoch_end_2_8']
 
-    assert events == expected_synced_events
+    assert events[:18] == expected_synced_events
 
 
 def test_callbacks_non_sink_batch_size2():
@@ -303,7 +303,7 @@ def test_callbacks_non_sink_batch_size2():
                               'ds_step_begin_2_5', 'ms_step_end_2_3', 'ds_step_begin_2_7',
                               'ms_step_end_2_4', 'ms_epoch_end_2_4']
 
-    assert events == expected_synced_events
+    assert events[:10] == expected_synced_events
 
 
 def test_callbacks_non_sink_mismatch_size():
@@ -443,7 +443,7 @@ def test_callbacks_one_cb():
     data = data.map(operations=(lambda x: x), callbacks=[my_epoch_begin, my_step_end])
     data = data.map(operations=(lambda x: x), callbacks=[my_epoch_end, my_step_begin])
 
-    itr = data.create_tuple_iterator()
+    itr = data.create_tuple_iterator(num_epochs=2)
     for _ in range(2):
         for _ in itr:
             pass

tests/ut/python/dataset/test_center_crop.py

@@ -48,7 +48,7 @@ def test_center_crop_op(height=375, width=375, plot=False):
 
     image_cropped = []
     image = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image_cropped.append(item1["image"].copy())
         image.append(item2["image"].copy())
     if plot:
@@ -98,7 +98,7 @@ def test_center_crop_comp(height=375, width=375, plot=False):
 
     image_c_cropped = []
     image_py_cropped = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         c_image = item1["image"]
         py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         # Note: The images aren't exactly the same due to rounding error
@@ -131,7 +131,7 @@ def test_crop_grayscale(height=375, width=375):
     crop_gray = vision.CenterCrop([height, width])
     data1 = data1.map(input_columns=["image"], operations=crop_gray)
 
-    for item1 in data1.create_dict_iterator():
+    for item1 in data1.create_dict_iterator(num_epochs=1):
         c_image = item1["image"]
 
         # Check that the image is grayscale

tests/ut/python/dataset/test_config.py

@@ -287,7 +287,7 @@ def test_deterministic_python_seed():
     data1 = data1.map(input_columns=["image"], operations=transform())
     data1_output = []
     # config.set_seed() calls random.seed()
-    for data_one in data1.create_dict_iterator():
+    for data_one in data1.create_dict_iterator(num_epochs=1):
         data1_output.append(data_one["image"])
 
     # Second dataset
@@ -297,7 +297,7 @@ def test_deterministic_python_seed():
     ds.config.set_seed(0)
 
     data2_output = []
-    for data_two in data2.create_dict_iterator():
+    for data_two in data2.create_dict_iterator(num_epochs=1):
         data2_output.append(data_two["image"])
 
     np.testing.assert_equal(data1_output, data2_output)
@@ -330,7 +330,7 @@ def test_deterministic_python_seed_multi_thread():
     data1 = data1.map(input_columns=["image"], operations=transform(), python_multiprocessing=True)
     data1_output = []
     # config.set_seed() calls random.seed()
-    for data_one in data1.create_dict_iterator():
+    for data_one in data1.create_dict_iterator(num_epochs=1):
         data1_output.append(data_one["image"])
 
     # Second dataset
@@ -341,7 +341,7 @@ def test_deterministic_python_seed_multi_thread():
     ds.config.set_seed(0)
 
     data2_output = []
-    for data_two in data2.create_dict_iterator():
+    for data_two in data2.create_dict_iterator(num_epochs=1):
         data2_output.append(data_two["image"])
 
     try:

tests/ut/python/dataset/test_cut_out.py

@@ -59,7 +59,7 @@ def test_cut_out_op(plot=False):
     data2 = data2.map(input_columns=["image"], operations=transforms_2)
 
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         num_iter += 1
         image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         # C image doesn't require transpose
@@ -106,7 +106,7 @@ def test_cut_out_op_multicut(plot=False):
 
     num_iter = 0
     image_list_1, image_list_2 = [], []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         num_iter += 1
         image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         # C image doesn't require transpose
@@ -187,7 +187,7 @@ def test_cut_out_comp(plot=False):
 
     num_iter = 0
     image_list_1, image_list_2 = [], []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         num_iter += 1
         image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         # C image doesn't require transpose

tests/ut/python/dataset/test_dataset_numpy_slices.py

@@ -62,7 +62,7 @@ def test_numpy_slices_list_append():
     data1 = data1.map(input_columns=["image"], operations=[vision.Decode(True), resize_op])
 
     res = []
-    for data in data1.create_dict_iterator():
+    for data in data1.create_dict_iterator(num_epochs=1):
         res.append(data["image"])
 
     ds = de.NumpySlicesDataset(res, column_names=["col1"], shuffle=False)

tests/ut/python/dataset/test_datasets_celeba.py

@@ -27,7 +27,7 @@ def test_celeba_dataset_label():
         [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
          0, 0, 1]]
     count = 0
-    for item in data.create_dict_iterator():
+    for item in data.create_dict_iterator(num_epochs=1):
         logger.info("----------image--------")
         logger.info(item["image"])
         logger.info("----------attr--------")
@@ -50,7 +50,7 @@ def test_celeba_dataset_op():
     data = data.map(input_columns=["image"], operations=resize_op)
 
     count = 0
-    for item in data.create_dict_iterator():
+    for item in data.create_dict_iterator(num_epochs=1):
         logger.info("----------image--------")
         logger.info(item["image"])
         count = count + 1
@@ -63,7 +63,7 @@ def test_celeba_dataset_ext():
     expect_labels = [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1,
                      0, 1, 0, 1, 0, 0, 1],
     count = 0
-    for item in data.create_dict_iterator():
+    for item in data.create_dict_iterator(num_epochs=1):
         logger.info("----------image--------")
         logger.info(item["image"])
         logger.info("----------attr--------")
@@ -77,7 +77,7 @@ def test_celeba_dataset_ext():
 def test_celeba_dataset_distribute():
     data = ds.CelebADataset(DATA_DIR, decode=True, num_shards=2, shard_id=0)
     count = 0
-    for item in data.create_dict_iterator():
+    for item in data.create_dict_iterator(num_epochs=1):
         logger.info("----------image--------")
         logger.info(item["image"])
         logger.info("----------attr--------")

tests/ut/python/dataset/test_datasets_cifarop.py

@@ -75,7 +75,7 @@ def test_cifar10_content_check():
     images, labels = load_cifar(DATA_DIR_10)
     num_iter = 0
     # in this example, each dictionary has keys "image" and "label"
-    for i, d in enumerate(data1.create_dict_iterator()):
+    for i, d in enumerate(data1.create_dict_iterator(num_epochs=1)):
         np.testing.assert_array_equal(d["image"], images[i])
         np.testing.assert_array_equal(d["label"], labels[i])
         num_iter += 1
@@ -91,21 +91,21 @@ def test_cifar10_basic():
     # case 0: test loading the whole dataset
     data0 = ds.Cifar10Dataset(DATA_DIR_10)
     num_iter0 = 0
-    for _ in data0.create_dict_iterator():
+    for _ in data0.create_dict_iterator(num_epochs=1):
         num_iter0 += 1
     assert num_iter0 == 10000
 
     # case 1: test num_samples
     data1 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100)
     num_iter1 = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_iter1 += 1
     assert num_iter1 == 100
 
     # case 2: test num_parallel_workers
     data2 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=50, num_parallel_workers=1)
     num_iter2 = 0
-    for _ in data2.create_dict_iterator():
+    for _ in data2.create_dict_iterator(num_epochs=1):
         num_iter2 += 1
     assert num_iter2 == 50
 
@@ -113,7 +113,7 @@ def test_cifar10_basic():
     data3 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100)
     data3 = data3.repeat(3)
     num_iter3 = 0
-    for _ in data3.create_dict_iterator():
+    for _ in data3.create_dict_iterator(num_epochs=1):
         num_iter3 += 1
     assert num_iter3 == 300
 
@@ -125,7 +125,7 @@ def test_cifar10_basic():
     assert data4.get_dataset_size() == 15
     assert data4.get_batch_size() == 7
     num_iter4 = 0
-    for _ in data4.create_dict_iterator():
+    for _ in data4.create_dict_iterator(num_epochs=1):
         num_iter4 += 1
     assert num_iter4 == 15
 
@@ -137,7 +137,7 @@ def test_cifar10_basic():
     assert data5.get_dataset_size() == 14
     assert data5.get_batch_size() == 7
     num_iter5 = 0
-    for _ in data5.create_dict_iterator():
+    for _ in data5.create_dict_iterator(num_epochs=1):
         num_iter5 += 1
     assert num_iter5 == 14
 
@@ -153,7 +153,7 @@ def test_cifar10_pk_sampler():
     data = ds.Cifar10Dataset(DATA_DIR_10, sampler=sampler)
     num_iter = 0
     label_list = []
-    for item in data.create_dict_iterator():
+    for item in data.create_dict_iterator(num_epochs=1):
         label_list.append(item["label"])
         num_iter += 1
     np.testing.assert_array_equal(golden, label_list)
@@ -170,7 +170,7 @@ def test_cifar10_sequential_sampler():
     data1 = ds.Cifar10Dataset(DATA_DIR_10, sampler=sampler)
     data2 = ds.Cifar10Dataset(DATA_DIR_10, shuffle=False, num_samples=num_samples)
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         np.testing.assert_equal(item1["label"], item2["label"])
         num_iter += 1
     assert num_iter == num_samples
@@ -225,7 +225,7 @@ def test_cifar10_visualize(plot=False):
     data1 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=10, shuffle=False)
     num_iter = 0
     image_list, label_list = [], []
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         image = item["image"]
         label = item["label"]
         image_list.append(image)
@@ -251,7 +251,7 @@ def test_cifar100_content_check():
     images, labels = load_cifar(DATA_DIR_100, kind="cifar100")
     num_iter = 0
     # in this example, each dictionary has keys "image", "coarse_label" and "fine_image"
-    for i, d in enumerate(data1.create_dict_iterator()):
+    for i, d in enumerate(data1.create_dict_iterator(num_epochs=1)):
         np.testing.assert_array_equal(d["image"], images[i])
         np.testing.assert_array_equal(d["coarse_label"], labels[i][0])
         np.testing.assert_array_equal(d["fine_label"], labels[i][1])
@@ -268,21 +268,21 @@ def test_cifar100_basic():
     # case 1: test num_samples
     data1 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100)
     num_iter1 = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_iter1 += 1
     assert num_iter1 == 100
 
     # case 2: test repeat
     data1 = data1.repeat(2)
     num_iter2 = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_iter2 += 1
     assert num_iter2 == 200
 
     # case 3: test num_parallel_workers
     data2 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100, num_parallel_workers=1)
     num_iter3 = 0
-    for _ in data2.create_dict_iterator():
+    for _ in data2.create_dict_iterator(num_epochs=1):
         num_iter3 += 1
     assert num_iter3 == 100
 
@@ -294,7 +294,7 @@ def test_cifar100_basic():
     assert data3.get_dataset_size() == 34
     assert data3.get_batch_size() == 3
     num_iter4 = 0
-    for _ in data3.create_dict_iterator():
+    for _ in data3.create_dict_iterator(num_epochs=1):
         num_iter4 += 1
     assert num_iter4 == 34
 
@@ -304,7 +304,7 @@ def test_cifar100_basic():
     assert data4.get_dataset_size() == 33
     assert data4.get_batch_size() == 3
     num_iter5 = 0
-    for _ in data4.create_dict_iterator():
+    for _ in data4.create_dict_iterator(num_epochs=1):
         num_iter5 += 1
     assert num_iter5 == 33
 
@@ -319,7 +319,7 @@ def test_cifar100_pk_sampler():
     data = ds.Cifar100Dataset(DATA_DIR_100, sampler=sampler)
     num_iter = 0
     label_list = []
-    for item in data.create_dict_iterator():
+    for item in data.create_dict_iterator(num_epochs=1):
         label_list.append(item["coarse_label"])
         num_iter += 1
     np.testing.assert_array_equal(golden, label_list)
@@ -375,7 +375,7 @@ def test_cifar100_visualize(plot=False):
     data1 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=10, shuffle=False)
     num_iter = 0
     image_list, label_list = [], []
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         image = item["image"]
         coarse_label = item["coarse_label"]
         fine_label = item["fine_label"]

tests/ut/python/dataset/test_datasets_clue.py

@@ -26,7 +26,7 @@ def test_clue():
     data = ds.CLUEDataset(TRAIN_FILE, task='AFQMC', usage='train', shuffle=False)
     data = data.repeat(2)
     data = data.skip(3)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'label': d['label'].item().decode("utf8"),
             'sentence1': d['sentence1'].item().decode("utf8"),
@@ -43,7 +43,7 @@ def test_clue_num_shards():
 
     buffer = []
     data = ds.CLUEDataset(TRAIN_FILE, task='AFQMC', usage='train', num_shards=3, shard_id=1)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'label': d['label'].item().decode("utf8"),
             'sentence1': d['sentence1'].item().decode("utf8"),
@@ -60,7 +60,7 @@ def test_clue_num_samples():
 
     data = ds.CLUEDataset(TRAIN_FILE, task='AFQMC', usage='train', num_samples=2)
     count = 0
-    for _ in data.create_dict_iterator():
+    for _ in data.create_dict_iterator(num_epochs=1):
         count += 1
     assert count == 2
 
@@ -87,7 +87,7 @@ def test_clue_afqmc():
     # train
     buffer = []
     data = ds.CLUEDataset(TRAIN_FILE, task='AFQMC', usage='train', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'label': d['label'].item().decode("utf8"),
             'sentence1': d['sentence1'].item().decode("utf8"),
@@ -98,7 +98,7 @@ def test_clue_afqmc():
     # test
     buffer = []
     data = ds.CLUEDataset(TEST_FILE, task='AFQMC', usage='test', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'id': d['id'],
             'sentence1': d['sentence1'].item().decode("utf8"),
@@ -109,7 +109,7 @@ def test_clue_afqmc():
     # evaluation
     buffer = []
     data = ds.CLUEDataset(EVAL_FILE, task='AFQMC', usage='eval', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'label': d['label'].item().decode("utf8"),
             'sentence1': d['sentence1'].item().decode("utf8"),
@@ -129,7 +129,7 @@ def test_clue_cmnli():
     # train
     buffer = []
     data = ds.CLUEDataset(TRAIN_FILE, task='CMNLI', usage='train', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'label': d['label'].item().decode("utf8"),
             'sentence1': d['sentence1'].item().decode("utf8"),
@@ -140,7 +140,7 @@ def test_clue_cmnli():
     # test
     buffer = []
     data = ds.CLUEDataset(TEST_FILE, task='CMNLI', usage='test', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'id': d['id'],
             'sentence1': d['sentence1'],
@@ -151,7 +151,7 @@ def test_clue_cmnli():
     # eval
     buffer = []
     data = ds.CLUEDataset(EVAL_FILE, task='CMNLI', usage='eval', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'label': d['label'],
             'sentence1': d['sentence1'],
@@ -171,7 +171,7 @@ def test_clue_csl():
     # train
     buffer = []
     data = ds.CLUEDataset(TRAIN_FILE, task='CSL', usage='train', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'id': d['id'],
             'abst': d['abst'].item().decode("utf8"),
@@ -183,7 +183,7 @@ def test_clue_csl():
     # test
     buffer = []
     data = ds.CLUEDataset(TEST_FILE, task='CSL', usage='test', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'id': d['id'],
             'abst': d['abst'].item().decode("utf8"),
@@ -194,7 +194,7 @@ def test_clue_csl():
     # eval
     buffer = []
     data = ds.CLUEDataset(EVAL_FILE, task='CSL', usage='eval', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'id': d['id'],
             'abst': d['abst'].item().decode("utf8"),
@@ -215,7 +215,7 @@ def test_clue_iflytek():
     # train
     buffer = []
     data = ds.CLUEDataset(TRAIN_FILE, task='IFLYTEK', usage='train', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'label': d['label'].item().decode("utf8"),
             'label_des': d['label_des'].item().decode("utf8"),
@@ -226,7 +226,7 @@ def test_clue_iflytek():
     # test
     buffer = []
     data = ds.CLUEDataset(TEST_FILE, task='IFLYTEK', usage='test', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'id': d['id'],
             'sentence': d['sentence'].item().decode("utf8")
@@ -236,7 +236,7 @@ def test_clue_iflytek():
     # eval
     buffer = []
     data = ds.CLUEDataset(EVAL_FILE, task='IFLYTEK', usage='eval', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'label': d['label'].item().decode("utf8"),
             'label_des': d['label_des'].item().decode("utf8"),
@@ -256,7 +256,7 @@ def test_clue_tnews():
     # train
     buffer = []
     data = ds.CLUEDataset(TRAIN_FILE, task='TNEWS', usage='train', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'label': d['label'].item().decode("utf8"),
             'label_desc': d['label_desc'].item().decode("utf8"),
@@ -269,7 +269,7 @@ def test_clue_tnews():
     # test
     buffer = []
     data = ds.CLUEDataset(TEST_FILE, task='TNEWS', usage='test', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'id': d['id'],
             'sentence': d['sentence'].item().decode("utf8"),
@@ -281,7 +281,7 @@ def test_clue_tnews():
     # eval
     buffer = []
     data = ds.CLUEDataset(EVAL_FILE, task='TNEWS', usage='eval', shuffle=False)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'label': d['label'].item().decode("utf8"),
             'label_desc': d['label_desc'].item().decode("utf8"),
@@ -303,7 +303,7 @@ def test_clue_wsc():
     # train
     buffer = []
     data = ds.CLUEDataset(TRAIN_FILE, task='WSC', usage='train')
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'span1_index': d['span1_index'],
             'span2_index': d['span2_index'],
@@ -318,7 +318,7 @@ def test_clue_wsc():
     # test
     buffer = []
     data = ds.CLUEDataset(TEST_FILE, task='WSC', usage='test')
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'span1_index': d['span1_index'],
             'span2_index': d['span2_index'],
@@ -332,7 +332,7 @@ def test_clue_wsc():
     # eval
     buffer = []
     data = ds.CLUEDataset(EVAL_FILE, task='WSC', usage='eval')
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append({
             'span1_index': d['span1_index'],
             'span2_index': d['span2_index'],

tests/ut/python/dataset/test_datasets_coco.py

@@ -32,7 +32,7 @@ def test_coco_detection():
     image_shape = []
     bbox = []
     category_id = []
-    for data in data1.create_dict_iterator():
+    for data in data1.create_dict_iterator(num_epochs=1):
         image_shape.append(data["image"].shape)
         bbox.append(data["bbox"])
         category_id.append(data["category_id"])
@@ -64,7 +64,7 @@ def test_coco_stuff():
     image_shape = []
     segmentation = []
     iscrowd = []
-    for data in data1.create_dict_iterator():
+    for data in data1.create_dict_iterator(num_epochs=1):
         image_shape.append(data["image"].shape)
         segmentation.append(data["segmentation"])
         iscrowd.append(data["iscrowd"])
@@ -104,7 +104,7 @@ def test_coco_keypoint():
     image_shape = []
     keypoints = []
     num_keypoints = []
-    for data in data1.create_dict_iterator():
+    for data in data1.create_dict_iterator(num_epochs=1):
         image_shape.append(data["image"].shape)
         keypoints.append(data["keypoints"])
         num_keypoints.append(data["num_keypoints"])
@@ -132,7 +132,7 @@ def test_coco_panoptic():
     category_id = []
     iscrowd = []
     area = []
-    for data in data1.create_dict_iterator():
+    for data in data1.create_dict_iterator(num_epochs=1):
         image_shape.append(data["image"].shape)
         bbox.append(data["bbox"])
         category_id.append(data["category_id"])
@@ -175,7 +175,7 @@ def test_coco_case_0():
     data1 = data1.shuffle(10)
     data1 = data1.batch(3, pad_info={})
     num_iter = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 2
 
@@ -186,11 +186,11 @@ def test_coco_case_1():
     dataset1, dataset2 = data1.split(sizes=sizes, randomize=randomize)
 
     num_iter = 0
-    for _ in dataset1.create_dict_iterator():
+    for _ in dataset1.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 3
     num_iter = 0
-    for _ in dataset2.create_dict_iterator():
+    for _ in dataset2.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 3
 

tests/ut/python/dataset/test_datasets_csv.py

@@ -33,7 +33,7 @@ def test_csv_dataset_basic():
         shuffle=False)
     data = data.repeat(2)
     data = data.skip(2)
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append(d)
     assert len(buffer) == 4
 
@@ -45,7 +45,7 @@ def test_csv_dataset_one_file():
         column_names=['col1', 'col2', 'col3', 'col4'],
         shuffle=False)
     buffer = []
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append(d)
     assert len(buffer) == 3
 
@@ -58,7 +58,7 @@ def test_csv_dataset_all_file():
         column_names=['col1', 'col2', 'col3', 'col4'],
         shuffle=False)
     buffer = []
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.append(d)
     assert len(buffer) == 10
 
@@ -70,7 +70,7 @@ def test_csv_dataset_num_samples():
         column_names=['col1', 'col2', 'col3', 'col4'],
         shuffle=False, num_samples=2)
     count = 0
-    for _ in data.create_dict_iterator():
+    for _ in data.create_dict_iterator(num_epochs=1):
         count += 1
     assert count == 2
 
@@ -83,7 +83,7 @@ def test_csv_dataset_distribution():
         column_names=['col1', 'col2', 'col3', 'col4'],
         shuffle=False, num_shards=2, shard_id=0)
     count = 0
-    for _ in data.create_dict_iterator():
+    for _ in data.create_dict_iterator(num_epochs=1):
         count += 1
     assert count == 2
 
@@ -96,7 +96,7 @@ def test_csv_dataset_quoted():
         column_names=['col1', 'col2', 'col3', 'col4'],
         shuffle=False)
     buffer = []
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.extend([d['col1'].item().decode("utf8"),
                        d['col2'].item().decode("utf8"),
                        d['col3'].item().decode("utf8"),
@@ -113,7 +113,7 @@ def test_csv_dataset_separated():
         column_names=['col1', 'col2', 'col3', 'col4'],
         shuffle=False)
     buffer = []
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.extend([d['col1'].item().decode("utf8"),
                        d['col2'].item().decode("utf8"),
                        d['col3'].item().decode("utf8"),
@@ -129,7 +129,7 @@ def test_csv_dataset_embedded():
         column_names=['col1', 'col2', 'col3', 'col4'],
         shuffle=False)
     buffer = []
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.extend([d['col1'].item().decode("utf8"),
                        d['col2'].item().decode("utf8"),
                        d['col3'].item().decode("utf8"),
@@ -145,7 +145,7 @@ def test_csv_dataset_chinese():
         column_names=['col1', 'col2', 'col3', 'col4', 'col5'],
         shuffle=False)
     buffer = []
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.extend([d['col1'].item().decode("utf8"),
                        d['col2'].item().decode("utf8"),
                        d['col3'].item().decode("utf8"),
@@ -161,7 +161,7 @@ def test_csv_dataset_header():
         column_defaults=["", "", "", ""],
         shuffle=False)
     buffer = []
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.extend([d['col1'].item().decode("utf8"),
                        d['col2'].item().decode("utf8"),
                        d['col3'].item().decode("utf8"),
@@ -177,7 +177,7 @@ def test_csv_dataset_number():
         column_names=['col1', 'col2', 'col3', 'col4'],
         shuffle=False)
     buffer = []
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         buffer.extend([d['col1'].item(),
                        d['col2'].item(),
                        d['col3'].item(),
@@ -203,7 +203,7 @@ def test_csv_dataset_exception():
         column_names=['col1', 'col2', 'col3', 'col4'],
         shuffle=False)
     with pytest.raises(Exception) as err:
-        for _ in data.create_dict_iterator():
+        for _ in data.create_dict_iterator(num_epochs=1):
             pass
     assert "Failed to parse file" in str(err.value)
 
@@ -216,7 +216,7 @@ def test_csv_dataset_type_error():
         column_names=['col1', 'col2', 'col3', 'col4'],
         shuffle=False)
     with pytest.raises(Exception) as err:
-        for _ in data.create_dict_iterator():
+        for _ in data.create_dict_iterator(num_epochs=1):
             pass
     assert "type does not match" in str(err.value)
 

tests/ut/python/dataset/test_datasets_generator.py

@@ -46,7 +46,7 @@ def test_generator_0():
     data1 = ds.GeneratorDataset(generator_1d, ["data"])
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([i])
         np.testing.assert_array_equal(item["data"], golden)
         i = i + 1
@@ -68,7 +68,7 @@ def test_generator_1():
     data1 = ds.GeneratorDataset(generator_md, ["data"])
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([[i, i + 1], [i + 2, i + 3]])
         np.testing.assert_array_equal(item["data"], golden)
         i = i + 1
@@ -90,7 +90,7 @@ def test_generator_2():
     data1 = ds.GeneratorDataset(generator_mc, ["col0", "col1"])
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([i])
         np.testing.assert_array_equal(item["col0"], golden)
         golden = np.array([[i, i + 1], [i + 2, i + 3]])
@@ -110,7 +110,7 @@ def test_generator_3():
     data1 = data1.repeat(4)
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([i])
         np.testing.assert_array_equal(item["data"], golden)
         i = i + 1
@@ -130,7 +130,7 @@ def test_generator_4():
     data1 = data1.batch(4)
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([[i], [i + 1], [i + 2], [i + 3]])
         np.testing.assert_array_equal(item["data"], golden)
         i = i + 4
@@ -150,7 +150,7 @@ def type_tester(t):
     data1 = data1.batch(4)
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([[i], [i + 1], [i + 2], [i + 3]], dtype=t)
         np.testing.assert_array_equal(item["data"], golden)
         i = i + 4
@@ -177,7 +177,7 @@ def type_tester_with_type_check(t, c):
     data1 = data1.batch(4)
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([[i], [i + 1], [i + 2], [i + 3]], dtype=t)
         np.testing.assert_array_equal(item["data"], golden)
         i = i + 4
@@ -212,7 +212,7 @@ def type_tester_with_type_check_2c(t, c):
     data1 = data1.batch(4)
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([[i], [i + 1], [i + 2], [i + 3]], dtype=t)
         np.testing.assert_array_equal(item["data0"], golden)
         i = i + 4
@@ -249,7 +249,7 @@ def test_generator_8():
                       num_parallel_workers=2)
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([i * 3])
         np.testing.assert_array_equal(item["out0"], golden)
         golden = np.array([[i * 7, (i + 1) * 7], [(i + 2) * 7, (i + 3) * 7]])
@@ -303,7 +303,7 @@ def test_generator_10():
 
     # Expected column order is |col0|out1|out2|
     i = 0
-    for item in data1.create_tuple_iterator():
+    for item in data1.create_tuple_iterator(num_epochs=1):
         golden = np.array([i])
         np.testing.assert_array_equal(item[0], golden)
         golden = np.array([[i, i + 1], [i + 2, i + 3]])
@@ -327,7 +327,7 @@ def test_generator_11():
 
     # Expected column order is |out1|out2|
     i = 0
-    for item in data1.create_tuple_iterator():
+    for item in data1.create_tuple_iterator(num_epochs=1):
         # len should be 2 because col0 is dropped (not included in columns_order)
         assert len(item) == 2
         golden = np.array([[i, i + 1], [i + 2, i + 3]])
@@ -349,7 +349,7 @@ def test_generator_12():
 
     # Expected column order is |col0|col1|
     i = 0
-    for item in data1.create_tuple_iterator():
+    for item in data1.create_tuple_iterator(num_epochs=1):
         assert len(item) == 2
         golden = np.array([i * 5])
         np.testing.assert_array_equal(item[0], golden)
@@ -362,7 +362,7 @@ def test_generator_12():
 
     # Expected column order is |col0|col1|
     i = 0
-    for item in data1.create_tuple_iterator():
+    for item in data1.create_tuple_iterator(num_epochs=1):
         assert len(item) == 2
         golden = np.array([i * 5])
         np.testing.assert_array_equal(item[1], golden)
@@ -383,7 +383,7 @@ def test_generator_13():
 
     # Expected column order is |out0|col1|
     i = 0
-    for item in data1.create_tuple_iterator():
+    for item in data1.create_tuple_iterator(num_epochs=1):
         assert len(item) == 2
         golden = np.array([i * 5])
         np.testing.assert_array_equal(item[0], golden)
@@ -391,7 +391,7 @@ def test_generator_13():
         np.testing.assert_array_equal(item[1], golden)
         i = i + 1
 
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # len should be 2 because col0 is dropped (not included in columns_order)
         assert len(item) == 2
         golden = np.array([i * 5])
@@ -410,7 +410,7 @@ def test_generator_14():
     source = [(np.array([x]),) for x in range(256)]
     ds1 = ds.GeneratorDataset(source, ["data"], sampler=ds.SequentialSampler(), num_parallel_workers=4).repeat(2)
     i = 0
-    for data in ds1.create_dict_iterator():  # each data is a dictionary
+    for data in ds1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([i])
         np.testing.assert_array_equal(data["data"], golden)
         i = i + 1
@@ -428,7 +428,7 @@ def test_generator_15():
     source = [(np.array([x]),) for x in range(256)]
     ds1 = ds.GeneratorDataset(source, ["data"], sampler=sampler, num_parallel_workers=4).repeat(2)
     i = 0
-    for data in ds1.create_dict_iterator():  # each data is a dictionary
+    for data in ds1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([i])
         np.testing.assert_array_equal(data["data"], golden)
         i = i + 1
@@ -447,7 +447,7 @@ def test_generator_16():
     data1 = ds.GeneratorDataset(source, ["col0", "col1"], sampler=ds.SequentialSampler())
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([i])
         np.testing.assert_array_equal(item["col0"], golden)
         golden = np.array([i + 1])
@@ -467,7 +467,7 @@ def test_generator_17():
     data1 = ds.GeneratorDataset(source, ["col0", "col1"], sampler=sampler)
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([i])
         np.testing.assert_array_equal(item["col0"], golden)
         golden = np.array([i + 1])
@@ -527,7 +527,7 @@ def test_generator_sequential_sampler():
     source = [(np.array([x]),) for x in range(64)]
     ds1 = ds.GeneratorDataset(source, ["data"], sampler=ds.SequentialSampler())
     i = 0
-    for data in ds1.create_dict_iterator():  # each data is a dictionary
+    for data in ds1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([i])
         np.testing.assert_array_equal(data["data"], golden)
         i = i + 1
@@ -536,7 +536,7 @@ def test_generator_sequential_sampler():
 def test_generator_random_sampler():
     source = [(np.array([x]),) for x in range(64)]
     ds1 = ds.GeneratorDataset(source, ["data"], shuffle=True)
-    for _ in ds1.create_dict_iterator():  # each data is a dictionary
+    for _ in ds1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         pass
 
 
@@ -545,7 +545,7 @@ def test_generator_distributed_sampler():
     for sid in range(8):
         ds1 = ds.GeneratorDataset(source, ["data"], shuffle=False, num_shards=8, shard_id=sid)
         i = sid
-        for data in ds1.create_dict_iterator():  # each data is a dictionary
+        for data in ds1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
             golden = np.array([i])
             np.testing.assert_array_equal(data["data"], golden)
             i = i + 8
@@ -559,17 +559,17 @@ def test_generator_num_samples():
     ds3 = ds.GeneratorDataset(generator_1d, ["data"], num_samples=num_samples)
 
     count = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         count = count + 1
     assert count == num_samples
 
     count = 0
-    for _ in ds2.create_dict_iterator():
+    for _ in ds2.create_dict_iterator(num_epochs=1):
         count = count + 1
     assert count == num_samples
 
     count = 0
-    for _ in ds3.create_dict_iterator():
+    for _ in ds3.create_dict_iterator(num_epochs=1):
         count = count + 1
     assert count == num_samples
 
@@ -581,12 +581,12 @@ def test_generator_num_samples_underflow():
     ds3 = ds.GeneratorDataset(generator_1d, ["data"], num_samples=num_samples)
 
     count = 0
-    for _ in ds2.create_dict_iterator():
+    for _ in ds2.create_dict_iterator(num_epochs=1):
         count = count + 1
     assert count == 64
 
     count = 0
-    for _ in ds3.create_dict_iterator():
+    for _ in ds3.create_dict_iterator(num_epochs=1):
         count = count + 1
     assert count == 64
 
@@ -604,7 +604,7 @@ def type_tester_with_type_check_2c_schema(t, c):
     data1 = data1.batch(4)
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([[i], [i + 1], [i + 2], [i + 3]], dtype=t)
         np.testing.assert_array_equal(item["data0"], golden)
         i = i + 4
@@ -635,7 +635,7 @@ def test_generator_dataset_size_0():
     data_size = data1.get_dataset_size()
 
     num_rows = 0
-    for _ in data1.create_dict_iterator():  # each data is a dictionary
+    for _ in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         num_rows = num_rows + 1
     assert data_size == num_rows
 
@@ -652,7 +652,7 @@ def test_generator_dataset_size_1():
     data_size = data1.get_dataset_size()
 
     num_rows = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_rows = num_rows + 1
     assert data_size == num_rows
 
@@ -669,7 +669,7 @@ def test_generator_dataset_size_2():
     data_size = data1.get_dataset_size()
 
     num_rows = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_rows = num_rows + 1
     assert data_size == num_rows
 
@@ -686,7 +686,7 @@ def test_generator_dataset_size_3():
     data_size = data1.get_dataset_size()
 
     num_rows = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_rows += 1
     assert data_size == num_rows
 
@@ -702,7 +702,7 @@ def test_generator_dataset_size_4():
     data_size = data1.get_dataset_size()
 
     num_rows = 0
-    for _ in data1.create_dict_iterator():  # each data is a dictionary
+    for _ in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         num_rows = num_rows + 1
     assert data_size == num_rows
 
@@ -716,7 +716,7 @@ def test_generator_dataset_size_5():
     data1 = ds.GeneratorDataset(dataset_generator, ["data"], num_shards=3, shard_id=0)
 
     num_rows = 0
-    for _ in data1.create_dict_iterator():  # each data is a dictionary
+    for _ in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         num_rows = num_rows + 1
     data_size = data1.get_dataset_size()
     assert data_size == num_rows
@@ -737,7 +737,7 @@ def manual_test_generator_keyboard_interrupt():
             return 1024
 
     ds1 = ds.GeneratorDataset(MyDS(), ["data"], num_parallel_workers=4).repeat(2)
-    for _ in ds1.create_dict_iterator():  # each data is a dictionary
+    for _ in ds1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         pass
 
 

tests/ut/python/dataset/test_datasets_imagefolder.py

@@ -28,7 +28,7 @@ def test_imagefolder_basic():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -48,7 +48,7 @@ def test_imagefolder_numsamples():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -61,7 +61,7 @@ def test_imagefolder_numsamples():
     data1 = ds.ImageFolderDatasetV2(DATA_DIR, num_parallel_workers=2, sampler=random_sampler)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         num_iter += 1
 
     assert num_iter == 3
@@ -70,7 +70,7 @@ def test_imagefolder_numsamples():
     data1 = ds.ImageFolderDatasetV2(DATA_DIR, num_parallel_workers=2, sampler=random_sampler)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         num_iter += 1
 
     assert num_iter == 3
@@ -86,7 +86,7 @@ def test_imagefolder_numshards():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -106,7 +106,7 @@ def test_imagefolder_shardid():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -126,7 +126,7 @@ def test_imagefolder_noshuffle():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -147,7 +147,7 @@ def test_imagefolder_extrashuffle():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -171,7 +171,7 @@ def test_imagefolder_classindex():
               333, 333, 333, 333, 333, 333, 333, 333, 333, 333, 333]
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -196,7 +196,7 @@ def test_imagefolder_negative_classindex():
               -333, -333, -333, -333, -333, -333, -333, -333, -333, -333, -333]
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -218,7 +218,7 @@ def test_imagefolder_extensions():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -239,7 +239,7 @@ def test_imagefolder_decode():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -267,7 +267,7 @@ def test_sequential_sampler():
 
     result = []
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         result.append(item["label"])
         num_iter += 1
@@ -287,7 +287,7 @@ def test_random_sampler():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -308,7 +308,7 @@ def test_distributed_sampler():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -329,7 +329,7 @@ def test_pk_sampler():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -351,7 +351,7 @@ def test_subset_random_sampler():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -373,7 +373,7 @@ def test_weighted_random_sampler():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -393,7 +393,7 @@ def test_imagefolder_rename():
     data1 = data1.repeat(repeat_count)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
@@ -405,7 +405,7 @@ def test_imagefolder_rename():
     data1 = data1.rename(input_columns=["image"], output_columns="image2")
 
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image2"]))
         logger.info("label is {}".format(item["label"]))
@@ -430,7 +430,7 @@ def test_imagefolder_zip():
     data3 = ds.zip((data1, data2))
 
     num_iter = 0
-    for item in data3.create_dict_iterator():  # each data is a dictionary
+    for item in data3.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))

tests/ut/python/dataset/test_datasets_manifestop.py

@@ -26,7 +26,7 @@ def test_manifest_dataset_train():
     count = 0
     cat_count = 0
     dog_count = 0
-    for item in data.create_dict_iterator():
+    for item in data.create_dict_iterator(num_epochs=1):
         logger.info("item[image] is {}".format(item["image"]))
         count = count + 1
         if item["label"].size == 1 and item["label"] == 0:
@@ -41,7 +41,7 @@ def test_manifest_dataset_train():
 def test_manifest_dataset_eval():
     data = ds.ManifestDataset(DATA_FILE, "eval", decode=True)
     count = 0
-    for item in data.create_dict_iterator():
+    for item in data.create_dict_iterator(num_epochs=1):
         logger.info("item[image] is {}".format(item["image"]))
         count = count + 1
         if item["label"] != 0 and item["label"] != 1:
@@ -55,7 +55,7 @@ def test_manifest_dataset_class_index():
     out_class_indexing = data.get_class_indexing()
     assert out_class_indexing == {"dog": 11}
     count = 0
-    for item in data.create_dict_iterator():
+    for item in data.create_dict_iterator(num_epochs=1):
         logger.info("item[image] is {}".format(item["image"]))
         count = count + 1
         if item["label"] != 11:
@@ -71,7 +71,7 @@ def test_manifest_dataset_get_class_index():
     class_indexing = data.get_class_indexing()
     assert class_indexing == {'cat': 0, 'dog': 1, 'flower': 2}
     count = 0
-    for item in data.create_dict_iterator():
+    for item in data.create_dict_iterator(num_epochs=1):
         logger.info("item[image] is {}".format(item["image"]))
         count = count + 1
     assert count == 4
@@ -81,7 +81,7 @@ def test_manifest_dataset_multi_label():
     data = ds.ManifestDataset(DATA_FILE, decode=True, shuffle=False)
     count = 0
     expect_label = [1, 0, 0, [0, 2]]
-    for item in data.create_dict_iterator():
+    for item in data.create_dict_iterator(num_epochs=1):
         assert item["label"].tolist() == expect_label[count]
         logger.info("item[image] is {}".format(item["image"]))
         count = count + 1
@@ -107,7 +107,7 @@ def test_manifest_dataset_multi_label_onehot():
     data = data.map(input_columns=["label"], operations=multi_label_hot)
     data = data.batch(2)
     count = 0
-    for item in data.create_dict_iterator():
+    for item in data.create_dict_iterator(num_epochs=1):
         assert item["label"].tolist() == expect_label[count]
         logger.info("item[image] is {}".format(item["image"]))
         count = count + 1

tests/ut/python/dataset/test_datasets_mnist.py

@@ -64,7 +64,7 @@ def test_mnist_content_check():
     num_iter = 0
     # in this example, each dictionary has keys "image" and "label"
     image_list, label_list = [], []
-    for i, data in enumerate(data1.create_dict_iterator()):
+    for i, data in enumerate(data1.create_dict_iterator(num_epochs=1)):
         image_list.append(data["image"])
         label_list.append("label {}".format(data["label"]))
         np.testing.assert_array_equal(data["image"], images[i])
@@ -82,14 +82,14 @@ def test_mnist_basic():
     # case 1: test loading whole dataset
     data1 = ds.MnistDataset(DATA_DIR)
     num_iter1 = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_iter1 += 1
     assert num_iter1 == 10000
 
     # case 2: test num_samples
     data2 = ds.MnistDataset(DATA_DIR, num_samples=500)
     num_iter2 = 0
-    for _ in data2.create_dict_iterator():
+    for _ in data2.create_dict_iterator(num_epochs=1):
         num_iter2 += 1
     assert num_iter2 == 500
 
@@ -97,7 +97,7 @@ def test_mnist_basic():
     data3 = ds.MnistDataset(DATA_DIR, num_samples=200)
     data3 = data3.repeat(5)
     num_iter3 = 0
-    for _ in data3.create_dict_iterator():
+    for _ in data3.create_dict_iterator(num_epochs=1):
         num_iter3 += 1
     assert num_iter3 == 1000
 
@@ -109,7 +109,7 @@ def test_mnist_basic():
     assert data4.get_dataset_size() == 15
     assert data4.get_batch_size() == 7
     num_iter4 = 0
-    for _ in data4.create_dict_iterator():
+    for _ in data4.create_dict_iterator(num_epochs=1):
         num_iter4 += 1
     assert num_iter4 == 15
 
@@ -121,7 +121,7 @@ def test_mnist_basic():
     assert data5.get_dataset_size() == 14
     assert data5.get_batch_size() == 7
     num_iter5 = 0
-    for _ in data5.create_dict_iterator():
+    for _ in data5.create_dict_iterator(num_epochs=1):
         num_iter5 += 1
     assert num_iter5 == 14
 
@@ -137,7 +137,7 @@ def test_mnist_pk_sampler():
     data = ds.MnistDataset(DATA_DIR, sampler=sampler)
     num_iter = 0
     label_list = []
-    for item in data.create_dict_iterator():
+    for item in data.create_dict_iterator(num_epochs=1):
         label_list.append(item["label"])
         num_iter += 1
     np.testing.assert_array_equal(golden, label_list)
@@ -155,7 +155,7 @@ def test_mnist_sequential_sampler():
     data2 = ds.MnistDataset(DATA_DIR, shuffle=False, num_samples=num_samples)
     label_list1, label_list2 = [], []
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         label_list1.append(item1["label"])
         label_list2.append(item2["label"])
         num_iter += 1
@@ -214,7 +214,7 @@ def test_mnist_visualize(plot=False):
     data1 = ds.MnistDataset(DATA_DIR, num_samples=10, shuffle=False)
     num_iter = 0
     image_list, label_list = [], []
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         image = item["image"]
         label = item["label"]
         image_list.append(image)

tests/ut/python/dataset/test_datasets_sharding.py

@@ -25,7 +25,7 @@ def test_imagefolder_shardings(print_res=False):
                                         shuffle=shuffle, class_indexing=class_index, decode=True)
         data1 = data1.repeat(repeat_cnt)
         res = []
-        for item in data1.create_dict_iterator():  # each data is a dictionary
+        for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
             res.append(item["label"].item())
         if print_res:
             logger.info("labels of dataset: {}".format(res))
@@ -59,7 +59,7 @@ def test_tfrecord_shardings1(print_res=False):
                                    shuffle=ds.Shuffle.FILES, num_parallel_workers=1)
         data1 = data1.repeat(repeat_cnt)
         res = []
-        for item in data1.create_dict_iterator():  # each data is a dictionary
+        for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
             res.append(item["scalars"][0])
         if print_res:
             logger.info("scalars of dataset: {}".format(res))
@@ -97,7 +97,7 @@ def test_tfrecord_shardings4(print_res=False):
                                    shuffle=ds.Shuffle.FILES, num_parallel_workers=4)
         data1 = data1.repeat(repeat_cnt)
         res = []
-        for item in data1.create_dict_iterator():  # each data is a dictionary
+        for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
             res.append(item["scalars"][0])
         if print_res:
             logger.info("scalars of dataset: {}".format(res))
@@ -141,7 +141,7 @@ def test_manifest_shardings(print_res=False):
                                    shuffle=shuffle, decode=True)
         data1 = data1.repeat(repeat_cnt)
         res = []
-        for item in data1.create_dict_iterator():  # each data is a dictionary
+        for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
             res.append(item["label"].item())
         if print_res:
             logger.info("labels of dataset: {}".format(res))
@@ -166,7 +166,7 @@ def test_voc_shardings(print_res=False):
         data1 = ds.VOCDataset(voc_dir, decode=True, sampler=sampler)
         data1 = data1.repeat(repeat_cnt)
         res = []
-        for item in data1.create_dict_iterator():  # each data is a dictionary
+        for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
             res.append(item["image"].shape[0])
         if print_res:
             logger.info("labels of dataset: {}".format(res))
@@ -194,7 +194,7 @@ def test_cifar10_shardings(print_res=False):
                                   shuffle=shuffle)
         data1 = data1.repeat(repeat_cnt)
         res = []
-        for item in data1.create_dict_iterator():  # each data is a dictionary
+        for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
             res.append(item["label"].item())
         if print_res:
             logger.info("labels of dataset: {}".format(res))
@@ -214,7 +214,7 @@ def test_cifar100_shardings(print_res=False):
                                    shuffle=shuffle)
         data1 = data1.repeat(repeat_cnt)
         res = []
-        for item in data1.create_dict_iterator():  # each data is a dictionary
+        for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
             res.append(item["coarse_label"].item())
         if print_res:
             logger.info("labels of dataset: {}".format(res))
@@ -233,7 +233,7 @@ def test_mnist_shardings(print_res=False):
                                 shuffle=shuffle)
         data1 = data1.repeat(repeat_cnt)
         res = []
-        for item in data1.create_dict_iterator():  # each data is a dictionary
+        for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
             res.append(item["label"].item())
         if print_res:
             logger.info("labels of dataset: {}".format(res))

tests/ut/python/dataset/test_datasets_textfileop.py

@@ -25,7 +25,7 @@ DATA_ALL_FILE = "../data/dataset/testTextFileDataset/*"
 def test_textline_dataset_one_file():
     data = ds.TextFileDataset(DATA_FILE)
     count = 0
-    for i in data.create_dict_iterator():
+    for i in data.create_dict_iterator(num_epochs=1):
         logger.info("{}".format(i["text"]))
         count += 1
     assert count == 3
@@ -34,7 +34,7 @@ def test_textline_dataset_one_file():
 def test_textline_dataset_all_file():
     data = ds.TextFileDataset(DATA_ALL_FILE)
     count = 0
-    for i in data.create_dict_iterator():
+    for i in data.create_dict_iterator(num_epochs=1):
         logger.info("{}".format(i["text"]))
         count += 1
     assert count == 5
@@ -43,7 +43,7 @@ def test_textline_dataset_all_file():
 def test_textline_dataset_num_samples_zero():
     data = ds.TextFileDataset(DATA_FILE, num_samples=0)
     count = 0
-    for i in data.create_dict_iterator():
+    for i in data.create_dict_iterator(num_epochs=1):
         logger.info("{}".format(i["text"]))
         count += 1
     assert count == 3
@@ -56,7 +56,7 @@ def test_textline_dataset_shuffle_false4():
     count = 0
     line = ["This is a text file.", "Another file.",
             "Be happy every day.", "End of file.", "Good luck to everyone."]
-    for i in data.create_dict_iterator():
+    for i in data.create_dict_iterator(num_epochs=1):
         strs = i["text"].item().decode("utf8")
         assert strs == line[count]
         count += 1
@@ -73,7 +73,7 @@ def test_textline_dataset_shuffle_false1():
     count = 0
     line = ["This is a text file.", "Be happy every day.", "Good luck to everyone.",
             "Another file.", "End of file."]
-    for i in data.create_dict_iterator():
+    for i in data.create_dict_iterator(num_epochs=1):
         strs = i["text"].item().decode("utf8")
         assert strs == line[count]
         count += 1
@@ -90,7 +90,7 @@ def test_textline_dataset_shuffle_files4():
     count = 0
     line = ["This is a text file.", "Another file.",
             "Be happy every day.", "End of file.", "Good luck to everyone."]
-    for i in data.create_dict_iterator():
+    for i in data.create_dict_iterator(num_epochs=1):
         strs = i["text"].item().decode("utf8")
         assert strs == line[count]
         count += 1
@@ -107,7 +107,7 @@ def test_textline_dataset_shuffle_files1():
     count = 0
     line = ["This is a text file.", "Be happy every day.", "Good luck to everyone.",
             "Another file.", "End of file."]
-    for i in data.create_dict_iterator():
+    for i in data.create_dict_iterator(num_epochs=1):
         strs = i["text"].item().decode("utf8")
         assert strs == line[count]
         count += 1
@@ -124,7 +124,7 @@ def test_textline_dataset_shuffle_global4():
     count = 0
     line = ["Another file.", "Good luck to everyone.", "End of file.",
             "This is a text file.", "Be happy every day."]
-    for i in data.create_dict_iterator():
+    for i in data.create_dict_iterator(num_epochs=1):
         strs = i["text"].item().decode("utf8")
         assert strs == line[count]
         count += 1
@@ -141,7 +141,7 @@ def test_textline_dataset_shuffle_global1():
     count = 0
     line = ["Another file.", "Good luck to everyone.", "This is a text file.",
             "End of file.", "Be happy every day."]
-    for i in data.create_dict_iterator():
+    for i in data.create_dict_iterator(num_epochs=1):
         strs = i["text"].item().decode("utf8")
         assert strs == line[count]
         count += 1
@@ -154,7 +154,7 @@ def test_textline_dataset_shuffle_global1():
 def test_textline_dataset_num_samples():
     data = ds.TextFileDataset(DATA_FILE, num_samples=2)
     count = 0
-    for _ in data.create_dict_iterator():
+    for _ in data.create_dict_iterator(num_epochs=1):
         count += 1
     assert count == 2
 
@@ -162,7 +162,7 @@ def test_textline_dataset_num_samples():
 def test_textline_dataset_distribution():
     data = ds.TextFileDataset(DATA_ALL_FILE, num_shards=2, shard_id=1)
     count = 0
-    for _ in data.create_dict_iterator():
+    for _ in data.create_dict_iterator(num_epochs=1):
         count += 1
     assert count == 3
 
@@ -174,7 +174,7 @@ def test_textline_dataset_repeat():
     line = ["This is a text file.", "Be happy every day.", "Good luck to everyone.",
             "This is a text file.", "Be happy every day.", "Good luck to everyone.",
             "This is a text file.", "Be happy every day.", "Good luck to everyone."]
-    for i in data.create_dict_iterator():
+    for i in data.create_dict_iterator(num_epochs=1):
         strs = i["text"].item().decode("utf8")
         assert strs == line[count]
         count += 1

tests/ut/python/dataset/test_datasets_tfrecord.py

@@ -39,7 +39,7 @@ def test_tfrecord_shape():
     schema_file = "../data/dataset/testTFTestAllTypes/datasetSchemaRank0.json"
     ds1 = ds.TFRecordDataset(FILES, schema_file)
     ds1 = ds1.batch(2)
-    for data in ds1.create_dict_iterator():
+    for data in ds1.create_dict_iterator(num_epochs=1):
         logger.info(data)
     output_shape = ds1.output_shapes()
     assert len(output_shape[-1]) == 1
@@ -51,7 +51,7 @@ def test_tfrecord_read_all_dataset():
     ds1 = ds.TFRecordDataset(FILES, schema_file)
     assert ds1.get_dataset_size() == 12
     count = 0
-    for _ in ds1.create_tuple_iterator():
+    for _ in ds1.create_tuple_iterator(num_epochs=1):
         count += 1
     assert count == 12
 
@@ -62,7 +62,7 @@ def test_tfrecord_num_samples():
     ds1 = ds.TFRecordDataset(FILES, schema_file, num_samples=8)
     assert ds1.get_dataset_size() == 8
     count = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         count += 1
     assert count == 8
 
@@ -73,7 +73,7 @@ def test_tfrecord_num_samples2():
     ds1 = ds.TFRecordDataset(FILES, schema_file)
     assert ds1.get_dataset_size() == 7
     count = 0
-    for _ in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator(num_epochs=1):
         count += 1
     assert count == 7
 
@@ -139,7 +139,7 @@ def test_tfrecord_multi_files():
     data1 = ds.TFRecordDataset(DATA_FILES2, SCHEMA_FILE2, shuffle=False)
     data1 = data1.repeat(1)
     num_iter = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_iter += 1
 
     assert num_iter == 12
@@ -187,7 +187,7 @@ def test_tfrecord_shard():
                                    shuffle=ds.Shuffle.FILES)
         data1 = data1.repeat(num_repeats)
         res = list()
-        for item in data1.create_dict_iterator():
+        for item in data1.create_dict_iterator(num_epochs=1):
             res.append(item["scalars"][0])
         return res
 
@@ -215,7 +215,7 @@ def test_tfrecord_shard_equal_rows():
         ds1 = ds.TFRecordDataset(tf_files, num_shards=num_shards, shard_id=shard_id, shard_equal_rows=True)
         ds1 = ds1.repeat(num_repeats)
         res = list()
-        for data in ds1.create_dict_iterator():
+        for data in ds1.create_dict_iterator(num_epochs=1):
             res.append(data["scalars"][0])
         return res
 
@@ -238,7 +238,7 @@ def test_tfrecord_shard_equal_rows():
 def test_tfrecord_no_schema_columns_list():
     logger.info("test_tfrecord_no_schema_columns_list")
     data = ds.TFRecordDataset(FILES, shuffle=False, columns_list=["col_sint16"])
-    row = data.create_dict_iterator().__next__()
+    row = data.create_dict_iterator(num_epochs=1).__next__()
     assert row["col_sint16"] == [-32768]
 
     with pytest.raises(KeyError) as info:
@@ -258,7 +258,7 @@ def test_tfrecord_schema_columns_list():
     schema.add_column('col_sint32', de_type=mstype.int64, shape=[1])
     schema.add_column('col_sint64', de_type=mstype.int64, shape=[1])
     data = ds.TFRecordDataset(FILES, schema=schema, shuffle=False, columns_list=["col_sint16"])
-    row = data.create_dict_iterator().__next__()
+    row = data.create_dict_iterator(num_epochs=1).__next__()
     assert row["col_sint16"] == [-32768]
 
     with pytest.raises(KeyError) as info:
@@ -275,7 +275,7 @@ def test_tfrecord_invalid_files():
     data = ds.TFRecordDataset(files, SCHEMA_FILE, shuffle=ds.Shuffle.FILES)
 
     with pytest.raises(RuntimeError) as info:
-        _ = data.create_dict_iterator().get_next()
+        _ = data.create_dict_iterator(num_epochs=1).get_next()
     assert "cannot be opened" in str(info.value)
     assert "not valid tfrecord files" in str(info.value)
     assert valid_file not in str(info.value)

tests/ut/python/dataset/test_datasets_voc.py

@@ -23,7 +23,7 @@ TARGET_SHAPE = [680, 680, 680, 680, 642, 607, 561, 596, 612, 680]
 def test_voc_segmentation():
     data1 = ds.VOCDataset(DATA_DIR, task="Segmentation", mode="train", decode=True, shuffle=False)
     num = 0
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         assert item["image"].shape[0] == IMAGE_SHAPE[num]
         assert item["target"].shape[0] == TARGET_SHAPE[num]
         num += 1
@@ -34,7 +34,7 @@ def test_voc_detection():
     data1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
     num = 0
     count = [0, 0, 0, 0, 0, 0]
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         assert item["image"].shape[0] == IMAGE_SHAPE[num]
         for label in item["label"]:
             count[label[0]] += 1
@@ -53,7 +53,7 @@ def test_voc_class_index():
     assert (class_index2 == {'car': 0, 'cat': 1, 'train': 5})
     num = 0
     count = [0, 0, 0, 0, 0, 0]
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         for label in item["label"]:
             count[label[0]] += 1
             assert label[0] in (0, 1, 5)
@@ -71,7 +71,7 @@ def test_voc_get_class_indexing():
     assert (class_index2 == {'car': 0, 'cat': 1, 'chair': 2, 'dog': 3, 'person': 4, 'train': 5})
     num = 0
     count = [0, 0, 0, 0, 0, 0]
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         for label in item["label"]:
             count[label[0]] += 1
             assert label[0] in (0, 1, 2, 3, 4, 5)
@@ -93,7 +93,7 @@ def test_case_0():
     data1 = data1.batch(batch_size, drop_remainder=True)
 
     num = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num += 1
     assert num == 20
 
@@ -110,7 +110,7 @@ def test_case_1():
     data1 = data1.batch(batch_size, drop_remainder=True, pad_info={})
 
     num = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num += 1
     assert num == 18
 
@@ -122,12 +122,12 @@ def test_case_2():
     dataset1, dataset2 = data1.split(sizes=sizes, randomize=randomize)
 
     num_iter = 0
-    for _ in dataset1.create_dict_iterator():
+    for _ in dataset1.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 5
 
     num_iter = 0
-    for _ in dataset2.create_dict_iterator():
+    for _ in dataset2.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 5
 
@@ -135,7 +135,7 @@ def test_case_2():
 def test_voc_exception():
     try:
         data1 = ds.VOCDataset(DATA_DIR, task="InvalidTask", mode="train", decode=True)
-        for _ in data1.create_dict_iterator():
+        for _ in data1.create_dict_iterator(num_epochs=1):
             pass
         assert False
     except ValueError:
@@ -143,7 +143,7 @@ def test_voc_exception():
 
     try:
         data2 = ds.VOCDataset(DATA_DIR, task="Segmentation", mode="train", class_indexing={"cat": 0}, decode=True)
-        for _ in data2.create_dict_iterator():
+        for _ in data2.create_dict_iterator(num_epochs=1):
             pass
         assert False
     except ValueError:
@@ -151,7 +151,7 @@ def test_voc_exception():
 
     try:
         data3 = ds.VOCDataset(DATA_DIR, task="Detection", mode="notexist", decode=True)
-        for _ in data3.create_dict_iterator():
+        for _ in data3.create_dict_iterator(num_epochs=1):
             pass
         assert False
     except ValueError:
@@ -159,7 +159,7 @@ def test_voc_exception():
 
     try:
         data4 = ds.VOCDataset(DATA_DIR, task="Detection", mode="xmlnotexist", decode=True)
-        for _ in data4.create_dict_iterator():
+        for _ in data4.create_dict_iterator(num_epochs=1):
             pass
         assert False
     except RuntimeError:
@@ -167,7 +167,7 @@ def test_voc_exception():
 
     try:
         data5 = ds.VOCDataset(DATA_DIR, task="Detection", mode="invalidxml", decode=True)
-        for _ in data5.create_dict_iterator():
+        for _ in data5.create_dict_iterator(num_epochs=1):
             pass
         assert False
     except RuntimeError:
@@ -175,7 +175,7 @@ def test_voc_exception():
 
     try:
         data6 = ds.VOCDataset(DATA_DIR, task="Detection", mode="xmlnoobject", decode=True)
-        for _ in data6.create_dict_iterator():
+        for _ in data6.create_dict_iterator(num_epochs=1):
             pass
         assert False
     except RuntimeError:

tests/ut/python/dataset/test_decode.py

@@ -40,7 +40,7 @@ def test_decode_op():
 
     # Second dataset
     data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         actual = item1["image"]
         expected = cv2.imdecode(item2["image"], cv2.IMREAD_COLOR)
         expected = cv2.cvtColor(expected, cv2.COLOR_BGR2RGB)
@@ -59,13 +59,13 @@ def test_decode_op_tf_file_dataset():
     data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=ds.Shuffle.FILES)
     data1 = data1.map(input_columns=["image"], operations=vision.Decode(True))
 
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         logger.info('decode == {}'.format(item['image']))
 
     # Second dataset
     data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
 
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         actual = item1["image"]
         expected = cv2.imdecode(item2["image"], cv2.IMREAD_COLOR)
         expected = cv2.cvtColor(expected, cv2.COLOR_BGR2RGB)

tests/ut/python/dataset/test_duplicate_op.py

@@ -26,7 +26,7 @@ def compare(array):
     array = np.array(array)
     data = data.map(input_columns=["x"], output_columns=["x", "y"], columns_order=["x", "y"],
                     operations=ops.Duplicate())
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         np.testing.assert_array_equal(array, d["x"])
         np.testing.assert_array_equal(array, d["y"])
 

tests/ut/python/dataset/test_epoch_ctrl.py

@@ -134,7 +134,7 @@ def test_generator_dict_0():
 
     i = 0
     # create the iterator inside the loop declaration
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([i])
         np.testing.assert_array_equal(item["data"], golden)
         i = i + 1
@@ -152,7 +152,7 @@ def test_generator_dict_1():
         i = 0
         # BAD. Do not create iterator every time inside.
         # Create iterator outside the epoch for loop.
-        for item in data1.create_dict_iterator():  # each data is a dictionary
+        for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
             golden = np.array([i])
             np.testing.assert_array_equal(item["data"], golden)
             i = i + 1
@@ -318,7 +318,7 @@ def test_generator_tuple_0():
 
     i = 0
     # create the iterator inside the loop declaration
-    for item in data1.create_tuple_iterator():  # each data is a dictionary
+    for item in data1.create_tuple_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([i])
         np.testing.assert_array_equal(item[0], golden)
         i = i + 1
@@ -336,7 +336,7 @@ def test_generator_tuple_1():
         i = 0
         # BAD. Do not create iterator every time inside.
         # Create iterator outside the epoch for loop.
-        for item in data1.create_tuple_iterator():  # each data is a dictionary
+        for item in data1.create_tuple_iterator(num_epochs=1):  # each data is a dictionary
             golden = np.array([i])
             np.testing.assert_array_equal(item[0], golden)
             i = i + 1

tests/ut/python/dataset/test_exceptions.py

@@ -50,7 +50,7 @@ def test_exception_02():
     # Confirm 1 sample in dataset
     assert sum([1 for _ in data]) == 1
     num_iters = 0
-    for _ in data.create_dict_iterator():
+    for _ in data.create_dict_iterator(num_epochs=1):
         num_iters += 1
     assert num_iters == 1
 

tests/ut/python/dataset/test_filterop.py

@@ -35,7 +35,7 @@ def test_diff_predicate_func():
 
         num_iter = 0
         label_list = []
-        for data in dataset.create_dict_iterator():
+        for data in dataset.create_dict_iterator(num_epochs=1):
             num_iter += 1
             label = data["label"]
             label_list.append(label)
@@ -64,7 +64,7 @@ def test_filter_by_generator_with_no():
     dataset_f = dataset.filter(predicate=lambda data: data < 11, num_parallel_workers=4)
     num_iter = 0
     expected_rs = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
-    for item in dataset_f.create_dict_iterator():
+    for item in dataset_f.create_dict_iterator(num_epochs=1):
         assert item["data"] == expected_rs[num_iter]
         num_iter += 1
 
@@ -77,7 +77,7 @@ def test_filter_by_generator_with_repeat():
     num_iter = 0
     ret_data = []
     expected_rs = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
-    for item in dataset_f.create_dict_iterator():
+    for item in dataset_f.create_dict_iterator(num_epochs=1):
         num_iter += 1
         ret_data.append(item["data"])
     assert num_iter == 44
@@ -95,7 +95,7 @@ def test_filter_by_generator_with_repeat_after():
     num_iter = 0
     ret_data = []
     expected_rs = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
-    for item in dataset_r.create_dict_iterator():
+    for item in dataset_r.create_dict_iterator(num_epochs=1):
         num_iter += 1
         ret_data.append(item["data"])
     assert num_iter == 44
@@ -120,7 +120,7 @@ def test_filter_by_generator_with_batch():
     dataset_f = dataset_b.filter(predicate=filter_func_batch, num_parallel_workers=4)
     num_iter = 0
     ret_data = []
-    for item in dataset_f.create_dict_iterator():
+    for item in dataset_f.create_dict_iterator(num_epochs=1):
         num_iter += 1
         ret_data.append(item["data"])
     assert num_iter == 3
@@ -136,7 +136,7 @@ def test_filter_by_generator_with_batch_after():
     dataset_b = dataset_f.batch(4)
     num_iter = 0
     ret_data = []
-    for item in dataset_b.create_dict_iterator():
+    for item in dataset_b.create_dict_iterator(num_epochs=1):
         num_iter += 1
         ret_data.append(item["data"])
     assert num_iter == 6
@@ -155,7 +155,7 @@ def test_filter_by_generator_with_shuffle():
     dataset_s = dataset.shuffle(4)
     dataset_f = dataset_s.filter(predicate=filter_func_shuffle, num_parallel_workers=4)
     num_iter = 0
-    for _ in dataset_f.create_dict_iterator():
+    for _ in dataset_f.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 21
 
@@ -170,7 +170,7 @@ def test_filter_by_generator_with_shuffle_after():
     dataset_f = dataset.filter(predicate=filter_func_shuffle_after, num_parallel_workers=4)
     dataset_s = dataset_f.shuffle(4)
     num_iter = 0
-    for _ in dataset_s.create_dict_iterator():
+    for _ in dataset_s.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 21
 
@@ -202,7 +202,7 @@ def test_filter_by_generator_with_zip():
     dataset_f = dataz.filter(predicate=filter_func_zip, num_parallel_workers=1)
     num_iter = 0
     ret_data = []
-    for item in dataset_f.create_dict_iterator():
+    for item in dataset_f.create_dict_iterator(num_epochs=1):
         num_iter += 1
         ret_data.append({"data1": item["data1"], "data2": item["data2"]})
     assert num_iter == 21
@@ -221,7 +221,7 @@ def test_filter_by_generator_with_zip_after():
     dataz = ds.zip((dt1, dt2))
     num_iter = 0
     ret_data = []
-    for item in dataz.create_dict_iterator():
+    for item in dataz.create_dict_iterator(num_epochs=1):
         num_iter += 1
         ret_data.append({"data1": item["data1"], "data2": item["data2"]})
     assert num_iter == 21
@@ -266,7 +266,7 @@ def test_filter_by_generator_with_map_all_col():
     dataset_f = dataset_map.filter(input_columns=["col1"], predicate=filter_func_map_part, num_parallel_workers=1)
     num_iter = 0
     ret_data = []
-    for item in dataset_f.create_dict_iterator():
+    for item in dataset_f.create_dict_iterator(num_epochs=1):
         num_iter += 1
         ret_data.append(item["col1"])
     assert num_iter == 3
@@ -282,7 +282,7 @@ def test_filter_by_generator_with_map_part_col():
     dataset_f = dataset_map.filter(input_columns=["out1", "col2"], predicate=filter_func_map, num_parallel_workers=4)
     num_iter = 0
     ret_data = []
-    for item in dataset_f.create_dict_iterator():
+    for item in dataset_f.create_dict_iterator(num_epochs=1):
         num_iter += 1
         print(item)
         ret_data.append(item["out1"])
@@ -302,7 +302,7 @@ def test_filter_by_generator_with_rename():
     dataset_f = dataset_b.filter(predicate=filter_func_rename, num_parallel_workers=4)
     num_iter = 0
     ret_data = []
-    for item in dataset_f.create_dict_iterator():
+    for item in dataset_f.create_dict_iterator(num_epochs=1):
         num_iter += 1
         ret_data.append(item["col1"])
     assert num_iter == 55
@@ -336,7 +336,7 @@ def test_filter_by_generator_with_input_column():
     dataset_f4 = dataset_f3.filter(predicate=filter_func_input_column1, num_parallel_workers=4)
     num_iter = 0
     ret_data = []
-    for item in dataset_f4.create_dict_iterator():
+    for item in dataset_f4.create_dict_iterator(num_epochs=1):
         num_iter += 1
         ret_data.append(item["out1"])
     assert num_iter == 8
@@ -370,7 +370,7 @@ def test_filter_by_generator_Partial0():
     dataset_zip = ds.zip((dataset1, dataset2))
     dataset_f1 = dataset_zip.filter(predicate=filter_func_Partial_0, num_parallel_workers=2)
     ret = []
-    for item in dataset_f1.create_dict_iterator():
+    for item in dataset_f1.create_dict_iterator(num_epochs=1):
         ret.append(item["col1"])
     assert ret[0] == 5
     assert ret[6] == 12
@@ -384,7 +384,7 @@ def test_filter_by_generator_Partial1():
     dataset_f1 = dataset_zip.filter(predicate=filter_func_Partial_0, num_parallel_workers=2)
     dataset_map = dataset_f1.map(input_columns=["col1"], output_columns=["out1"], operations=lambda x1: x1 + 400)
     ret = []
-    for item in dataset_map.create_dict_iterator():
+    for item in dataset_map.create_dict_iterator(num_epochs=1):
         ret.append(item["out1"])
     assert ret[0] == 405
     assert ret[6] == 412
@@ -403,7 +403,7 @@ def test_filter_by_generator_Partial2():
                                   operations=lambda x1, x3: (x1 + 400, x3 + 500))
     ret1 = []
     ret3 = []
-    for item in dataset_map.create_dict_iterator():
+    for item in dataset_map.create_dict_iterator(num_epochs=1):
         ret1.append(item["out1"])
         ret3.append(item["out3"])
     assert ret1[0] == 400
@@ -428,7 +428,7 @@ def test_filter_by_generator_Partial():
     dataset_s = dataset.shuffle(4)
     dataset_f1 = dataset_s.filter(input_columns=["col1", "col2"], predicate=filter_func_Partial, num_parallel_workers=1)
 
-    for item in dataset_f1.create_dict_iterator():
+    for item in dataset_f1.create_dict_iterator(num_epochs=1):
         assert item["col1"] % 3 == 0
 
 
@@ -442,7 +442,7 @@ def test_filte_case_dataset_cifar10():
     DATA_DIR_10 = "../data/dataset/testCifar10Data"
     dataset_c = ds.Cifar10Dataset(dataset_dir=DATA_DIR_10, num_samples=100000, shuffle=False)
     dataset_f1 = dataset_c.filter(input_columns=["image", "label"], predicate=filter_func_cifar, num_parallel_workers=1)
-    for item in dataset_f1.create_dict_iterator():
+    for item in dataset_f1.create_dict_iterator(num_epochs=1):
         # in this example, each dictionary has keys "image" and "label"
         assert item["label"] % 3 == 0
 
@@ -476,7 +476,7 @@ def test_filter_by_generator_with_map_all_sort():
     dataset_f = dataz.filter(predicate=filter_func_part_sort, num_parallel_workers=1)
     num_iter = 0
     ret_data = []
-    for item in dataset_f.create_dict_iterator():
+    for item in dataset_f.create_dict_iterator(num_epochs=1):
         num_iter += 1
         ret_data.append(item)
 
@@ -490,7 +490,7 @@ def test_filter_by_generator_get_dataset_size():
     data_sie = dataset.get_dataset_size()
 
     num_iter = 0
-    for _ in dataset.create_dict_iterator():
+    for _ in dataset.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert data_sie == num_iter
 

tests/ut/python/dataset/test_five_crop.py

@@ -53,7 +53,7 @@ def test_five_crop_op(plot=False):
     data2 = data2.map(input_columns=["image"], operations=transform_2())
 
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         num_iter += 1
         image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_2 = item2["image"]

tests/ut/python/dataset/test_from_dataset.py

@@ -28,7 +28,7 @@ def test_demo_basic_from_dataset():
                                     special_first=True)
     data = data.map(input_columns=["text"], operations=text.Lookup(vocab, "<unk>"))
     res = []
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         res.append(d["text"].item())
     assert res == [4, 5, 3, 6, 7, 2], res
 
@@ -41,7 +41,7 @@ def test_demo_basic_from_dataset_with_tokenizer():
                                     special_first=True)
     data = data.map(input_columns=["text"], operations=text.Lookup(vocab, "<unk>"))
     res = []
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         res.append(list(d["text"]))
     assert res == [[13, 3, 7, 14, 9, 17, 3, 2, 19, 9, 2, 11, 3, 4, 16, 4, 8, 6, 5], [21, 20, 10, 25, 23, 26],
                    [24, 22, 10, 12, 8, 6, 7, 4, 18, 15, 5], [2, 2]]
@@ -62,7 +62,7 @@ def test_from_dataset():
                                         special_first=True)
         corpus_dataset = corpus_dataset.map(input_columns="text", operations=text.Lookup(vocab, "<unk>"))
         res = []
-        for d in corpus_dataset.create_dict_iterator():
+        for d in corpus_dataset.create_dict_iterator(num_epochs=1):
             res.append(list(d["text"]))
         return res
 
@@ -110,7 +110,7 @@ def test_from_dataset_special_token():
         data = ds.GeneratorDataset(gen_input(texts), column_names=["text"])
         data = data.map(input_columns="text", operations=text.Lookup(vocab, "<unk>"))
         res = []
-        for d in data.create_dict_iterator():
+        for d in data.create_dict_iterator(num_epochs=1):
             res.append(d["text"].item())
         return res
 

tests/ut/python/dataset/test_graphdata.py

@@ -186,7 +186,7 @@ def test_graphdata_generatordataset():
     dataset = ds.GeneratorDataset(source=GNNGraphDataset(g, batch_num), column_names=out_column_names,
                                   sampler=RandomBatchedSampler(edge_num, batch_num), num_parallel_workers=4)
     dataset = dataset.repeat(2)
-    itr = dataset.create_dict_iterator()
+    itr = dataset.create_dict_iterator(num_epochs=1)
     i = 0
     for data in itr:
         assert data['neighbors'].shape == (2, 7)

tests/ut/python/dataset/test_graphdata_distributed.py

@@ -112,7 +112,7 @@ def test_graphdata_distributed():
                                   sampler=RandomBatchedSampler(edge_num, batch_num), num_parallel_workers=4,
                                   python_multiprocessing=False)
     dataset = dataset.repeat(2)
-    itr = dataset.create_dict_iterator()
+    itr = dataset.create_dict_iterator(num_epochs=1)
     i = 0
     for data in itr:
         assert data['neighbors'].shape == (2, 7)

tests/ut/python/dataset/test_iterator.py

@@ -28,7 +28,8 @@ def check(project_columns):
     data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=COLUMNS, shuffle=False)
     data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=project_columns, shuffle=False)
 
-    for data_actual, data_expected in zip(data1.create_tuple_iterator(project_columns), data2.create_tuple_iterator()):
+    for data_actual, data_expected in zip(data1.create_tuple_iterator(project_columns, num_epochs=1),
+                                          data2.create_tuple_iterator(num_epochs=1)):
         assert len(data_actual) == len(data_expected)
         assert all([np.array_equal(d1, d2) for d1, d2 in zip(data_actual, data_expected)])
 
@@ -48,9 +49,9 @@ def test_iterator_create_tuple():
 def test_iterator_weak_ref():
     ITERATORS_LIST.clear()
     data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR)
-    itr1 = data.create_tuple_iterator()
-    itr2 = data.create_tuple_iterator()
-    itr3 = data.create_tuple_iterator()
+    itr1 = data.create_tuple_iterator(num_epochs=1)
+    itr2 = data.create_tuple_iterator(num_epochs=1)
+    itr3 = data.create_tuple_iterator(num_epochs=1)
 
     assert len(ITERATORS_LIST) == 3
     assert sum(itr() is not None for itr in ITERATORS_LIST) == 3
@@ -67,9 +68,9 @@ def test_iterator_weak_ref():
     assert len(ITERATORS_LIST) == 3
     assert sum(itr() is not None for itr in ITERATORS_LIST) == 0
 
-    itr1 = data.create_tuple_iterator()
-    itr2 = data.create_tuple_iterator()
-    itr3 = data.create_tuple_iterator()
+    itr1 = data.create_tuple_iterator(num_epochs=1)
+    itr2 = data.create_tuple_iterator(num_epochs=1)
+    itr3 = data.create_tuple_iterator(num_epochs=1)
 
     _cleanup()
     with pytest.raises(AttributeError) as info:
@@ -102,7 +103,7 @@ def test_tree_copy():
     data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=COLUMNS)
     data1 = data.map(operations=[MyDict()])
 
-    itr = data1.create_tuple_iterator()
+    itr = data1.create_tuple_iterator(num_epochs=1)
 
     assert id(data1) != id(itr.dataset)
     assert id(data) != id(itr.dataset.children[0])

tests/ut/python/dataset/test_linear_transformation.py

@@ -62,7 +62,7 @@ def test_linear_transformation_op(plot=False):
 
     image_transformed = []
     image = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_transformed.append(image1)

tests/ut/python/dataset/test_minddataset.py

@@ -186,7 +186,7 @@ def test_nlp_compress_data(add_and_remove_nlp_compress_file):
         NLP_FILE_NAME + "0", None, num_readers, shuffle=False)
     assert data_set.get_dataset_size() == 16
     num_iter = 0
-    for x, item in zip(data, data_set.create_dict_iterator()):
+    for x, item in zip(data, data_set.create_dict_iterator(num_epochs=1)):
         assert (item["array_a"] == x["array_a"]).all()
         assert (item["array_b"] == x["array_b"]).all()
         assert item["array_c"].tobytes() == x["array_c"]
@@ -205,7 +205,7 @@ def test_nlp_compress_data_old_version(add_and_remove_nlp_compress_file):
         OLD_NLP_FILE_NAME + "0", None, num_readers, shuffle=False)
     assert old_data_set.get_dataset_size() == 16
     num_iter = 0
-    for x, item in zip(old_data_set.create_dict_iterator(), data_set.create_dict_iterator()):
+    for x, item in zip(old_data_set.create_dict_iterator(num_epochs=1), data_set.create_dict_iterator(num_epochs=1)):
         assert (item["array_a"] == x["array_a"]).all()
         assert (item["array_b"] == x["array_b"]).all()
         assert (item["array_c"] == x["array_c"]).all()
@@ -254,7 +254,7 @@ def test_cv_minddataset_partition_tutorial(add_and_remove_cv_file):
             data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
                                       num_shards=num_shards, shard_id=partition_id)
             num_iter = 0
-            for item in data_set.create_dict_iterator():
+            for item in data_set.create_dict_iterator(num_epochs=1):
                 logger.info("-------------- partition : {} ------------------------".format(partition_id))
                 logger.info("-------------- item[file_name]: {}-----------------------".format(item["file_name"]))
                 logger.info("-------------- item[label]: {} -----------------------".format(item["label"]))
@@ -276,7 +276,7 @@ def test_cv_minddataset_partition_num_samples_0(add_and_remove_cv_file):
                                       num_shards=num_shards,
                                       shard_id=partition_id, num_samples=1)
             num_iter = 0
-            for item in data_set.create_dict_iterator():
+            for item in data_set.create_dict_iterator(num_epochs=1):
                 logger.info("-------------- partition : {} ------------------------".format(partition_id))
                 logger.info("-------------- item[file_name]: {}-----------------------".format(item["file_name"]))
                 logger.info("-------------- item[label]: {} -----------------------".format(item["label"]))
@@ -298,7 +298,7 @@ def test_cv_minddataset_partition_num_samples_1(add_and_remove_cv_file):
                                       num_shards=num_shards,
                                       shard_id=partition_id, num_samples=2)
             num_iter = 0
-            for item in data_set.create_dict_iterator():
+            for item in data_set.create_dict_iterator(num_epochs=1):
                 logger.info("-------------- partition : {} ------------------------".format(partition_id))
                 logger.info("-------------- item[file_name]: {}-----------------------".format(item["file_name"]))
                 logger.info("-------------- item[label]: {} -----------------------".format(item["label"]))
@@ -320,7 +320,7 @@ def test_cv_minddataset_partition_num_samples_2(add_and_remove_cv_file):
                                       num_shards=num_shards,
                                       shard_id=partition_id, num_samples=3)
             num_iter = 0
-            for item in data_set.create_dict_iterator():
+            for item in data_set.create_dict_iterator(num_epochs=1):
                 logger.info("-------------- partition : {} ------------------------".format(partition_id))
                 logger.info("-------------- item[file_name]: {}-----------------------".format(item["file_name"]))
                 logger.info("-------------- item[label]: {} -----------------------".format(item["label"]))
@@ -348,7 +348,7 @@ def test_cv_minddataset_partition_tutorial_check_shuffle_result(add_and_remove_c
         data_set = data_set.repeat(3)
 
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             logger.info("-------------- partition : {} ------------------------".format(partition_id))
             logger.info("-------------- item[file_name]: {}-----------------------".format(item["file_name"]))
             logger.info("-------------- item[label]: {} -----------------------".format(item["label"]))
@@ -387,7 +387,7 @@ def test_cv_minddataset_partition_tutorial_check_whole_reshuffle_result_per_epoc
         data_set = data_set.repeat(3)
 
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             logger.info("-------------- partition : {} ------------------------".format(partition_id))
             logger.info("-------------- item[file_name]: {}-----------------------".format(item["file_name"]))
             logger.info("-------------- item[label]: {} -----------------------".format(item["label"]))
@@ -420,7 +420,7 @@ def test_cv_minddataset_check_shuffle_result(add_and_remove_cv_file):
     data_set = data_set.repeat(3)
 
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info("-------------- item[file_name]: {}-----------------------".format(item["file_name"]))
         logger.info("-------------- item[label]: {} -----------------------".format(item["label"]))
         num_iter += 1
@@ -446,7 +446,7 @@ def test_cv_minddataset_check_shuffle_result(add_and_remove_cv_file):
     data_set2 = data_set2.repeat(3)
 
     num_iter = 0
-    for item in data_set2.create_dict_iterator():
+    for item in data_set2.create_dict_iterator(num_epochs=1):
         logger.info("-------------- item[file_name]: {}-----------------------".format(item["file_name"]))
         logger.info("-------------- item[label]: {} -----------------------".format(item["label"]))
         num_iter += 1
@@ -477,7 +477,7 @@ def test_cv_minddataset_check_shuffle_result(add_and_remove_cv_file):
     data_set3 = data_set3.repeat(3)
 
     num_iter = 0
-    for item in data_set3.create_dict_iterator():
+    for item in data_set3.create_dict_iterator(num_epochs=1):
         logger.info("-------------- item[file_name]: {}-----------------------".format(item["file_name"]))
         logger.info("-------------- item[label]: {} -----------------------".format(item["label"]))
         num_iter += 1
@@ -509,7 +509,7 @@ def test_cv_minddataset_dataset_size(add_and_remove_cv_file):
     repeat_num = 2
     data_set = data_set.repeat(repeat_num)
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- get dataset size {} -----------------".format(num_iter))
         logger.info(
@@ -538,7 +538,7 @@ def test_cv_minddataset_repeat_reshuffle(add_and_remove_cv_file):
     data_set = data_set.repeat(2)
     num_iter = 0
     labels = []
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- get dataset size {} -----------------".format(num_iter))
         logger.info(
@@ -567,7 +567,7 @@ def test_cv_minddataset_batch_size_larger_than_records(add_and_remove_cv_file):
                             operations=resize_op, num_parallel_workers=2)
     data_set = data_set.batch(32, drop_remainder=True)
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- get dataset size {} -----------------".format(num_iter))
         logger.info(
@@ -586,7 +586,7 @@ def test_cv_minddataset_issue_888(add_and_remove_cv_file):
     data_set = data_set.shuffle(2)
     data_set = data_set.repeat(9)
     num_iter = 0
-    for _ in data_set.create_dict_iterator():
+    for _ in data_set.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 18
 
@@ -599,7 +599,7 @@ def test_cv_minddataset_reader_file_list(add_and_remove_cv_file):
                                for x in range(FILES_NUM)], columns_list, num_readers)
     assert data_set.get_dataset_size() == 10
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -621,7 +621,7 @@ def test_cv_minddataset_reader_one_partition(add_and_remove_cv_file):
     data_set = ds.MindDataset([CV_FILE_NAME + "0"], columns_list, num_readers)
     assert data_set.get_dataset_size() < 10
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -674,7 +674,7 @@ def test_cv_minddataset_reader_two_dataset(add_and_remove_cv_file):
                                   columns_list, num_readers)
         assert data_set.get_dataset_size() == 30
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             logger.info(
                 "-------------- cv reader basic: {} ------------------------".format(num_iter))
             logger.info(
@@ -734,7 +734,7 @@ def test_cv_minddataset_reader_two_dataset_partition(add_and_remove_cv_file):
                                   columns_list, num_readers)
         assert data_set.get_dataset_size() < 20
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             logger.info(
                 "-------------- cv reader basic: {} ------------------------".format(num_iter))
             logger.info(
@@ -764,7 +764,7 @@ def test_cv_minddataset_reader_basic_tutorial(add_and_remove_cv_file):
     data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers)
     assert data_set.get_dataset_size() == 10
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -784,7 +784,7 @@ def test_nlp_minddataset_reader_basic_tutorial(add_and_remove_nlp_file):
     data_set = ds.MindDataset(NLP_FILE_NAME + "0", None, num_readers)
     assert data_set.get_dataset_size() == 10
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -858,7 +858,7 @@ def test_cv_minddataset_reader_no_columns(add_and_remove_cv_file):
     data_set = ds.MindDataset(CV_FILE_NAME + "0")
     assert data_set.get_dataset_size() == 10
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -881,7 +881,7 @@ def test_cv_minddataset_reader_repeat_tutorial(add_and_remove_cv_file):
     repeat_num = 2
     data_set = data_set.repeat(repeat_num)
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- repeat two test {} ------------------------".format(num_iter))
         logger.info(
@@ -1210,7 +1210,7 @@ def test_write_with_multi_bytes_and_array_and_read_by_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 13
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1229,7 +1229,7 @@ def test_write_with_multi_bytes_and_array_and_read_by_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 3
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1246,7 +1246,7 @@ def test_write_with_multi_bytes_and_array_and_read_by_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 4
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1265,7 +1265,7 @@ def test_write_with_multi_bytes_and_array_and_read_by_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 3
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1284,7 +1284,7 @@ def test_write_with_multi_bytes_and_array_and_read_by_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 5
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1303,7 +1303,7 @@ def test_write_with_multi_bytes_and_array_and_read_by_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 5
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1323,7 +1323,7 @@ def test_write_with_multi_bytes_and_array_and_read_by_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 11
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1413,7 +1413,7 @@ def test_write_with_multi_bytes_and_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 7
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1431,7 +1431,7 @@ def test_write_with_multi_bytes_and_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 3
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1449,7 +1449,7 @@ def test_write_with_multi_bytes_and_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 2
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1467,7 +1467,7 @@ def test_write_with_multi_bytes_and_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 2
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1485,7 +1485,7 @@ def test_write_with_multi_bytes_and_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 3
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1504,7 +1504,7 @@ def test_write_with_multi_bytes_and_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 5
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1607,7 +1607,7 @@ def test_write_with_multi_array_and_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 8
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1627,7 +1627,7 @@ def test_write_with_multi_array_and_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 6
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1647,7 +1647,7 @@ def test_write_with_multi_array_and_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 3
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1667,7 +1667,7 @@ def test_write_with_multi_array_and_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 3
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1685,7 +1685,7 @@ def test_write_with_multi_array_and_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 1
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1706,7 +1706,7 @@ def test_write_with_multi_array_and_MindDataset():
                                   shuffle=False)
         assert data_set.get_dataset_size() == 6
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 8
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1753,7 +1753,7 @@ def test_numpy_generic():
         data_set = ds.MindDataset(CV_FILE_NAME + "0", None, num_readers, shuffle=False)
         assert data_set.get_dataset_size() == 10
         idx = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert item['label1'] == item['label1']
             assert item['label2'] == item['label2']
             assert item['label3'] == item['label3']
@@ -1853,7 +1853,7 @@ def test_write_with_float32_float64_float32_array_float64_array_and_MindDataset(
                                   shuffle=False)
         assert data_set.get_dataset_size() == 5
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 8
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1875,7 +1875,7 @@ def test_write_with_float32_float64_float32_array_float64_array_and_MindDataset(
                                   shuffle=False)
         assert data_set.get_dataset_size() == 5
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 2
             for field in item:
                 if isinstance(item[field], np.ndarray):
@@ -1897,7 +1897,7 @@ def test_write_with_float32_float64_float32_array_float64_array_and_MindDataset(
                                   shuffle=False)
         assert data_set.get_dataset_size() == 5
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 2
             for field in item:
                 if isinstance(item[field], np.ndarray):

tests/ut/python/dataset/test_minddataset_exception.py

@@ -97,7 +97,7 @@ def test_invalid_mindrecord():
     with pytest.raises(Exception, match="MindRecordOp init failed"):
         data_set = ds.MindDataset('dummy.mindrecord', columns_list, num_readers)
         num_iter = 0
-        for _ in data_set.create_dict_iterator():
+        for _ in data_set.create_dict_iterator(num_epochs=1):
             num_iter += 1
         try:
             assert num_iter == 0
@@ -116,7 +116,7 @@ def test_minddataset_lack_db():
     with pytest.raises(Exception, match="MindRecordOp init failed"):
         data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers)
         num_iter = 0
-        for _ in data_set.create_dict_iterator():
+        for _ in data_set.create_dict_iterator(num_epochs=1):
             num_iter += 1
         try:
             assert num_iter == 0
@@ -135,7 +135,7 @@ def test_cv_minddataset_pk_sample_error_class_column():
     with pytest.raises(Exception, match="MindRecordOp launch failed"):
         data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers, sampler=sampler)
         num_iter = 0
-        for _ in data_set.create_dict_iterator():
+        for _ in data_set.create_dict_iterator(num_epochs=1):
             num_iter += 1
     os.remove(CV_FILE_NAME)
     os.remove("{}.db".format(CV_FILE_NAME))
@@ -150,7 +150,7 @@ def test_cv_minddataset_pk_sample_exclusive_shuffle():
         data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers,
                                   sampler=sampler, shuffle=False)
         num_iter = 0
-        for _ in data_set.create_dict_iterator():
+        for _ in data_set.create_dict_iterator(num_epochs=1):
             num_iter += 1
     os.remove(CV_FILE_NAME)
     os.remove("{}.db".format(CV_FILE_NAME))
@@ -165,7 +165,7 @@ def test_cv_minddataset_reader_different_schema():
         data_set = ds.MindDataset([CV_FILE_NAME, CV1_FILE_NAME], columns_list,
                                   num_readers)
         num_iter = 0
-        for _ in data_set.create_dict_iterator():
+        for _ in data_set.create_dict_iterator(num_epochs=1):
             num_iter += 1
     os.remove(CV_FILE_NAME)
     os.remove("{}.db".format(CV_FILE_NAME))
@@ -182,7 +182,7 @@ def test_cv_minddataset_reader_different_page_size():
         data_set = ds.MindDataset([CV_FILE_NAME, CV1_FILE_NAME], columns_list,
                                   num_readers)
         num_iter = 0
-        for _ in data_set.create_dict_iterator():
+        for _ in data_set.create_dict_iterator(num_epochs=1):
             num_iter += 1
     os.remove(CV_FILE_NAME)
     os.remove("{}.db".format(CV_FILE_NAME))
@@ -197,7 +197,7 @@ def test_minddataset_invalidate_num_shards():
     with pytest.raises(Exception) as error_info:
         data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers, True, 1, 2)
         num_iter = 0
-        for _ in data_set.create_dict_iterator():
+        for _ in data_set.create_dict_iterator(num_epochs=1):
             num_iter += 1
     try:
         assert 'Input shard_id is not within the required interval of (0 to 0).' in str(error_info.value)
@@ -217,7 +217,7 @@ def test_minddataset_invalidate_shard_id():
     with pytest.raises(Exception) as error_info:
         data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers, True, 1, -1)
         num_iter = 0
-        for _ in data_set.create_dict_iterator():
+        for _ in data_set.create_dict_iterator(num_epochs=1):
             num_iter += 1
     try:
         assert 'Input shard_id is not within the required interval of (0 to 0).' in str(error_info.value)
@@ -237,7 +237,7 @@ def test_minddataset_shard_id_bigger_than_num_shard():
     with pytest.raises(Exception) as error_info:
         data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers, True, 2, 2)
         num_iter = 0
-        for _ in data_set.create_dict_iterator():
+        for _ in data_set.create_dict_iterator(num_epochs=1):
             num_iter += 1
     try:
         assert 'Input shard_id is not within the required interval of (0 to 1).' in str(error_info.value)
@@ -249,7 +249,7 @@ def test_minddataset_shard_id_bigger_than_num_shard():
     with pytest.raises(Exception) as error_info:
         data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers, True, 2, 5)
         num_iter = 0
-        for _ in data_set.create_dict_iterator():
+        for _ in data_set.create_dict_iterator(num_epochs=1):
             num_iter += 1
     try:
         assert 'Input shard_id is not within the required interval of (0 to 1).' in str(error_info.value)
@@ -274,7 +274,7 @@ def test_cv_minddataset_partition_num_samples_equals_0():
                                       num_shards=num_shards,
                                       shard_id=partition_id, num_samples=0)
             num_iter = 0
-            for _ in data_set.create_dict_iterator():
+            for _ in data_set.create_dict_iterator(num_epochs=1):
                 num_iter += 1
     with pytest.raises(Exception) as error_info:
         partitions(5)

tests/ut/python/dataset/test_minddataset_multi_images.py

@@ -29,7 +29,7 @@ def test_cv_minddataset_reader_two_png_tutorial():
     data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers)
     assert data_set.get_dataset_size() == 5
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         assert len(item) == 5
         logger.info("-------------- cv reader basic is {} -----------------".format(num_iter))
         logger.info("-------------- item[id] is {} ------------------------".format(item["id"]))
@@ -50,7 +50,7 @@ def test_cv_minddataset_reader_two_png_tutorial_just_image2():
     data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers)
     assert data_set.get_dataset_size() == 5
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         assert len(item) == 2
         logger.info("-------------- cv reader basic is {} -----------------".format(num_iter))
         logger.info("-------------- item[img_data] is {} ------------------".format(item["img_data"]))

tests/ut/python/dataset/test_minddataset_multi_images_and_ndarray.py

@@ -57,7 +57,7 @@ def test_cv_minddataset_reader_multi_image_and_ndarray_tutorial():
         data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers)
         assert data_set.get_dataset_size() == 5
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             assert len(item) == 7
             logger.info("item: {}".format(item))
             assert item["image_0"].dtype == np.uint8

tests/ut/python/dataset/test_minddataset_padded.py

@@ -123,7 +123,7 @@ def test_cv_minddataset_reader_basic_padded_samples(add_and_remove_cv_file):
     assert data_set.get_dataset_size() == 15
     num_iter = 0
     num_padded_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
         logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
@@ -158,7 +158,7 @@ def test_cv_minddataset_partition_padded_samples(add_and_remove_cv_file):
                                       padded_sample=padded_sample,
                                       num_padded=num_padded)
             assert data_set.get_dataset_size() == dataset_size
-            for item in data_set.create_dict_iterator():
+            for item in data_set.create_dict_iterator(num_epochs=1):
                 logger.info("-------------- partition : {} ------------------------".format(partition_id))
                 logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
                 logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
@@ -205,7 +205,7 @@ def test_cv_minddataset_partition_padded_samples_multi_epoch(add_and_remove_cv_f
             assert data_set.get_dataset_size() == dataset_size
             data_set = data_set.repeat(repeat_size)
             local_index = 0
-            for item in data_set.create_dict_iterator():
+            for item in data_set.create_dict_iterator(num_epochs=1):
                 logger.info("-------------- partition : {} ------------------------".format(partition_id))
                 logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
                 logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
@@ -266,7 +266,7 @@ def test_cv_minddataset_partition_padded_samples_no_dividsible(add_and_remove_cv
                                       padded_sample=padded_sample,
                                       num_padded=num_padded)
             num_iter = 0
-            for item in data_set.create_dict_iterator():
+            for item in data_set.create_dict_iterator(num_epochs=1):
                 num_iter += 1
         return num_iter
 
@@ -309,7 +309,7 @@ def test_cv_minddataset_partition_padded_samples_no_equal_column_list(add_and_re
                                       shard_id=partition_id,
                                       padded_sample=padded_sample,
                                       num_padded=num_padded)
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             logger.info("-------------- partition : {} ------------------------".format(partition_id))
             logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
             logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
@@ -331,7 +331,7 @@ def test_cv_minddataset_partition_padded_samples_no_column_list(add_and_remove_c
                                       shard_id=partition_id,
                                       padded_sample=padded_sample,
                                       num_padded=num_padded)
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             logger.info("-------------- partition : {} ------------------------".format(partition_id))
             logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
             logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
@@ -352,7 +352,7 @@ def test_cv_minddataset_partition_padded_samples_no_num_padded(add_and_remove_cv
                                       num_shards=num_shards,
                                       shard_id=partition_id,
                                       padded_sample=padded_sample)
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             logger.info("-------------- partition : {} ------------------------".format(partition_id))
             logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
             logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
@@ -373,7 +373,7 @@ def test_cv_minddataset_partition_padded_samples_no_padded_samples(add_and_remov
                                       num_shards=num_shards,
                                       shard_id=partition_id,
                                       num_padded=num_padded)
-        for item in data_set.create_dict_iterator():
+        for item in data_set.create_dict_iterator(num_epochs=1):
             logger.info("-------------- partition : {} ------------------------".format(partition_id))
             logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
             logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
@@ -403,7 +403,7 @@ def test_nlp_minddataset_reader_basic_padded_samples(add_and_remove_nlp_file):
                                       padded_sample=padded_sample,
                                       num_padded=num_padded)
             assert data_set.get_dataset_size() == dataset_size
-            for item in data_set.create_dict_iterator():
+            for item in data_set.create_dict_iterator(num_epochs=1):
                 logger.info("-------------- item[id]: {} ------------------------".format(item["id"]))
                 logger.info("-------------- item[rating]: {} --------------------".format(item["rating"]))
                 logger.info("-------------- item[input_ids]: {}, shape: {} -----------------".format(item["input_ids"], item["input_ids"].shape))
@@ -448,7 +448,7 @@ def test_nlp_minddataset_reader_basic_padded_samples_multi_epoch(add_and_remove_
             data_set = data_set.repeat(repeat_size)
 
             local_index = 0
-            for item in data_set.create_dict_iterator():
+            for item in data_set.create_dict_iterator(num_epochs=1):
                 logger.info("-------------- item[id]: {} ------------------------".format(item["id"]))
                 logger.info("-------------- item[rating]: {} --------------------".format(item["rating"]))
                 logger.info("-------------- item[input_ids]: {}, shape: {} -----------------".format(item["input_ids"], item["input_ids"].shape))
@@ -508,7 +508,7 @@ def test_nlp_minddataset_reader_basic_padded_samples_check_whole_reshuffle_resul
             assert data_set.get_dataset_size() == dataset_size
             data_set = data_set.repeat(repeat_size)
             inner_num_iter = 0
-            for item in data_set.create_dict_iterator():
+            for item in data_set.create_dict_iterator(num_epochs=1):
                 logger.info("-------------- item[id]: {} ------------------------".format(item["id"]))
                 logger.info("-------------- item[rating]: {} --------------------".format(item["rating"]))
                 logger.info("-------------- item[input_ids]: {}, shape: {} -----------------"

tests/ut/python/dataset/test_minddataset_sampler.py

@@ -70,7 +70,7 @@ def test_cv_minddataset_pk_sample_no_column(add_and_remove_cv_file):
 
     assert data_set.get_dataset_size() == 6
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info("-------------- item[file_name]: \
@@ -90,7 +90,7 @@ def test_cv_minddataset_pk_sample_basic(add_and_remove_cv_file):
 
     assert data_set.get_dataset_size() == 6
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info("-------------- item[data]: \
@@ -111,7 +111,7 @@ def test_cv_minddataset_pk_sample_shuffle(add_and_remove_cv_file):
 
     assert data_set.get_dataset_size() == 9
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info("-------------- item[file_name]: \
@@ -132,7 +132,7 @@ def test_cv_minddataset_pk_sample_shuffle_1(add_and_remove_cv_file):
 
     assert data_set.get_dataset_size() == 5
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info("-------------- item[file_name]: \
@@ -152,7 +152,7 @@ def test_cv_minddataset_pk_sample_shuffle_2(add_and_remove_cv_file):
 
     assert data_set.get_dataset_size() == 9
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info("-------------- item[file_name]: \
@@ -172,7 +172,7 @@ def test_cv_minddataset_pk_sample_out_of_range_0(add_and_remove_cv_file):
                               sampler=sampler)
     assert data_set.get_dataset_size() == 15
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info("-------------- item[file_name]: \
@@ -191,7 +191,7 @@ def test_cv_minddataset_pk_sample_out_of_range_1(add_and_remove_cv_file):
                               sampler=sampler)
     assert data_set.get_dataset_size() == 15
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info("-------------- item[file_name]: \
@@ -210,7 +210,7 @@ def test_cv_minddataset_pk_sample_out_of_range_2(add_and_remove_cv_file):
                               sampler=sampler)
     assert data_set.get_dataset_size() == 10
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info("-------------- item[file_name]: \
@@ -231,7 +231,7 @@ def test_cv_minddataset_subset_random_sample_basic(add_and_remove_cv_file):
                               sampler=sampler)
     assert data_set.get_dataset_size() == 5
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -254,7 +254,7 @@ def test_cv_minddataset_subset_random_sample_replica(add_and_remove_cv_file):
                               sampler=sampler)
     assert data_set.get_dataset_size() == 6
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -277,7 +277,7 @@ def test_cv_minddataset_subset_random_sample_empty(add_and_remove_cv_file):
                               sampler=sampler)
     assert data_set.get_dataset_size() == 0
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -300,7 +300,7 @@ def test_cv_minddataset_subset_random_sample_out_of_range(add_and_remove_cv_file
                               sampler=sampler)
     assert data_set.get_dataset_size() == 5
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -322,7 +322,7 @@ def test_cv_minddataset_subset_random_sample_negative(add_and_remove_cv_file):
                               sampler=sampler)
     assert data_set.get_dataset_size() == 5
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -345,7 +345,7 @@ def test_cv_minddataset_random_sampler_basic(add_and_remove_cv_file):
     assert data_set.get_dataset_size() == 10
     num_iter = 0
     new_dataset = []
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -371,7 +371,7 @@ def test_cv_minddataset_random_sampler_repeat(add_and_remove_cv_file):
     epoch1_dataset = []
     epoch2_dataset = []
     epoch3_dataset = []
-    for item in ds1.create_dict_iterator():
+    for item in ds1.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -400,7 +400,7 @@ def test_cv_minddataset_random_sampler_replacement(add_and_remove_cv_file):
                               sampler=sampler)
     assert data_set.get_dataset_size() == 5
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -422,7 +422,7 @@ def test_cv_minddataset_sequential_sampler_basic(add_and_remove_cv_file):
                               sampler=sampler)
     assert data_set.get_dataset_size() == 4
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -447,7 +447,7 @@ def test_cv_minddataset_sequential_sampler_exceed_size(add_and_remove_cv_file):
     dataset_size = data_set.get_dataset_size()
     assert dataset_size == 10
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for item in data_set.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info(
@@ -473,7 +473,7 @@ def test_cv_minddataset_split_basic(add_and_remove_cv_file):
     assert d1.get_dataset_size() == 8
     assert d2.get_dataset_size() == 2
     num_iter = 0
-    for item in d1.create_dict_iterator():
+    for item in d1.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- item[data]: {}  -----------------------------".format(item["data"]))
         logger.info(
@@ -485,7 +485,7 @@ def test_cv_minddataset_split_basic(add_and_remove_cv_file):
         num_iter += 1
     assert num_iter == 8
     num_iter = 0
-    for item in d2.create_dict_iterator():
+    for item in d2.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- item[data]: {}  -----------------------------".format(item["data"]))
         logger.info(
@@ -509,7 +509,7 @@ def test_cv_minddataset_split_exact_percent(add_and_remove_cv_file):
     assert d1.get_dataset_size() == 8
     assert d2.get_dataset_size() == 2
     num_iter = 0
-    for item in d1.create_dict_iterator():
+    for item in d1.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- item[data]: {}  -----------------------------".format(item["data"]))
         logger.info(
@@ -521,7 +521,7 @@ def test_cv_minddataset_split_exact_percent(add_and_remove_cv_file):
         num_iter += 1
     assert num_iter == 8
     num_iter = 0
-    for item in d2.create_dict_iterator():
+    for item in d2.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- item[data]: {}  -----------------------------".format(item["data"]))
         logger.info(
@@ -545,7 +545,7 @@ def test_cv_minddataset_split_fuzzy_percent(add_and_remove_cv_file):
     assert d1.get_dataset_size() == 4
     assert d2.get_dataset_size() == 6
     num_iter = 0
-    for item in d1.create_dict_iterator():
+    for item in d1.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- item[data]: {}  -----------------------------".format(item["data"]))
         logger.info(
@@ -557,7 +557,7 @@ def test_cv_minddataset_split_fuzzy_percent(add_and_remove_cv_file):
         num_iter += 1
     assert num_iter == 4
     num_iter = 0
-    for item in d2.create_dict_iterator():
+    for item in d2.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- item[data]: {}  -----------------------------".format(item["data"]))
         logger.info(
@@ -585,7 +585,7 @@ def test_cv_minddataset_split_deterministic(add_and_remove_cv_file):
     d1_dataset = []
     d2_dataset = []
     num_iter = 0
-    for item in d1.create_dict_iterator():
+    for item in d1.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- item[data]: {}  -----------------------------".format(item["data"]))
         logger.info(
@@ -596,7 +596,7 @@ def test_cv_minddataset_split_deterministic(add_and_remove_cv_file):
         num_iter += 1
     assert num_iter == 8
     num_iter = 0
-    for item in d2.create_dict_iterator():
+    for item in d2.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- item[data]: {}  -----------------------------".format(item["data"]))
         logger.info(
@@ -628,7 +628,7 @@ def test_cv_minddataset_split_sharding(add_and_remove_cv_file):
 
     num_iter = 0
     d1_shard1 = []
-    for item in d1.create_dict_iterator():
+    for item in d1.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- item[data]: {}  -----------------------------".format(item["data"]))
         logger.info(
@@ -649,7 +649,7 @@ def test_cv_minddataset_split_sharding(add_and_remove_cv_file):
     epoch2_dataset = []
     epoch3_dataset = []
     num_iter = 0
-    for item in d1s.create_dict_iterator():
+    for item in d1s.create_dict_iterator(num_epochs=1):
         logger.info(
             "-------------- item[data]: {}  -----------------------------".format(item["data"]))
         logger.info(

tests/ut/python/dataset/test_mixup_label_smoothing.py

@@ -45,7 +45,7 @@ def test_one_hot_op():
     golden_label = np.ones(num_classes) * epsilon_para / num_classes
     golden_label[1] = 1 - epsilon_para / num_classes
 
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         label = data["label"]
         logger.info("label is {}".format(label))
         logger.info("golden_label is {}".format(golden_label))
@@ -84,7 +84,7 @@ def test_mix_up_single():
                   ]
     ds1 = ds1.map(input_columns=["image", "label"], operations=transforms)
 
-    for data1, data2 in zip(ds1.create_dict_iterator(), ds2.create_dict_iterator()):
+    for data1, data2 in zip(ds1.create_dict_iterator(num_epochs=1), ds2.create_dict_iterator(num_epochs=1)):
         image1 = data1["image"]
         label = data1["label"]
         logger.info("label is {}".format(label))
@@ -134,7 +134,7 @@ def test_mix_up_multi():
     ds1 = ds1.map(input_columns=["image", "label"], operations=transforms)
     num_iter = 0
     batch1_image1 = 0
-    for data1, data2 in zip(ds1.create_dict_iterator(), ds2.create_dict_iterator()):
+    for data1, data2 in zip(ds1.create_dict_iterator(num_epochs=1), ds2.create_dict_iterator(num_epochs=1)):
         image1 = data1["image"]
         label1 = data1["label"]
         logger.info("label: {}".format(label1))

tests/ut/python/dataset/test_ngram_op.py

@@ -42,7 +42,7 @@ def test_multiple_ngrams():
     dataset = dataset.map(input_columns=["text"], operations=text.Ngram([1, 2, 3], ("_", 2), ("_", 2), " "))
 
     i = 0
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         assert [d.decode("utf8") for d in data["text"]] == n_gram_mottos[i]
         i += 1
 
@@ -64,7 +64,7 @@ def test_simple_ngram():
     dataset = dataset.map(input_columns=["text"], operations=text.Ngram(3, separator=" "))
 
     i = 0
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         assert [d.decode("utf8") for d in data["text"]] == n_gram_mottos[i], i
         i += 1
 
@@ -79,7 +79,7 @@ def test_corner_cases():
         try:
             dataset = ds.GeneratorDataset(gen(input_line), column_names=["text"])
             dataset = dataset.map(input_columns=["text"], operations=text.Ngram(n, l_pad, r_pad, separator=sep))
-            for data in dataset.create_dict_iterator():
+            for data in dataset.create_dict_iterator(num_epochs=1):
                 return [d.decode("utf8") for d in data["text"]]
         except (ValueError, TypeError) as e:
             return str(e)

tests/ut/python/dataset/test_nlp.py

@@ -38,7 +38,7 @@ def test_on_tokenized_line():
     data = data.map(input_columns=["text"], operations=lookup)
     res = np.array([[10, 1, 11, 1, 12, 1, 15, 1, 13, 1, 14],
                     [11, 1, 12, 1, 10, 1, 14, 1, 13, 1, 15]], dtype=np.int32)
-    for i, d in enumerate(data.create_dict_iterator()):
+    for i, d in enumerate(data.create_dict_iterator(num_epochs=1)):
         np.testing.assert_array_equal(d["text"], res[i])
 
 
@@ -56,7 +56,7 @@ def test_on_tokenized_line_with_no_special_tokens():
     data = data.map(input_columns=["text"], operations=lookup)
     res = np.array([[8, 0, 9, 0, 10, 0, 13, 0, 11, 0, 12],
                     [9, 0, 10, 0, 8, 0, 12, 0, 11, 0, 13]], dtype=np.int32)
-    for i, d in enumerate(data.create_dict_iterator()):
+    for i, d in enumerate(data.create_dict_iterator(num_epochs=1)):
         np.testing.assert_array_equal(d["text"], res[i])
 
 

tests/ut/python/dataset/test_noop_mode.py

@@ -24,7 +24,7 @@ def test_noop_pserver():
     os.environ['MS_ROLE'] = 'MS_PSERVER'
     data1 = ds.VOCDataset(DATA_DIR, task="Segmentation", mode="train", decode=True, shuffle=False)
     num = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num += 1
     assert num == 0
     del os.environ['MS_ROLE']
@@ -34,7 +34,7 @@ def test_noop_sched():
     os.environ['MS_ROLE'] = 'MS_SCHED'
     data1 = ds.VOCDataset(DATA_DIR, task="Segmentation", mode="train", decode=True, shuffle=False)
     num = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num += 1
     assert num == 0
     del os.environ['MS_ROLE']

tests/ut/python/dataset/test_normalizeOp.py

@@ -106,7 +106,7 @@ def test_normalize_op_c(plot=False):
     data2 = data2.map(input_columns=["image"], operations=decode_op)
 
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image_de_normalized = item1["image"]
         image_original = item2["image"]
         image_np_normalized = normalize_np(image_original, mean, std)
@@ -143,7 +143,7 @@ def test_normalize_op_py(plot=False):
     data2 = data2.map(input_columns=["image"], operations=transform())
 
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image_de_normalized = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_np_normalized = (normalize_np(item2["image"].transpose(1, 2, 0), mean, std) * 255).astype(np.uint8)
         image_original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
@@ -171,7 +171,7 @@ def test_decode_op():
     data1 = data1.map(input_columns=["image"], operations=decode_op)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         logger.info("Looping inside iterator {}".format(num_iter))
         _ = item["image"]
         num_iter += 1
@@ -194,7 +194,7 @@ def test_decode_normalize_op():
     data1 = data1.map(input_columns=["image"], operations=[decode_op, normalize_op])
 
     num_iter = 0
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         logger.info("Looping inside iterator {}".format(num_iter))
         _ = item["image"]
         num_iter += 1
@@ -263,7 +263,7 @@ def test_normalize_exception_invalid_size_py():
     logger.info("test_normalize_exception_invalid_size_py")
     data = util_test_normalize([0.75, 0.25], [0.18, 0.32], "python")
     try:
-        _ = data.create_dict_iterator().get_next()
+        _ = data.create_dict_iterator(num_epochs=1).get_next()
     except RuntimeError as e:
         logger.info("Got an exception in DE: {}".format(str(e)))
         assert "Length of mean and std must both be 1 or" in str(e)

tests/ut/python/dataset/test_onehot_op.py

@@ -90,7 +90,7 @@ def test_one_hot_post_aug():
     data1 = data1.batch(batch_size, drop_remainder=True)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         logger.info("image is: {}".format(item["image"]))
         logger.info("label is: {}".format(item["label"]))
         num_iter += 1

tests/ut/python/dataset/test_opt.py

@@ -37,7 +37,7 @@ def test_case_0():
 
     data1 = data1.batch(2)
 
-    for _ in data1.create_dict_iterator():  # each data is a dictionary
+    for _ in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         pass
 
 

tests/ut/python/dataset/test_opt_pass.py

@@ -30,7 +30,7 @@ def test_map_reorder0():
     data0 = data0.map(input_columns="col0", output_columns="out", columns_order=["col1", "out"],
                       operations=(lambda x: x))
 
-    for item in data0.create_tuple_iterator():  # each data is a dictionary
+    for item in data0.create_tuple_iterator(num_epochs=1):  # each data is a dictionary
         assert item == [np.array(1), np.array(0)]
 
 # tests the construction of multiple ops from a single dataset.
@@ -49,7 +49,7 @@ def test_map_reorder1():
     data2 = ds.zip((data0, data1))
     data2 = data2.map(input_columns="a0", columns_order=["b2", "a2", "b1", "a1", "b0", "a0"], operations=(lambda x: x))
 
-    for item in data2.create_tuple_iterator():
+    for item in data2.create_tuple_iterator(num_epochs=1):
         assert item == [np.array(2), np.array(2), np.array(1), np.array(1), np.array(0), np.array(0)]
 
 # tests the construction of multiple ops from a single dataset.

tests/ut/python/dataset/test_pad.py

@@ -55,7 +55,7 @@ def test_pad_op():
     data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
     data2 = data2.map(input_columns=["image"], operations=transform())
 
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         c_image = item1["image"]
         py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
 
@@ -93,7 +93,7 @@ def test_pad_grayscale():
     pad_gray = c_vision.Pad(100, fill_value=(20, 20, 20))
     data1 = data1.map(input_columns=["image"], operations=pad_gray)
     dataset_shape_1 = []
-    for item1 in data1.create_dict_iterator():
+    for item1 in data1.create_dict_iterator(num_epochs=1):
         c_image = item1["image"]
         dataset_shape_1.append(c_image.shape)
 
@@ -107,7 +107,7 @@ def test_pad_grayscale():
 
     data2 = data2.map(input_columns=["image"], operations=ctrans)
 
-    for item2 in data2.create_dict_iterator():
+    for item2 in data2.create_dict_iterator(num_epochs=1):
         c_image = item2["image"]
         dataset_shape_2.append(c_image.shape)
 

tests/ut/python/dataset/test_pad_batch.py

@@ -62,7 +62,7 @@ def test_batch_padding_01():
     data1 = ds.GeneratorDataset((lambda: gen_2cols(2)), ["col1d", "col2d"])
     data1 = data1.batch(batch_size=2, drop_remainder=False, pad_info={"col2d": ([2, 2], -2), "col1d": ([2], -1)})
     data1 = data1.repeat(2)
-    for data in data1.create_dict_iterator():
+    for data in data1.create_dict_iterator(num_epochs=1):
         np.testing.assert_array_equal([[0, -1], [1, -1]], data["col1d"])
         np.testing.assert_array_equal([[[100, -2], [200, -2]], [[101, -2], [201, -2]]], data["col2d"])
 
@@ -71,7 +71,7 @@ def test_batch_padding_02():
     data1 = ds.GeneratorDataset((lambda: gen_2cols(2)), ["col1d", "col2d"])
     data1 = data1.batch(batch_size=2, drop_remainder=False, pad_info={"col2d": ([1, 2], -2)})
     data1 = data1.repeat(2)
-    for data in data1.create_dict_iterator():
+    for data in data1.create_dict_iterator(num_epochs=1):
         np.testing.assert_array_equal([[0], [1]], data["col1d"])
         np.testing.assert_array_equal([[[100, -2]], [[101, -2]]], data["col2d"])
 
@@ -81,7 +81,7 @@ def test_batch_padding_03():
     data1 = data1.batch(batch_size=2, drop_remainder=False, pad_info={"col": (None, -1)})  # pad automatically
     data1 = data1.repeat(2)
     res = dict()
-    for ind, data in enumerate(data1.create_dict_iterator()):
+    for ind, data in enumerate(data1.create_dict_iterator(num_epochs=1)):
         res[ind] = data["col"].copy()
     np.testing.assert_array_equal(res[0], [[0, -1], [0, 1]])
     np.testing.assert_array_equal(res[1], [[0, 1, 2, -1], [0, 1, 2, 3]])
@@ -93,7 +93,7 @@ def test_batch_padding_04():
     data1 = ds.GeneratorDataset((lambda: gen_var_cols(2)), ["col1", "col2"])
     data1 = data1.batch(batch_size=2, drop_remainder=False, pad_info={})  # pad automatically
     data1 = data1.repeat(2)
-    for data in data1.create_dict_iterator():
+    for data in data1.create_dict_iterator(num_epochs=1):
         np.testing.assert_array_equal(data["col1"], [[0, 0], [0, 1]])
         np.testing.assert_array_equal(data["col2"], [[100, 0], [100, 101]])
 
@@ -102,7 +102,7 @@ def test_batch_padding_05():
     data1 = ds.GeneratorDataset((lambda: gen_var_cols_2d(3)), ["col1", "col2"])
     data1 = data1.batch(batch_size=3, drop_remainder=False,
                         pad_info={"col2": ([2, None], -2), "col1": (None, -1)})  # pad automatically
-    for data in data1.create_dict_iterator():
+    for data in data1.create_dict_iterator(num_epochs=1):
         np.testing.assert_array_equal(data["col1"], [[[0, -1, -1]], [[0, 1, -1]], [[0, 1, 2]]])
         np.testing.assert_array_equal(data["col2"], [[[100, -2, -2], [-2, -2, -2]], [[100, 101, -2], [-2, -2, -2]],
                                                      [[100, 101, 102], [-2, -2, -2]]])
@@ -117,7 +117,7 @@ def batch_padding_performance_3d():
     data1 = data1.batch(batch_size=24, drop_remainder=True, pad_info=pad_info)
     start_time = time.time()
     num_batches = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_batches += 1
     _ = "total number of batch:" + str(num_batches) + " time elapsed:" + str(time.time() - start_time)
     # print(res)
@@ -133,7 +133,7 @@ def batch_padding_performance_1d():
     data1 = data1.batch(batch_size=24, drop_remainder=True, pad_info=pad_info)
     start_time = time.time()
     num_batches = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_batches += 1
     _ = "total number of batch:" + str(num_batches) + " time elapsed:" + str(time.time() - start_time)
     # print(res)
@@ -149,7 +149,7 @@ def batch_pyfunc_padding_3d():
     data1 = data1.batch(batch_size=24, drop_remainder=True)
     start_time = time.time()
     num_batches = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_batches += 1
     _ = "total number of batch:" + str(num_batches) + " time elapsed:" + str(time.time() - start_time)
     # print(res)
@@ -164,7 +164,7 @@ def batch_pyfunc_padding_1d():
     data1 = data1.batch(batch_size=24, drop_remainder=True)
     start_time = time.time()
     num_batches = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_batches += 1
     _ = "total number of batch:" + str(num_batches) + " time elapsed:" + str(time.time() - start_time)
     # print(res)
@@ -180,7 +180,7 @@ def test_pad_via_map():
         data1 = data1.map(input_columns="image", operations=(lambda x: np.pad(x, (0, 816))))
         data1 = data1.batch(batch_size=25, drop_remainder=True)
         res = []
-        for data in data1.create_dict_iterator():
+        for data in data1.create_dict_iterator(num_epochs=1):
             res.append(data["image"])
         return res
 
@@ -189,7 +189,7 @@ def test_pad_via_map():
         data2 = data2.map(input_columns="image", operations=(lambda x: x.reshape(-1)))  # reshape to 1d
         data2 = data2.batch(batch_size=25, drop_remainder=True, pad_info={"image": ([3888], 0)})
         res = []
-        for data in data2.create_dict_iterator():
+        for data in data2.create_dict_iterator(num_epochs=1):
             res.append(data["image"])
         return res
 

tests/ut/python/dataset/test_paddeddataset.py

@@ -52,7 +52,7 @@ def test_TFRecord_Padded():
         testsampler = ds.DistributedSampler(num_shards=shard_num, shard_id=i, shuffle=False, num_samples=None)
         concat_ds.use_sampler(testsampler)
         shard_list = []
-        for item in concat_ds.create_dict_iterator():
+        for item in concat_ds.create_dict_iterator(num_epochs=1):
             shard_list.append(len(item['image']))
         verify_list.append(shard_list)
     assert verify_list == result_list
@@ -74,7 +74,7 @@ def test_GeneratorDataSet_Padded():
         distributed_sampler = ds.DistributedSampler(num_shards=shard_num, shard_id=i, shuffle=False, num_samples=None)
         data3.use_sampler(distributed_sampler)
         tem_list = []
-        for ele in data3.create_dict_iterator():
+        for ele in data3.create_dict_iterator(num_epochs=1):
             tem_list.append(ele['col1'][0])
         verify_list.append(tem_list)
 
@@ -98,7 +98,7 @@ def test_Reapeat_afterPadded():
     ds3.use_sampler(testsampler)
     repeat_num = 2
     ds3 = ds3.repeat(repeat_num)
-    for item in ds3.create_dict_iterator():
+    for item in ds3.create_dict_iterator(num_epochs=1):
         verify_list.append(len(item['image']))
 
     assert verify_list == result_list * repeat_num
@@ -140,7 +140,7 @@ def test_Unevenly_distributed():
         tem_list = []
         testsampler = ds.DistributedSampler(num_shards=numShard, shard_id=i, shuffle=False, num_samples=None)
         ds3.use_sampler(testsampler)
-        for item in ds3.create_dict_iterator():
+        for item in ds3.create_dict_iterator(num_epochs=1):
             tem_list.append(len(item['image']))
         verify_list.append(tem_list)
     assert verify_list == result_list
@@ -164,7 +164,7 @@ def test_three_datasets_connected():
         distributed_sampler = ds.DistributedSampler(num_shards=shard_num, shard_id=i, shuffle=False, num_samples=None)
         data4.use_sampler(distributed_sampler)
         tem_list = []
-        for ele in data4.create_dict_iterator():
+        for ele in data4.create_dict_iterator(num_epochs=1):
             tem_list.append(ele['col1'][0])
         verify_list.append(tem_list)
 
@@ -220,7 +220,7 @@ def test_imagefolder_padded():
     assert sum([1 for _ in data3]) == 10
     verify_list = []
 
-    for ele in data3.create_dict_iterator():
+    for ele in data3.create_dict_iterator(num_epochs=1):
         verify_list.append(len(ele['image']))
     assert verify_list[8] == 1
     assert verify_list[9] == 6
@@ -246,7 +246,7 @@ def test_imagefolder_padded_with_decode():
         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():
+        for ele in data3.create_dict_iterator(num_epochs=1):
             print("label: {}".format(ele['label']))
             count += 1
             shard_sample_count += 1
@@ -275,7 +275,7 @@ def test_imagefolder_padded_with_decode_and_get_dataset_size():
         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():
+        for ele in data3.create_dict_iterator(num_epochs=1):
             print("label: {}".format(ele['label']))
             count += 1
             shard_sample_count += 1
@@ -298,7 +298,7 @@ def test_more_shard_padded():
         tem_list = []
         testsampler = ds.DistributedSampler(num_shards=numShard, shard_id=i, shuffle=False, num_samples=None)
         data3.use_sampler(testsampler)
-        for item in data3.create_dict_iterator():
+        for item in data3.create_dict_iterator(num_epochs=1):
             tem_list.append(item['col1'])
         vertifyList.append(tem_list)
 
@@ -324,7 +324,7 @@ def test_more_shard_padded():
         tem_list = []
         testsampler = ds.DistributedSampler(num_shards=numShard, shard_id=i, shuffle=False, num_samples=None)
         ds3.use_sampler(testsampler)
-        for item in ds3.create_dict_iterator():
+        for item in ds3.create_dict_iterator(num_epochs=1):
             tem_list.append(len(item['image']))
         vertifyList1.append(tem_list)
 
@@ -408,7 +408,7 @@ def test_Mindrecord_Padded(remove_mindrecord_file):
         testsampler = ds.DistributedSampler(num_shards=shard_num, shard_id=i, shuffle=False, num_samples=None)
         ds2.use_sampler(testsampler)
         tem_list = []
-        for ele in ds2.create_dict_iterator():
+        for ele in ds2.create_dict_iterator(num_epochs=1):
             tem_list.append(int(ele['file_name'].tostring().decode().lstrip('image_').rstrip('.jpg')))
         result_list.append(tem_list)
     assert result_list == verify_list
@@ -421,7 +421,7 @@ def test_clue_padded_and_skip_with_0_samples():
 
     data = ds.CLUEDataset(TRAIN_FILE, task='AFQMC', usage='train')
     count = 0
-    for _ in data.create_dict_iterator():
+    for _ in data.create_dict_iterator(num_epochs=1):
         count += 1
     assert count == 3
 
@@ -437,20 +437,20 @@ def test_clue_padded_and_skip_with_0_samples():
     dataset.use_sampler(testsampler)
     assert dataset.get_dataset_size() == 2
     count = 0
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         count += 1
     assert count == 2
 
     dataset = dataset.skip(count=2)    # dataset2 has none samples
     count = 0
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         count += 1
     assert count == 0
 
     with pytest.raises(ValueError, match="There is no samples in the "):
         dataset = dataset.concat(data_copy1)
         count = 0
-        for data in dataset.create_dict_iterator():
+        for data in dataset.create_dict_iterator(num_epochs=1):
             count += 1
         assert count == 2
 

tests/ut/python/dataset/test_pair_truncate.py

@@ -24,7 +24,7 @@ import mindspore.dataset.text as text
 def compare(in1, in2, length, out1, out2):
     data = ds.NumpySlicesDataset({"s1": [in1], "s2": [in2]})
     data = data.map(input_columns=["s1", "s2"], operations=text.TruncateSequencePair(length))
-    for d in data.create_dict_iterator():
+    for d in data.create_dict_iterator(num_epochs=1):
         np.testing.assert_array_equal(out1, d["s1"])
         np.testing.assert_array_equal(out2, d["s2"])
 

tests/ut/python/dataset/test_pyfunc.py

@@ -36,7 +36,7 @@ def test_case_0():
     data1 = data1.map(input_columns="col0", output_columns="out", operations=(lambda x: x + x))
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # In this test, the dataset is 2x2 sequential tensors
         golden = np.array([[i * 2, (i + 1) * 2], [(i + 2) * 2, (i + 3) * 2]])
         np.testing.assert_array_equal(item["out"], golden)
@@ -57,7 +57,7 @@ def test_case_1():
                       columns_order=["out0", "out1"])
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # In this test, the dataset is 2x2 sequential tensors
         golden = np.array([[i, i + 1], [i + 2, i + 3]])
         np.testing.assert_array_equal(item["out0"], golden)
@@ -81,7 +81,7 @@ def test_case_2():
                       columns_order=["out"])
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # In this test, the dataset is 2x2 sequential tensors
         golden = np.array([[i * 2, (i + 1) * 2], [(i + 2) * 2, (i + 3) * 2]])
         np.testing.assert_array_equal(item["out"], golden)
@@ -103,7 +103,7 @@ def test_case_3():
                       operations=(lambda x, y: (x, x + y, x + y + 1)), columns_order=["out0", "out1", "out2"])
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # In this test, the dataset is 2x2 sequential tensors
         golden = np.array([[i, i + 1], [i + 2, i + 3]])
         np.testing.assert_array_equal(item["out0"], golden)
@@ -129,7 +129,7 @@ def test_case_4():
                       operations=(lambda x, y: (x, x + y, x + y + 1)), columns_order=["out0", "out1", "out2"])
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # In this test, the dataset is 2x2 sequential tensors
         golden = np.array([[i, i + 1], [i + 2, i + 3]])
         np.testing.assert_array_equal(item["out0"], golden)
@@ -156,7 +156,7 @@ def test_case_5():
 
     data1 = data1.map(input_columns="col0", output_columns="out", operations=func_5)
 
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # In this test, the dataset is 2x2 sequential tensors
         golden = np.array([[1, 1], [1, 1]])
         np.testing.assert_array_equal(item["out"], golden)
@@ -175,7 +175,7 @@ def test_case_6():
                       operations=[(lambda x: x + x), (lambda x: x + x)])
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # In this test, the dataset is 2x2 sequential tensors
         golden = np.array([[i * 4, (i + 1) * 4], [(i + 2) * 4, (i + 3) * 4]])
         np.testing.assert_array_equal(item["out"], golden)
@@ -195,7 +195,7 @@ def test_case_7():
                       num_parallel_workers=4, python_multiprocessing=True)
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # In this test, the dataset is 2x2 sequential tensors
         golden = np.array([[i * 2, (i + 1) * 2], [(i + 2) * 2, (i + 3) * 2]])
         np.testing.assert_array_equal(item["out"], golden)
@@ -218,7 +218,7 @@ def test_case_8():
                       python_multiprocessing=True)
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # In this test, the dataset is 2x2 sequential tensors
         golden = np.array([[i, i + 1], [i + 2, i + 3]])
         np.testing.assert_array_equal(item["out0"], golden)
@@ -243,7 +243,7 @@ def test_case_9():
                       num_parallel_workers=4, python_multiprocessing=True)
 
     i = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # In this test, the dataset is 2x2 sequential tensors
         golden = np.array([[i * 2 + 3, (i + 1) * 2 + 3], [(i + 2) * 2 + 3, (i + 3) * 2 + 3]])
         np.testing.assert_array_equal(item["out"], golden)

tests/ut/python/dataset/test_python_tokenizer.py

@@ -41,7 +41,7 @@ def test_whitespace_tokenizer_ch():
     tokenizer = text.PythonTokenizer(my_tokenizer)
     dataset = dataset.map(operations=tokenizer, num_parallel_workers=1)
     tokens = []
-    for i in dataset.create_dict_iterator():
+    for i in dataset.create_dict_iterator(num_epochs=1):
         s = text.to_str(i['text']).tolist()
         tokens.append(s)
     logger.info("The out tokens is : {}".format(tokens))

tests/ut/python/dataset/test_random_affine.py

@@ -58,7 +58,7 @@ def test_random_affine_op(plot=False):
 
     image_affine = []
     image_original = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_affine.append(image1)
@@ -91,7 +91,7 @@ def test_random_affine_op_c(plot=False):
 
     image_affine = []
     image_original = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image1 = item1["image"]
         image2 = item2["image"]
         image_affine.append(image1)
@@ -193,7 +193,7 @@ def test_random_affine_py_exception_non_pil_images():
                                          py_vision.RandomAffine(degrees=(15, 15))])
         dataset = dataset.map(input_columns=["image"], operations=transform(), num_parallel_workers=3,
                               python_multiprocessing=True)
-        for _ in dataset.create_dict_iterator():
+        for _ in dataset.create_dict_iterator(num_epochs=1):
             break
     except RuntimeError as e:
         logger.info("Got an exception in DE: {}".format(str(e)))

tests/ut/python/dataset/test_random_apply.py

@@ -57,7 +57,7 @@ def test_random_apply_op(plot=False):
 
     image_apply = []
     image_original = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_apply.append(image1)
@@ -118,7 +118,7 @@ def test_random_apply_exception_random_crop_badinput():
     data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
     data = data.map(input_columns=["image"], operations=transform())
     try:
-        _ = data.create_dict_iterator().get_next()
+        _ = data.create_dict_iterator(num_epochs=1).get_next()
     except RuntimeError as e:
         logger.info("Got an exception in DE: {}".format(str(e)))
         assert "Crop size" in str(e)

tests/ut/python/dataset/test_random_choice.py

@@ -54,7 +54,7 @@ def test_random_choice_op(plot=False):
 
     image_choice = []
     image_original = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_choice.append(image1)
@@ -93,7 +93,7 @@ def test_random_choice_comp(plot=False):
 
     image_choice = []
     image_original = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_choice.append(image1)
@@ -124,7 +124,7 @@ def test_random_choice_exception_random_crop_badinput():
     data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
     data = data.map(input_columns=["image"], operations=transform())
     try:
-        _ = data.create_dict_iterator().get_next()
+        _ = data.create_dict_iterator(num_epochs=1).get_next()
     except RuntimeError as e:
         logger.info("Got an exception in DE: {}".format(str(e)))
         assert "Crop size" in str(e)

tests/ut/python/dataset/test_random_color.py

@@ -118,7 +118,7 @@ def test_random_color_c(degrees=(0.1, 1.9), plot=False, run_golden=True):
     image_random_color_op = []
     image = []
 
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         actual = item1["image"]
         expected = item2["image"]
         image.append(actual)
@@ -193,7 +193,7 @@ def test_compare_random_color_op(degrees=None, plot=False):
     image_random_color_op = []
     image = []
 
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         actual = item1["image"]
         expected = item2["image"]
         image_random_color_op.append(actual)

tests/ut/python/dataset/test_random_color_adjust.py

@@ -52,7 +52,7 @@ def util_test_random_color_adjust_error(brightness=(1, 1), contrast=(1, 1), satu
     with pytest.raises(RuntimeError) as info:
         data1 = data1.map(input_columns=["image"], operations=random_adjust_op)
         dataset_shape_1 = []
-        for item1 in data1.create_dict_iterator():
+        for item1 in data1.create_dict_iterator(num_epochs=1):
             c_image = item1["image"]
             dataset_shape_1.append(c_image.shape)
 
@@ -91,7 +91,7 @@ def util_test_random_color_adjust_op(brightness=(1, 1), contrast=(1, 1), saturat
     data2 = data2.map(input_columns=["image"], operations=transform())
 
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         num_iter += 1
         c_image = item1["image"]
         py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)

tests/ut/python/dataset/test_random_crop.py

@@ -51,7 +51,7 @@ def test_random_crop_op_c(plot=False):
 
     image_cropped = []
     image = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image1 = item1["image"]
         image2 = item2["image"]
         image_cropped.append(image1)
@@ -85,7 +85,7 @@ def test_random_crop_op_py(plot=False):
 
     crop_images = []
     original_images = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         crop = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         crop_images.append(crop)
@@ -254,7 +254,7 @@ def test_random_crop_04_c():
     data = data.map(input_columns=["image"], operations=decode_op)
     data = data.map(input_columns=["image"], operations=random_crop_op)
     try:
-        data.create_dict_iterator().get_next()
+        data.create_dict_iterator(num_epochs=1).get_next()
     except RuntimeError as e:
         logger.info("Got an exception in DE: {}".format(str(e)))
         assert "Crop size is greater than the image dim" in str(e)
@@ -277,7 +277,7 @@ def test_random_crop_04_py():
     transform = py_vision.ComposeOp(transforms)
     data = data.map(input_columns=["image"], operations=transform())
     try:
-        data.create_dict_iterator().get_next()
+        data.create_dict_iterator(num_epochs=1).get_next()
     except RuntimeError as e:
         logger.info("Got an exception in DE: {}".format(str(e)))
         assert "Crop size" in str(e)
@@ -497,7 +497,7 @@ def test_random_crop_09():
     transform = py_vision.ComposeOp(transforms)
     data = data.map(input_columns=["image"], operations=transform())
     try:
-        data.create_dict_iterator().get_next()
+        data.create_dict_iterator(num_epochs=1).get_next()
     except RuntimeError as e:
         logger.info("Got an exception in DE: {}".format(str(e)))
         assert "should be PIL Image" in str(e)
@@ -528,7 +528,7 @@ def test_random_crop_comp(plot=False):
 
     image_c_cropped = []
     image_py_cropped = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         c_image = item1["image"]
         py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_c_cropped.append(c_image)

tests/ut/python/dataset/test_random_crop_and_resize.py

@@ -52,7 +52,7 @@ def test_random_crop_and_resize_op_c(plot=False):
     num_iter = 0
     crop_and_resize_images = []
     original_images = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         crop_and_resize = item1["image"]
         original = item2["image"]
         # Note: resize the original image with the same size as the one applied RandomResizedCrop()
@@ -94,7 +94,7 @@ def test_random_crop_and_resize_op_py(plot=False):
     num_iter = 0
     crop_and_resize_images = []
     original_images = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         crop_and_resize = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         original = cv2.resize(original, (512, 256))
@@ -326,7 +326,7 @@ def test_random_crop_and_resize_comp(plot=False):
 
     image_c_cropped = []
     image_py_cropped = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         c_image = item1["image"]
         py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_c_cropped.append(c_image)

tests/ut/python/dataset/test_random_crop_and_resize_with_bbox.py

@@ -58,7 +58,7 @@ def test_random_resized_crop_with_bbox_op_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -92,7 +92,7 @@ def test_random_resized_crop_with_bbox_op_coco_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()):
+    for unAug, Aug in zip(dataCoco1.create_dict_iterator(num_epochs=1), dataCoco2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -127,7 +127,7 @@ def test_random_resized_crop_with_bbox_op_edge_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -154,7 +154,7 @@ def test_random_resized_crop_with_bbox_op_invalid_c():
                                 columns_order=["image", "bbox"],
                                 operations=[test_op])
 
-        for _ in dataVoc2.create_dict_iterator():
+        for _ in dataVoc2.create_dict_iterator(num_epochs=1):
             break
 
     except ValueError as err:
@@ -180,7 +180,7 @@ def test_random_resized_crop_with_bbox_op_invalid2_c():
                                 columns_order=["image", "bbox"],
                                 operations=[test_op])
 
-        for _ in dataVoc2.create_dict_iterator():
+        for _ in dataVoc2.create_dict_iterator(num_epochs=1):
             break
 
     except ValueError as err:

tests/ut/python/dataset/test_random_crop_decode_resize.py

@@ -46,7 +46,7 @@ def test_random_crop_decode_resize_op(plot=False):
 
 
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         if num_iter > 0:
             break
         image1 = item1["image"]

tests/ut/python/dataset/test_random_crop_with_bbox.py

@@ -53,7 +53,7 @@ def test_random_crop_with_bbox_op_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -83,7 +83,7 @@ def test_random_crop_with_bbox_op_coco_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()):
+    for unAug, Aug in zip(dataCoco1.create_dict_iterator(num_epochs=1), dataCoco2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -118,7 +118,7 @@ def test_random_crop_with_bbox_op2_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -152,7 +152,7 @@ def test_random_crop_with_bbox_op3_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -190,7 +190,7 @@ def test_random_crop_with_bbox_op_edge_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -217,7 +217,7 @@ def test_random_crop_with_bbox_op_invalid_c():
                                 columns_order=["image", "bbox"],
                                 operations=[test_op])  # Add column for "bbox"
 
-        for _ in dataVoc2.create_dict_iterator():
+        for _ in dataVoc2.create_dict_iterator(num_epochs=1):
             break
     except TypeError as err:
         logger.info("Got an exception in DE: {}".format(str(err)))
@@ -257,7 +257,7 @@ def test_random_crop_with_bbox_op_bad_padding():
                                 columns_order=["image", "bbox"],
                                 operations=[test_op])
 
-        for _ in dataVoc2.create_dict_iterator():
+        for _ in dataVoc2.create_dict_iterator(num_epochs=1):
             break
     except ValueError as err:
         logger.info("Got an exception in DE: {}".format(str(err)))
@@ -271,7 +271,7 @@ def test_random_crop_with_bbox_op_bad_padding():
                                 columns_order=["image", "bbox"],
                                 operations=[test_op])
 
-        for _ in dataVoc2.create_dict_iterator():
+        for _ in dataVoc2.create_dict_iterator(num_epochs=1):
             break
     except RuntimeError as err:
         logger.info("Got an exception in DE: {}".format(str(err)))

tests/ut/python/dataset/test_random_dataset.py

@@ -29,7 +29,7 @@ def test_randomdataset_basic1():
     ds1 = ds1.repeat(4)
 
     num_iter = 0
-    for data in ds1.create_dict_iterator():  # each data is a dictionary
+    for data in ds1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         logger.info("{} image: {}".format(num_iter, data["image"]))
         logger.info("{} label: {}".format(num_iter, data["label"]))
@@ -54,7 +54,7 @@ def test_randomdataset_basic2():
     ds1 = ds1.repeat(4)
 
     num_iter = 0
-    for data in ds1.create_dict_iterator():  # each data is a dictionary
+    for data in ds1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         # in this example, each dictionary has keys "image" and "label"
         # logger.info(data["image"])
         logger.info("printing the label: {}".format(data["label"]))
@@ -77,7 +77,7 @@ def test_randomdataset_basic3():
     ds1 = ds1.repeat(2)
 
     num_iter = 0
-    for _ in ds1.create_tuple_iterator():
+    for _ in ds1.create_tuple_iterator(num_epochs=1):
         num_iter += 1
 
     logger.info("Number of data in ds1: {}".format(num_iter))

tests/ut/python/dataset/test_random_erasing.py

@@ -55,7 +55,7 @@ def test_random_erasing_op(plot=False):
     data2 = data2.map(input_columns=["image"], operations=transform_2())
 
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         num_iter += 1
         image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)

tests/ut/python/dataset/test_random_grayscale.py

@@ -55,7 +55,7 @@ def test_random_grayscale_valid_prob(plot=False):
 
     image_gray = []
     image = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_gray.append(image1)
@@ -94,7 +94,7 @@ def test_random_grayscale_input_grayscale_images():
 
     image_gray = []
     image = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_gray.append(image1)

tests/ut/python/dataset/test_random_horizontal_flip.py

@@ -58,7 +58,7 @@ def test_random_horizontal_op(plot=False):
     data2 = data2.map(input_columns=["image"], operations=decode_op)
 
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
 
         # with the seed value, we can only guarantee the first number generated
         if num_iter > 0:
@@ -193,7 +193,7 @@ def test_random_horizontal_comp(plot=False):
 
     images_list_c = []
     images_list_py = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image_c = item1["image"]
         image_py = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         images_list_c.append(image_c)

tests/ut/python/dataset/test_random_horizontal_flip_with_bbox.py

@@ -52,7 +52,7 @@ def test_random_horizontal_flip_with_bbox_op_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -82,7 +82,7 @@ def test_random_horizontal_flip_with_bbox_op_coco_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()):
+    for unAug, Aug in zip(dataCoco1.create_dict_iterator(num_epochs=1), dataCoco2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -121,7 +121,7 @@ def test_random_horizontal_flip_with_bbox_valid_rand_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -164,7 +164,7 @@ def test_random_horizontal_flip_with_bbox_valid_edge_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 

tests/ut/python/dataset/test_random_order.py

@@ -57,7 +57,7 @@ def test_random_order_op(plot=False):
 
     image_order = []
     image_original = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_order.append(image1)

tests/ut/python/dataset/test_random_perspective.py

@@ -58,7 +58,7 @@ def test_random_perspective_op(plot=False):
 
     image_perspective = []
     image_original = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         image_perspective.append(image1)

tests/ut/python/dataset/test_random_posterize.py

@@ -53,7 +53,7 @@ def test_random_posterize_op_c(plot=False, run_golden=False):
 
     image_posterize = []
     image_original = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image1 = item1["image"]
         image2 = item2["image"]
         image_posterize.append(image1)
@@ -99,7 +99,7 @@ def test_random_posterize_op_fixed_point_c(plot=False, run_golden=True):
 
     image_posterize = []
     image_original = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image1 = item1["image"]
         image2 = item2["image"]
         image_posterize.append(image1)

tests/ut/python/dataset/test_random_resize.py

@@ -44,7 +44,7 @@ def test_random_resize_op(plot=False):
     image_original = []
     image_resized = []
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image_1 = item1["image"]
         image_2 = item2["image"]
         image_original.append(image_1)

tests/ut/python/dataset/test_random_resize_with_bbox.py

@@ -58,7 +58,7 @@ def test_random_resize_with_bbox_op_voc_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -101,7 +101,7 @@ def test_random_resize_with_bbox_op_rand_coco_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()):
+    for unAug, Aug in zip(dataCoco1.create_dict_iterator(num_epochs=1), dataCoco2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -143,7 +143,7 @@ def test_random_resize_with_bbox_op_edge_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 

tests/ut/python/dataset/test_random_rotation.py

@@ -50,7 +50,7 @@ def test_random_rotation_op_c(plot=False):
     data2 = data2.map(input_columns=["image"], operations=decode_op)
 
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         if num_iter > 0:
             break
         rotation_de = item1["image"]
@@ -86,7 +86,7 @@ def test_random_rotation_op_py(plot=False):
     data2 = data2.map(input_columns=["image"], operations=transform2())
 
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         if num_iter > 0:
             break
         rotation_de = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
@@ -116,7 +116,7 @@ def test_random_rotation_expand():
     data1 = data1.map(input_columns=["image"], operations=random_rotation_op)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         rotation = item["image"]
         logger.info("shape after rotate: {}".format(rotation.shape))
         num_iter += 1
@@ -192,7 +192,7 @@ def test_rotation_diff(plot=False):
 
     num_iter = 0
     image_list_c, image_list_py = [], []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         num_iter += 1
         c_image = item1["image"]
         py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)

tests/ut/python/dataset/test_random_select_subpolicy.py

@@ -26,7 +26,7 @@ def test_random_select_subpolicy():
             data = ds.NumpySlicesDataset(arr, column_names="col", shuffle=False)
             data = data.map(input_columns=["col"], operations=visions.RandomSelectSubpolicy(policy))
             res = []
-            for i in data.create_dict_iterator():
+            for i in data.create_dict_iterator(num_epochs=1):
                 res.append(i["col"].tolist())
             return res
         except (TypeError, ValueError) as e:

tests/ut/python/dataset/test_random_solarize_op.py

@@ -62,7 +62,7 @@ def test_random_solarize_op(threshold=(10, 150), plot=False, run_golden=True):
     image_solarized = []
     image = []
 
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image_solarized.append(item1["image"].copy())
         image.append(item2["image"].copy())
     if plot:

tests/ut/python/dataset/test_random_vertical_flip.py

@@ -58,7 +58,7 @@ def test_random_vertical_op(plot=False):
     data2 = data2.map(input_columns=["image"], operations=decode_op)
 
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
 
         # with the seed value, we can only guarantee the first number generated
         if num_iter > 0:
@@ -193,7 +193,7 @@ def test_random_vertical_comp(plot=False):
 
     images_list_c = []
     images_list_py = []
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image_c = item1["image"]
         image_py = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
         images_list_c.append(image_c)

tests/ut/python/dataset/test_random_vertical_flip_with_bbox.py

@@ -53,7 +53,7 @@ def test_random_vertical_flip_with_bbox_op_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -84,7 +84,7 @@ def test_random_vertical_flip_with_bbox_op_coco_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()):
+    for unAug, Aug in zip(dataCoco1.create_dict_iterator(num_epochs=1), dataCoco2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -121,7 +121,7 @@ def test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -161,7 +161,7 @@ def test_random_vertical_flip_with_bbox_op_edge_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -186,7 +186,7 @@ def test_random_vertical_flip_with_bbox_op_invalid_c():
                                 columns_order=["image", "bbox"],
                                 operations=[test_op])
 
-        for _ in dataVoc2.create_dict_iterator():
+        for _ in dataVoc2.create_dict_iterator(num_epochs=1):
             break
 
     except ValueError as err:

tests/ut/python/dataset/test_rename.py

@@ -34,7 +34,7 @@ def test_rename():
 
     num_iter = 0
 
-    for _, item in enumerate(data.create_dict_iterator()):
+    for _, item in enumerate(data.create_dict_iterator(num_epochs=1)):
         logger.info("item[mask] is {}".format(item["masks"]))
         np.testing.assert_equal(item["masks"], item["input_ids"])
         logger.info("item[seg_ids] is {}".format(item["seg_ids"]))

tests/ut/python/dataset/test_repeat.py

@@ -84,7 +84,7 @@ def test_tf_repeat_03():
     data1 = data1.batch(batch_size, drop_remainder=True)
 
     num_iter = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_iter += 1
     logger.info("Number of tf data in data1: {}".format(num_iter))
     assert num_iter == 2
@@ -267,7 +267,7 @@ def test_repeat_count1():
     dataset_size = data1.get_dataset_size()
     logger.info("dataset repeat then batch's size is {}".format(dataset_size))
     num1_iter = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num1_iter += 1
 
     assert data1_size == 3
@@ -289,7 +289,7 @@ def test_repeat_count2():
     dataset_size = data1.get_dataset_size()
     logger.info("dataset batch then repeat's size is {}".format(dataset_size))
     num1_iter = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num1_iter += 1
 
     assert data1_size == 3

tests/ut/python/dataset/test_rescale_op.py

@@ -42,7 +42,7 @@ def get_rescaled(image_id):
     decode_op = vision.Decode()
     data1 = data1.map(input_columns=["image"], operations=decode_op)
     num_iter = 0
-    for item in data1.create_dict_iterator():
+    for item in data1.create_dict_iterator(num_epochs=1):
         image = item["image"]
         if num_iter == image_id:
             return rescale_np(image)
@@ -68,7 +68,7 @@ def test_rescale_op(plot=False):
     data2 = data1.map(input_columns=["image"], operations=rescale_op)
 
     num_iter = 0
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         image_original = item1["image"]
         image_de_rescaled = item2["image"]
         image_np_rescaled = get_rescaled(num_iter)

tests/ut/python/dataset/test_resize.py

@@ -47,7 +47,7 @@ def test_resize_op(plot=False):
         data2 = data1.map(input_columns=["image"], operations=resize_op)
         image_original = []
         image_resized = []
-        for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
             image_1 = item1["image"]
             image_2 = item2["image"]
             image_original.append(image_1)
@@ -79,7 +79,7 @@ def test_resize_md5(plot=False):
         # Compare with expected md5 from images
         save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
 
-        for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
             image_1 = item1["image"]
             image_2 = item2["image"]
             image_original.append(image_1)

tests/ut/python/dataset/test_resize_with_bbox.py

@@ -58,7 +58,7 @@ def test_resize_with_bbox_op_voc_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -95,7 +95,7 @@ def test_resize_with_bbox_op_coco_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataCOCO1.create_dict_iterator(), dataCOCO2.create_dict_iterator()):
+    for unAug, Aug in zip(dataCOCO1.create_dict_iterator(num_epochs=1), dataCOCO2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 
@@ -133,7 +133,7 @@ def test_resize_with_bbox_op_edge_c(plot_vis=False):
 
     unaugSamp, augSamp = [], []
 
-    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
         unaugSamp.append(unAug)
         augSamp.append(Aug)
 

tests/ut/python/dataset/test_rgb_hsv.py

@@ -154,7 +154,7 @@ def test_rgb_hsv_pipeline():
     ds2 = ds2.map(input_columns=["image"], operations=transform2())
 
     num_iter = 0
-    for data1, data2 in zip(ds1.create_dict_iterator(), ds2.create_dict_iterator()):
+    for data1, data2 in zip(ds1.create_dict_iterator(num_epochs=1), ds2.create_dict_iterator(num_epochs=1)):
         num_iter += 1
         ori_img = data1["image"]
         cvt_img = data2["image"]

tests/ut/python/dataset/test_sampler.py

@@ -33,7 +33,7 @@ def test_sequential_sampler(print_res=False):
         if num_repeats is not None:
             data1 = data1.repeat(num_repeats)
         res = []
-        for item in data1.create_dict_iterator():
+        for item in data1.create_dict_iterator(num_epochs=1):
             logger.info("item[image].shape[0]: {}, item[label].item(): {}"
                         .format(item["image"].shape[0], item["label"].item()))
             res.append(map_[(item["image"].shape[0], item["label"].item())])
@@ -55,7 +55,7 @@ def test_random_sampler(print_res=False):
         data1 = ds.ManifestDataset(manifest_file, sampler=sampler)
         data1 = data1.repeat(num_repeats)
         res = []
-        for item in data1.create_dict_iterator():
+        for item in data1.create_dict_iterator(num_epochs=1):
             res.append(map_[(item["image"].shape[0], item["label"].item())])
         if print_res:
             logger.info("image.shapes and labels: {}".format(res))
@@ -78,7 +78,7 @@ def test_random_sampler_multi_iter(print_res=False):
         data1 = ds.ManifestDataset(manifest_file, sampler=sampler)
         while num_repeats > 0:
             res = []
-            for item in data1.create_dict_iterator():
+            for item in data1.create_dict_iterator(num_epochs=1):
                 res.append(map_[(item["image"].shape[0], item["label"].item())])
             if print_res:
                 logger.info("image.shapes and labels: {}".format(res))
@@ -135,7 +135,7 @@ def test_python_sampler():
         if num_repeats is not None:
             data1 = data1.repeat(num_repeats)
         res = []
-        for item in data1.create_dict_iterator():
+        for item in data1.create_dict_iterator(num_epochs=1):
             logger.info("item[image].shape[0]: {}, item[label].item(): {}"
                         .format(item["image"].shape[0], item["label"].item()))
             res.append(map_[(item["image"].shape[0], item["label"].item())])
@@ -174,7 +174,7 @@ def test_subset_sampler():
         d = ds.ManifestDataset(manifest_file, sampler=sampler)
 
         res = []
-        for item in d.create_dict_iterator():
+        for item in d.create_dict_iterator(num_epochs=1):
             res.append(map_[(item["image"].shape[0], item["label"].item())])
 
         return res
@@ -202,7 +202,7 @@ def test_sampler_chain():
         data1 = ds.ManifestDataset(manifest_file, sampler=sampler)
 
         res = []
-        for item in data1.create_dict_iterator():
+        for item in data1.create_dict_iterator(num_epochs=1):
             logger.info("item[image].shape[0]: {}, item[label].item(): {}"
                         .format(item["image"].shape[0], item["label"].item()))
             res.append(map_[(item["image"].shape[0], item["label"].item())])

tests/ut/python/dataset/test_save_op.py

@@ -109,7 +109,7 @@ def test_case_00(add_and_remove_cv_file):  # only bin data
                         shuffle=False)
     assert d2.get_dataset_size() == 5
     num_iter = 0
-    for item in d2.create_dict_iterator():
+    for item in d2.create_dict_iterator(num_epochs=1):
         assert len(item) == 5
         for field in item:
             if isinstance(item[field], np.ndarray):
@@ -152,7 +152,7 @@ def test_case_01(add_and_remove_cv_file):  # only raw data
                         shuffle=False)
     assert d2.get_dataset_size() == 6
     num_iter = 0
-    for item in d2.create_dict_iterator():
+    for item in d2.create_dict_iterator(num_epochs=1):
         logger.info(item)
         assert len(item) == 2
         for field in item:
@@ -289,7 +289,7 @@ def test_case_02(add_and_remove_cv_file):  # muti-bytes
                         shuffle=False)
     assert d2.get_dataset_size() == 6
     num_iter = 0
-    for item in d2.create_dict_iterator():
+    for item in d2.create_dict_iterator(num_epochs=1):
         assert len(item) == 13
         for field in item:
             if isinstance(item[field], np.ndarray):
@@ -322,7 +322,7 @@ def test_case_03(add_and_remove_cv_file):
                         shuffle=False)
 
     i = 0
-    for item in d2.create_dict_iterator():  # each data is a dictionary
+    for item in d2.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([i])
         np.testing.assert_array_equal(item["data"], golden)
         i = i + 1
@@ -351,7 +351,7 @@ def type_tester(t):
 
     i = 0
     num_repeat = 0
-    for item in d2.create_dict_iterator():  # each data is a dictionary
+    for item in d2.create_dict_iterator(num_epochs=1):  # each data is a dictionary
         golden = np.array([[i], [i + 1], [i + 2], [i + 3]], dtype=t)
         logger.info(item)
         np.testing.assert_array_equal(item["data"], golden)
@@ -409,14 +409,14 @@ def test_case_07():
         os.remove("{}.db".format(CV_FILE_NAME2))
     d1 = ds.TFRecordDataset(TFRECORD_FILES, shuffle=False)
     tf_data = []
-    for x in d1.create_dict_iterator():
+    for x in d1.create_dict_iterator(num_epochs=1):
         tf_data.append(x)
     d1.save(CV_FILE_NAME2, FILES_NUM)
     d2 = ds.MindDataset(dataset_file=CV_FILE_NAME2,
                         num_parallel_workers=num_readers,
                         shuffle=False)
     mr_data = []
-    for x in d2.create_dict_iterator():
+    for x in d2.create_dict_iterator(num_epochs=1):
         mr_data.append(x)
     count = 0
     for x in tf_data:

tests/ut/python/dataset/test_sentencepiece_tokenizer.py

@@ -27,7 +27,7 @@ def test_from_vocab_to_str_UNIGRAM():
     dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
     dataset = dataset.map(operations=tokenizer)
     expect = ['▁I', '▁sa', 'w', '▁a', '▁girl', '▁with', '▁a', '▁te', 'les', 'co', 'pe', '.']
-    for i in dataset.create_dict_iterator():
+    for i in dataset.create_dict_iterator(num_epochs=1):
         ret = to_str(i["text"])
         for key, value in enumerate(ret):
             assert value == expect[key]
@@ -39,7 +39,7 @@ def test_from_vocab_to_str_BPE():
     dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
     dataset = dataset.map(operations=tokenizer)
     expect = ['▁I', '▁saw', '▁a', '▁girl', '▁with', '▁a', '▁te', 'les', 'c', 'ope', '.']
-    for i in dataset.create_dict_iterator():
+    for i in dataset.create_dict_iterator(num_epochs=1):
         ret = to_str(i["text"])
         for key, value in enumerate(ret):
             assert value == expect[key]
@@ -52,7 +52,7 @@ def test_from_vocab_to_str_CHAR():
     dataset = dataset.map(operations=tokenizer)
     expect = ['▁', 'I', '▁', 's', 'a', 'w', '▁', 'a', '▁', 'g', 'i', 'r', 'l', '▁', 'w', 'i', 't', 'h',\
               '▁', 'a', '▁', 't', 'e', 'l', 'e', 's', 'c', 'o', 'p', 'e', '.']
-    for i in dataset.create_dict_iterator():
+    for i in dataset.create_dict_iterator(num_epochs=1):
         ret = to_str(i["text"])
         for key, value in enumerate(ret):
             assert value == expect[key]
@@ -64,7 +64,7 @@ def test_from_vocab_to_str_WORD():
     dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
     dataset = dataset.map(operations=tokenizer)
     expect = ['▁I', '▁saw', '▁a', '▁girl', '▁with', '▁a', '▁telescope.']
-    for i in dataset.create_dict_iterator():
+    for i in dataset.create_dict_iterator(num_epochs=1):
         ret = to_str(i["text"])
         for key, value in enumerate(ret):
             assert value == expect[key]
@@ -76,7 +76,7 @@ def test_from_vocab_to_int():
     dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
     dataset = dataset.map(operations=tokenizer)
     expect = [6, 329, 183, 8, 945, 23, 8, 3783, 4382, 4641, 1405, 4]
-    for i in dataset.create_dict_iterator():
+    for i in dataset.create_dict_iterator(num_epochs=1):
         ret = i["text"]
         for key, value in enumerate(ret):
             assert value == expect[key]
@@ -89,7 +89,7 @@ def test_from_file_to_str():
     dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
     dataset = dataset.map(operations=tokenizer)
     expect = ['▁I', '▁sa', 'w', '▁a', '▁girl', '▁with', '▁a', '▁te', 'les', 'co', 'pe', '.']
-    for i in dataset.create_dict_iterator():
+    for i in dataset.create_dict_iterator(num_epochs=1):
         ret = to_str(i["text"])
         for key, value in enumerate(ret):
             assert value == expect[key]
@@ -102,7 +102,7 @@ def test_from_file_to_int():
     dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
     dataset = dataset.map(operations=tokenizer)
     expect = [6, 329, 183, 8, 945, 23, 8, 3783, 4382, 4641, 1405, 4]
-    for i in dataset.create_dict_iterator():
+    for i in dataset.create_dict_iterator(num_epochs=1):
         ret = i["text"]
         for key, value in enumerate(ret):
             assert value == expect[key]
@@ -115,7 +115,7 @@ def test_build_from_dataset():
     dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
     dataset = dataset.map(operations=tokenizer)
     expect = ['▁I', '▁sa', 'w', '▁a', '▁girl', '▁with', '▁a', '▁te', 'les', 'co', 'pe', '.']
-    for i in dataset.create_dict_iterator():
+    for i in dataset.create_dict_iterator(num_epochs=1):
         ret = to_str(i["text"])
         for key, value in enumerate(ret):
             assert value == expect[key]
@@ -134,7 +134,7 @@ def zip_test(dataset):
     dataset_1 = dataset_1.apply(apply_func)
     dataset_zip = ds.zip((dataset_1, dataset_2))
     expect = ['▁I', '▁sa', 'w', '▁a', '▁girl', '▁with', '▁a', '▁te', 'les', 'co', 'pe', '.']
-    for i in dataset_zip.create_dict_iterator():
+    for i in dataset_zip.create_dict_iterator(num_epochs=1):
         ret = to_str(i["text"])
         for key, value in enumerate(ret):
             assert value == expect[key]
@@ -144,7 +144,7 @@ def concat_test(dataset):
     dataset_1 = copy.deepcopy(dataset)
     dataset = dataset.concat(dataset_1)
     expect = ['▁I', '▁sa', 'w', '▁a', '▁girl', '▁with', '▁a', '▁te', 'les', 'co', 'pe', '.']
-    for i in dataset.create_dict_iterator():
+    for i in dataset.create_dict_iterator(num_epochs=1):
         ret = to_str(i["text"])
         for key, value in enumerate(ret):
             assert value == expect[key]

tests/ut/python/dataset/test_serdes_dataset.py

@@ -30,6 +30,7 @@ from mindspore.dataset.transforms.vision import Inter
 from test_minddataset_sampler import add_and_remove_cv_file, get_data, CV_DIR_NAME, CV_FILE_NAME
 from util import config_get_set_num_parallel_workers
 
+
 def test_imagefolder(remove_json_files=True):
     """
     Test simulating resnet50 dataset pipeline.
@@ -77,8 +78,10 @@ def test_imagefolder(remove_json_files=True):
     data4 = ds.deserialize(input_dict=ds1_dict)
     num_samples = 0
     # Iterate and compare the data in the original pipeline (data1) against the deserialized pipeline (data2)
-    for item1, item2, item3, item4 in zip(data1.create_dict_iterator(), data2.create_dict_iterator(),
-                                          data3.create_dict_iterator(), data4.create_dict_iterator()):
+    for item1, item2, item3, item4 in zip(data1.create_dict_iterator(num_epochs=1),
+                                          data2.create_dict_iterator(num_epochs=1),
+                                          data3.create_dict_iterator(num_epochs=1),
+                                          data4.create_dict_iterator(num_epochs=1)):
         np.testing.assert_array_equal(item1['image'], item2['image'])
         np.testing.assert_array_equal(item1['image'], item3['image'])
         np.testing.assert_array_equal(item1['label'], item2['label'])
@@ -117,8 +120,8 @@ def test_mnist_dataset(remove_json_files=True):
     data3 = ds.deserialize(json_filepath="mnist_dataset_pipeline_1.json")
 
     num = 0
-    for data1, data2, data3 in zip(data1.create_dict_iterator(), data2.create_dict_iterator(),
-                                   data3.create_dict_iterator()):
+    for data1, data2, data3 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1),
+                                   data3.create_dict_iterator(num_epochs=1)):
         np.testing.assert_array_equal(data1['image'], data2['image'])
         np.testing.assert_array_equal(data1['image'], data3['image'])
         np.testing.assert_array_equal(data1['label'], data2['label'])
@@ -197,8 +200,9 @@ def test_random_crop():
     data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
     data2 = data2.map(input_columns="image", operations=decode_op)
 
-    for item1, item1_1, item2 in zip(data1.create_dict_iterator(), data1_1.create_dict_iterator(),
-                                     data2.create_dict_iterator()):
+    for item1, item1_1, item2 in zip(data1.create_dict_iterator(num_epochs=1),
+                                     data1_1.create_dict_iterator(num_epochs=1),
+                                     data2.create_dict_iterator(num_epochs=1)):
         np.testing.assert_array_equal(item1['image'], item1_1['image'])
         _ = item2["image"]
 
@@ -251,7 +255,7 @@ def test_minddataset(add_and_remove_cv_file):
     _ = get_data(CV_DIR_NAME)
     assert data_set.get_dataset_size() == 5
     num_iter = 0
-    for _ in data_set.create_dict_iterator():
+    for _ in data_set.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 5
 

tests/ut/python/dataset/test_shuffle.py

@@ -129,7 +129,7 @@ def test_shuffle_06():
     data2 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES)
     data2 = data2.shuffle(buffer_size=buffer_size)
 
-    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
         np.testing.assert_equal(item1, item2)
 
 

tests/ut/python/dataset/test_skip.py

@@ -38,7 +38,7 @@ def test_tf_skip():
     data1 = data1.skip(2)
 
     num_iter = 0
-    for _ in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator(num_epochs=1):
         num_iter += 1
     assert num_iter == 1
 
@@ -205,7 +205,7 @@ def test_skip_exception_1():
     try:
         data1 = data1.skip(count=-1)
         num_iter = 0
-        for _ in data1.create_dict_iterator():
+        for _ in data1.create_dict_iterator(num_epochs=1):
             num_iter += 1
 
     except RuntimeError as e:

tests/ut/python/dataset/test_sliding_window.py

@@ -28,7 +28,7 @@ def test_sliding_window_string():
     dataset = dataset.map(input_columns=["text"], operations=text.SlidingWindow(2, 0))
 
     result = []
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         for i in range(data['text'].shape[0]):
             result.append([])
             for j in range(data['text'].shape[1]):
@@ -46,7 +46,7 @@ def test_sliding_window_number():
     dataset = ds.GeneratorDataset(gen(inputs), column_names=["number"])
     dataset = dataset.map(input_columns=["number"], operations=text.SlidingWindow(1, -1))
 
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         np.testing.assert_array_equal(data['number'], expect)
 
 def test_sliding_window_big_width():
@@ -56,7 +56,7 @@ def test_sliding_window_big_width():
     dataset = ds.NumpySlicesDataset(inputs, column_names=["number"], shuffle=False)
     dataset = dataset.map(input_columns=["number"], operations=text.SlidingWindow(30, 0))
 
-    for data in dataset.create_dict_iterator():
+    for data in dataset.create_dict_iterator(num_epochs=1):
         np.testing.assert_array_equal(data['number'], expect)
 
 def test_sliding_window_exception():
@@ -82,7 +82,7 @@ def test_sliding_window_exception():
         inputs = [[1, 2, 3, 4, 5]]
         dataset = ds.NumpySlicesDataset(inputs, column_names=["text"], shuffle=False)
         dataset = dataset.map(input_columns=["text"], operations=text.SlidingWindow(3, -100))
-        for _ in dataset.create_dict_iterator():
+        for _ in dataset.create_dict_iterator(num_epochs=1):
             pass
         assert False
     except RuntimeError as e:
@@ -92,7 +92,7 @@ def test_sliding_window_exception():
         inputs = ["aa", "bb", "cc"]
         dataset = ds.NumpySlicesDataset(inputs, column_names=["text"], shuffle=False)
         dataset = dataset.map(input_columns=["text"], operations=text.SlidingWindow(2, 0))
-        for _ in dataset.create_dict_iterator():
+        for _ in dataset.create_dict_iterator(num_epochs=1):
             pass
         assert False
     except RuntimeError as e: