!1457 fix 3 bug reports for split

Merge pull request !1457 from Peilin/splitOp-after-testing

example/lstm_aclImdb/train.py

@@ -71,7 +71,7 @@ if __name__ == '__main__':
     model = Model(network, loss, opt, {'acc': Accuracy()})
 
     print("============== Starting Training ==============")
-    ds_train = create_dataset(args.preprocess_path, cfg.batch_size, repeat_num=cfg.num_epochs)
+    ds_train = create_dataset(args.preprocess_path, cfg.batch_size, cfg.num_epochs)
     config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
                                  keep_checkpoint_max=cfg.keep_checkpoint_max)
     ckpoint_cb = ModelCheckpoint(prefix="lstm", directory=args.ckpt_path, config=config_ck)

mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.cc

@@ -70,21 +70,26 @@ Status RandomSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer) {
 }
 
 Status RandomSampler::InitSampler() {
-  num_samples_ = (user_num_samples_ < num_samples_) ? user_num_samples_ : num_samples_;
-  CHECK_FAIL_RETURN_UNEXPECTED(num_samples_ > 0 && num_rows_ > 0, "both num_samples & num_rows need to be positive");
-  samples_per_buffer_ = samples_per_buffer_ > num_samples_ ? num_samples_ : samples_per_buffer_;
+  CHECK_FAIL_RETURN_UNEXPECTED(num_rows_ > 0, "num_rows needs to be positive.");
 
   rnd_.seed(seed_);
 
   if (replacement_ == false) {
+    num_samples_ = std::min(num_samples_, num_rows_);
+
     shuffled_ids_.reserve(num_rows_);
     for (int64_t i = 0; i < num_rows_; i++) {
       shuffled_ids_.push_back(i);
     }
     std::shuffle(shuffled_ids_.begin(), shuffled_ids_.end(), rnd_);
   } else {
+    num_samples_ = std::min(num_samples_, user_num_samples_);
     dist = std::make_unique<std::uniform_int_distribution<int64_t>>(0, num_rows_ - 1);
   }
+
+  CHECK_FAIL_RETURN_UNEXPECTED(num_samples_ > 0, "num_samples needs to be positive.");
+  samples_per_buffer_ = samples_per_buffer_ > num_samples_ ? num_samples_ : samples_per_buffer_;
+
   return Status::OK();
 }
 

mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc

@@ -32,9 +32,7 @@ Status Sampler::HandshakeRandomAccessOp(const RandomAccessOp *op) {
     }
 
     // Handshake and init child first.
-    if (HasChildSampler()) {
-      RETURN_IF_NOT_OK(child_sampler->HandshakeRandomAccessOp(op));
-    }
+    RETURN_IF_NOT_OK(child_sampler->HandshakeRandomAccessOp(op));
   }
 
   CHECK_FAIL_RETURN_UNEXPECTED(op != nullptr, "RandomAccessOp is nullptr\n");

mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_sampler.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2020 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -28,9 +28,9 @@ SubsetSampler::SubsetSampler(int64_t start_index, int64_t subset_size)
     : Sampler(subset_size), start_index_(start_index), subset_size_(subset_size), current_id_(0) {}
 
 Status SubsetSampler::InitSampler() {
-  CHECK_FAIL_RETURN_UNEXPECTED(subset_size_ > 0, "subset_size_ <= 0\n");
+  CHECK_FAIL_RETURN_UNEXPECTED(subset_size_ > 0, "subset_size <= 0\n");
   CHECK_FAIL_RETURN_UNEXPECTED(start_index_ >= 0, "start_index < 0\n");
-  CHECK_FAIL_RETURN_UNEXPECTED(start_index_ < num_rows_, "start_index >= num_rows_\n");
+  CHECK_FAIL_RETURN_UNEXPECTED(start_index_ < num_rows_, "start_index >= num_rows\n");
   CHECK_FAIL_RETURN_UNEXPECTED(start_index_ + subset_size_ - 1 < num_rows_, "Final index out of bounds.\n");
 
   num_samples_ = subset_size_;

mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_sampler.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2020 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.

mindspore/dataset/__init__.py

@@ -23,7 +23,7 @@ from .engine.datasets import TFRecordDataset, ImageFolderDatasetV2, MnistDataset
     GeneratorDataset, ManifestDataset, Cifar10Dataset, Cifar100Dataset, VOCDataset, CelebADataset, TextFileDataset, \
     Schema, Shuffle, zip, RandomDataset
 from .engine.samplers import DistributedSampler, PKSampler, RandomSampler, SequentialSampler, SubsetRandomSampler, \
-    WeightedRandomSampler, Sampler
+    WeightedRandomSampler, SubsetSampler, Sampler
 from .engine.serializer_deserializer import serialize, deserialize, show
 from .engine.graphdata import GraphData
 

mindspore/dataset/engine/datasets.py

@@ -633,9 +633,9 @@ class Dataset:
                 Datasets of size f1*K, f2*K, …, fn*K (rounded to nearest integer) where K is the size
                 of the original dataset. If after rounding, any size equals 0, an error will occur.
                 All floats must be between 0 and 1 and must sum to 1, otherwise an error will occur.
-            randomize (bool): determines whether or not to split the data randomly. If true, the data
-                will be randomly split. Otherwise, each split will be created with consecutive rows
-                from the dataset.
+            randomize (bool, optional): determines whether or not to split the data randomly (default=True).
+                If true, the data will be randomly split. Otherwise, each split will be created with
+                consecutive rows from the dataset.
 
         Note:
             1. Dataset cannot be sharded if split is going to be called.
@@ -678,7 +678,8 @@ class Dataset:
             ds = copy.deepcopy(self)
             if randomize:
                 # want to shuffle the same way every epoch before split
-                ds = ds.shuffle()
+                # in alter_tree, shuffle buffer is minimum 10000, so use 10000 here
+                ds = ds.shuffle(10000)
                 ds.reshuffle_each_epoch = False
 
             if rows_to_skip > 0:
@@ -1209,6 +1210,9 @@ class MappableDataset(SourceDataset):
             >>> new_sampler = ds.DistributedSampler(10, 2)
             >>> data.use_sampler(new_sampler)
         """
+        if new_sampler is not None and not isinstance(new_sampler, (samplers.BuiltinSampler, samplers.Sampler)):
+            raise TypeError("new_sampler is not an instance of a sampler.")
+
         self.sampler = self.sampler.child_sampler
         self.add_sampler(new_sampler)
 
@@ -1218,6 +1222,11 @@ class MappableDataset(SourceDataset):
     def is_sharded(self):
         raise NotImplementedError("MappableDataset must implement is_sharded.")
 
+    def _get_sampler_dataset_size(self):
+        if self.sampler is not None:
+            return self.sampler.get_dataset_size()
+
+        return None
 
     @check_split
     def split(self, sizes, randomize=True):
@@ -1236,9 +1245,9 @@ class MappableDataset(SourceDataset):
                 Datasets of size f1*K, f2*K, …, fn*K (rounded to nearest integer) where K is the size
                 of the original dataset. If after rounding, any size equals 0, an error will occur.
                 All floats must be between 0 and 1 and must sum to 1, otherwise an error will occur.
-            randomize (bool): determines whether or not to split the data randomly. If true, the data
-                will be randomly split. Otherwise, each split will be created with consecutive rows
-                from the dataset.
+            randomize (bool, optional): determines whether or not to split the data randomly (default=True).
+                If true, the data will be randomly split. Otherwise, each split will be created with
+                consecutive rows from the dataset.
 
         Note:
             1. Dataset should not be sharded if split is going to be called. Instead, create a
@@ -2105,7 +2114,6 @@ class TransferDataset(DatasetOp):
         self.iterator = TupleIterator(self)
 
 
-
 class RangeDataset(MappableDataset):
     """
     A source dataset that reads and parses datasets stored on disk in a range.
@@ -2296,8 +2304,13 @@ class ImageFolderDatasetV2(MappableDataset):
         else:
             num_samples = self.num_samples
         num_rows = ImageFolderOp.get_num_rows_and_classes(self.dataset_dir, num_samples)[0]
+        rows_per_shard = get_num_rows(num_rows, self.num_shards)
+        rows_from_sampler = self._get_sampler_dataset_size()
 
-        return get_num_rows(num_rows, self.num_shards)
+        if rows_from_sampler is None:
+            return rows_per_shard
+
+        return min(rows_from_sampler, rows_per_shard)
 
     def num_classes(self):
         """
@@ -2425,8 +2438,13 @@ class MnistDataset(MappableDataset):
             num_samples = self.num_samples
 
         num_rows = MnistOp.get_num_rows(self.dataset_dir, num_samples)
+        rows_per_shard = get_num_rows(num_rows, self.num_shards)
+        rows_from_sampler = self._get_sampler_dataset_size()
+
+        if rows_from_sampler is None:
+            return rows_per_shard
 
-        return get_num_rows(num_rows, self.num_shards)
+        return min(rows_from_sampler, rows_per_shard)
 
     def is_shuffled(self):
         if self.shuffle_level is None:
@@ -2926,7 +2944,12 @@ class GeneratorDataset(MappableDataset):
         Return:
             Number, number of batches.
         """
-        return self._dataset_size
+        rows_from_sampler = self._get_sampler_dataset_size()
+
+        if rows_from_sampler is None:
+            return self._dataset_size
+
+        return min(rows_from_sampler, self._dataset_size)
 
     # manually set dataset_size as a temporary solution.
     def set_dataset_size(self, value):
@@ -3220,8 +3243,13 @@ class ManifestDataset(MappableDataset):
             class_indexing = self.class_indexing
 
         num_rows = ManifestOp.get_num_rows_and_classes(self.dataset_file, num_samples, class_indexing, self.usage)[0]
+        rows_per_shard = get_num_rows(num_rows, self.num_shards)
+        rows_from_sampler = self._get_sampler_dataset_size()
+
+        if rows_from_sampler is None:
+            return rows_per_shard
 
-        return get_num_rows(num_rows, self.num_shards)
+        return min(rows_from_sampler, rows_per_shard)
 
     def num_classes(self):
         """
@@ -3379,8 +3407,13 @@ class Cifar10Dataset(MappableDataset):
             num_samples = self.num_samples
 
         num_rows = CifarOp.get_num_rows(self.dataset_dir, num_samples, True)
+        rows_per_shard = get_num_rows(num_rows, self.num_shards)
+        rows_from_sampler = self._get_sampler_dataset_size()
 
-        return get_num_rows(num_rows, self.num_shards)
+        if rows_from_sampler is None:
+            return rows_per_shard
+
+        return min(rows_from_sampler, rows_per_shard)
 
     def is_shuffled(self):
         if self.shuffle_level is None:
@@ -3498,8 +3531,13 @@ class Cifar100Dataset(MappableDataset):
             num_samples = self.num_samples
 
         num_rows = CifarOp.get_num_rows(self.dataset_dir, num_samples, False)
+        rows_per_shard = get_num_rows(num_rows, self.num_shards)
+        rows_from_sampler = self._get_sampler_dataset_size()
+
+        if rows_from_sampler is None:
+            return rows_per_shard
 
-        return get_num_rows(num_rows, self.num_shards)
+        return min(rows_from_sampler, rows_per_shard)
 
     def is_shuffled(self):
         if self.shuffle_level is None:
@@ -3562,7 +3600,12 @@ class RandomDataset(SourceDataset):
         Return:
             Number, number of batches.
         """
-        return num_samples
+        rows_from_sampler = self._get_sampler_dataset_size()
+
+        if rows_from_sampler is None:
+            return self.num_samples
+
+        return min(rows_from_sampler, self.num_samples)
 
     def is_shuffled(self):
         return True
@@ -3871,7 +3914,12 @@ class VOCDataset(MappableDataset):
         Return:
             Number, number of batches.
         """
-        return self.num_samples
+        rows_from_sampler = self._get_sampler_dataset_size()
+
+        if rows_from_sampler is None:
+            return self.num_samples
+
+        return min(rows_from_sampler, self.num_samples)
 
     def get_class_indexing(self):
         """

mindspore/dataset/engine/samplers.py

@@ -114,6 +114,9 @@ class Sampler:
 
         return self.child_sampler.is_sharded()
 
+    def get_dataset_size(self):
+        return self._get_indices().size
+
 
 class BuiltinSampler:
     """
@@ -146,6 +149,12 @@ class BuiltinSampler:
     def is_sharded(self):
         raise NotImplementedError("Sampler must implement is_sharded.")
 
+    def get_dataset_size(self):
+        if self.child_sampler is not None:
+            return self.child_sampler.get_dataset_size()
+
+        return None
+
 
 class DistributedSampler(BuiltinSampler):
     """
@@ -330,6 +339,9 @@ class RandomSampler(BuiltinSampler):
 
         return self.child_sampler.is_sharded()
 
+    def get_dataset_size(self):
+        return self.num_samples
+
 
 class SequentialSampler(BuiltinSampler):
     """
@@ -421,6 +433,9 @@ class SubsetSampler(BuiltinSampler):
 
         return self.child_sampler.is_sharded()
 
+    def get_dataset_size(self):
+        return self.subset_size
+
 
 class SubsetRandomSampler(BuiltinSampler):
     """
@@ -467,6 +482,10 @@ class SubsetRandomSampler(BuiltinSampler):
         return cde.MindrecordSubsetRandomSampler(self.indices)
 
 
+    def get_dataset_size(self):
+        return len(indices)
+
+
 class WeightedRandomSampler(BuiltinSampler):
     """
     Samples the elements from [0, len(weights) - 1] randomly with the given weights (probabilities).
@@ -522,3 +541,6 @@ class WeightedRandomSampler(BuiltinSampler):
             return False
 
         return self.child_sampler.is_sharded()
+
+    def get_dataset_size(self):
+        return self.num_samples

tests/ut/python/dataset/test_sampler.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 import numpy as np
+import pytest
 
 import mindspore.dataset as ds
 from mindspore import log as logger
@@ -164,6 +165,35 @@ def test_python_sampler():
     assert list(sp1.get_indices()) == [0, 1, 2, 3, 4]
 
 
+def test_subset_sampler():
+    manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
+    map = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
+
+    def test_config(num_samples, start_index, subset_size):
+        sampler = ds.SubsetSampler(start_index, subset_size)
+        d = ds.ManifestDataset(manifest_file, sampler=sampler)
+
+        res = []
+        for item in d.create_dict_iterator():
+            res.append(map[(item["image"].shape[0], item["label"].item())])
+
+        return res
+
+    with pytest.raises(RuntimeError) as info:
+        test_config(5, 0, 0)
+    assert "subset_size <= 0" in str(info.value)
+
+    assert test_config(5, 0, 1) == [0]
+    assert test_config(5, 0, 2) == [0, 1]
+    assert test_config(5, 0, 3) == [0, 1, 2]
+    assert test_config(5, 0, 4) == [0, 1, 2, 3]
+    assert test_config(5, 0, 5) == [0, 1, 2, 3, 4]
+    assert test_config(5, 1, 1) == [1]
+    assert test_config(5, 2, 3) == [2, 3, 4]
+    assert test_config(5, 3, 2) == [3, 4]
+    assert test_config(5, 4, 1) == [4]
+
+
 def test_sampler_chain():
     manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
     map = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
@@ -190,10 +220,26 @@ def test_sampler_chain():
     assert test_config(5, 3) == [3]
     assert test_config(5, 4) == [4]
 
+def test_add_sampler_invalid_input():
+    manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
+    map = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
+    data1 = ds.ManifestDataset(manifest_file)
+
+    with pytest.raises(TypeError) as info:
+        data1.use_sampler(1)
+    assert "not an instance of a sampler" in str(info.value)
+
+    with pytest.raises(TypeError) as info:
+        data1.use_sampler("sampler")
+    assert "not an instance of a sampler" in str(info.value)
+
+
 if __name__ == '__main__':
     test_sequential_sampler(True)
     test_random_sampler(True)
     test_random_sampler_multi_iter(True)
     test_sampler_py_api()
     test_python_sampler()
+    test_subset_sampler()
     test_sampler_chain()
+    test_add_sampler_invalid_input()

tests/ut/python/dataset/test_split.py

@@ -23,7 +23,11 @@ from util import config_get_set_num_parallel_workers
 manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
 manifest_map = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
 
-def split_with_invalid_inputs(d): 
+text_file_dataset_path = "../data/dataset/testTextFileDataset/*"
+text_file_data = ["This is a text file.", "Another file.", "Be happy every day.",
+                  "End of file.", "Good luck to everyone."]
+
+def split_with_invalid_inputs(d):
     with pytest.raises(ValueError) as info:
         s1, s2 = d.split([])
     assert "sizes cannot be empty" in str(info.value)
@@ -68,8 +72,8 @@ def split_with_invalid_inputs(d):
         s1, s2 = d.split([0.05, 0.95])
     assert "percentage 0.05 is too small" in str(info.value)
 
+
 def test_unmappable_invalid_input():
-    text_file_dataset_path = "../data/dataset/testTextFileDataset/*"
     d = ds.TextFileDataset(text_file_dataset_path)
     split_with_invalid_inputs(d)
 
@@ -78,11 +82,10 @@ def test_unmappable_invalid_input():
         s1, s2 = d.split([4, 1])
     assert "dataset should not be sharded before split" in str(info.value)
 
+
 def test_unmappable_split():
-    text_file_dataset_path = "../data/dataset/testTextFileDataset/*"
-    text_file_data = ["This is a text file.", "Another file.", "Be happy every day.",
-            "End of file.", "Good luck to everyone."]
     original_num_parallel_workers = config_get_set_num_parallel_workers(4)
+
     d = ds.TextFileDataset(text_file_dataset_path, shuffle=False)
     s1, s2 = d.split([4, 1], randomize=False)
 
@@ -124,6 +127,142 @@ def test_unmappable_split():
 
     assert s1_output == text_file_data[0:2]
     assert s2_output == text_file_data[2:]
+
+    # Restore configuration num_parallel_workers
+    ds.config.set_num_parallel_workers(original_num_parallel_workers)
+
+
+def test_unmappable_randomize_deterministic():
+    original_num_parallel_workers = config_get_set_num_parallel_workers(4)
+
+    # the labels outputted by ShuffleOp for seed 53 is [0, 2, 1, 4, 3]
+    ds.config.set_seed(53)
+
+    d = ds.TextFileDataset(text_file_dataset_path, shuffle=False)
+    s1, s2 = d.split([0.8, 0.2])
+
+    for _ in range(10):
+        s1_output = []
+        for item in s1.create_dict_iterator():
+            s1_output.append(item["text"].item().decode("utf8"))
+
+        s2_output = []
+        for item in s2.create_dict_iterator():
+            s2_output.append(item["text"].item().decode("utf8"))
+
+        # note no overlap
+        assert s1_output == [text_file_data[0], text_file_data[2], text_file_data[1], text_file_data[4]]
+        assert s2_output == [text_file_data[3]]
+
+    # Restore configuration num_parallel_workers
+    ds.config.set_num_parallel_workers(original_num_parallel_workers)
+
+
+def test_unmappable_randomize_repeatable():
+    original_num_parallel_workers = config_get_set_num_parallel_workers(4)
+
+    # the labels outputted by ShuffleOp for seed 53 is [0, 2, 1, 4, 3]
+    ds.config.set_seed(53)
+
+    d = ds.TextFileDataset(text_file_dataset_path, shuffle=False)
+    s1, s2 = d.split([0.8, 0.2])
+
+    num_epochs = 5
+    s1 = s1.repeat(num_epochs)
+    s2 = s2.repeat(num_epochs)
+
+    s1_output = []
+    for item in s1.create_dict_iterator():
+        s1_output.append(item["text"].item().decode("utf8"))
+
+    s2_output = []
+    for item in s2.create_dict_iterator():
+        s2_output.append(item["text"].item().decode("utf8"))
+
+    # note no overlap
+    assert s1_output == [text_file_data[0], text_file_data[2], text_file_data[1], text_file_data[4]] * num_epochs
+    assert s2_output == [text_file_data[3]] * num_epochs
+
+    # Restore configuration num_parallel_workers
+    ds.config.set_num_parallel_workers(original_num_parallel_workers)
+
+
+def test_unmappable_get_dataset_size():
+    d = ds.TextFileDataset(text_file_dataset_path, shuffle=False)
+    s1, s2 = d.split([0.8, 0.2])
+
+    assert d.get_dataset_size() == 5
+    assert s1.get_dataset_size() == 4
+    assert s2.get_dataset_size() == 1
+
+
+def test_unmappable_multi_split():
+    original_num_parallel_workers = config_get_set_num_parallel_workers(4)
+
+    # the labels outputted by ShuffleOp for seed 53 is [0, 2, 1, 4, 3]
+    ds.config.set_seed(53)
+
+    d = ds.TextFileDataset(text_file_dataset_path, shuffle=False)
+    s1, s2 = d.split([4, 1])
+
+    s1_correct_output = [text_file_data[0], text_file_data[2], text_file_data[1], text_file_data[4]]
+
+    s1_output = []
+    for item in s1.create_dict_iterator():
+        s1_output.append(item["text"].item().decode("utf8"))
+    assert s1_output == s1_correct_output
+
+    # no randomize in second split
+    s1s1, s1s2, s1s3 = s1.split([1, 2, 1], randomize=False)
+
+    s1s1_output = []
+    for item in s1s1.create_dict_iterator():
+        s1s1_output.append(item["text"].item().decode("utf8"))
+
+    s1s2_output = []
+    for item in s1s2.create_dict_iterator():
+        s1s2_output.append(item["text"].item().decode("utf8"))
+
+    s1s3_output = []
+    for item in s1s3.create_dict_iterator():
+        s1s3_output.append(item["text"].item().decode("utf8"))
+
+    assert s1s1_output == [s1_correct_output[0]]
+    assert s1s2_output == [s1_correct_output[1], s1_correct_output[2]]
+    assert s1s3_output == [s1_correct_output[3]]
+
+    s2_output = []
+    for item in s2.create_dict_iterator():
+        s2_output.append(item["text"].item().decode("utf8"))
+    assert s2_output == [text_file_data[3]]
+
+    # randomize in second split
+    # the labels outputted by the ShuffleOp for seed 53 is [2, 3, 1, 0]
+    shuffled_ids = [2, 3, 1, 0]
+
+    s1s1, s1s2, s1s3 = s1.split([1, 2, 1])
+
+    s1s1_output = []
+    for item in s1s1.create_dict_iterator():
+        s1s1_output.append(item["text"].item().decode("utf8"))
+
+    s1s2_output = []
+    for item in s1s2.create_dict_iterator():
+        s1s2_output.append(item["text"].item().decode("utf8"))
+
+    s1s3_output = []
+    for item in s1s3.create_dict_iterator():
+        s1s3_output.append(item["text"].item().decode("utf8"))
+
+    assert s1s1_output == [s1_correct_output[shuffled_ids[0]]]
+    assert s1s2_output == [s1_correct_output[shuffled_ids[1]], s1_correct_output[shuffled_ids[2]]]
+    assert s1s3_output == [s1_correct_output[shuffled_ids[3]]]
+
+    s2_output = []
+    for item in s2.create_dict_iterator():
+        s2_output.append(item["text"].item().decode("utf8"))
+    assert s2_output == [text_file_data[3]]
+
     # Restore configuration num_parallel_workers
     ds.config.set_num_parallel_workers(original_num_parallel_workers)
 
@@ -137,6 +276,7 @@ def test_mappable_invalid_input():
         s1, s2 = d.split([4, 1])
     assert "dataset should not be sharded before split" in str(info.value)
 
+
 def test_mappable_split_general():
     d = ds.ManifestDataset(manifest_file, shuffle=False)
     d = d.take(5)
@@ -183,6 +323,7 @@ def test_mappable_split_general():
     assert s1_output == [0, 1]
     assert s2_output == [2, 3, 4]
 
+
 def test_mappable_split_optimized():
     d = ds.ManifestDataset(manifest_file, shuffle=False)
 
@@ -228,9 +369,9 @@ def test_mappable_split_optimized():
     assert s1_output == [0, 1]
     assert s2_output == [2, 3, 4]
 
+
 def test_mappable_randomize_deterministic():
-    # set arbitrary seed for shard after split
-    # the labels outputted by ManifestDataset for seed 53 is [0, 1, 3, 4]
+    # the labels outputted by ManifestDataset for seed 53 is [0, 1, 3, 4, 2]
     ds.config.set_seed(53)
 
     d = ds.ManifestDataset(manifest_file, shuffle=False)
@@ -249,9 +390,9 @@ def test_mappable_randomize_deterministic():
         assert s1_output == [0, 1, 3, 4]
         assert s2_output == [2]
 
+
 def test_mappable_randomize_repeatable():
-    # set arbitrary seed for shard after split
-    # the labels outputted by ManifestDataset for seed 53 is [0, 1, 3, 4]
+    # the labels outputted by ManifestDataset for seed 53 is [0, 1, 3, 4, 2]
     ds.config.set_seed(53)
 
     d = ds.ManifestDataset(manifest_file, shuffle=False)
@@ -273,9 +414,10 @@ def test_mappable_randomize_repeatable():
     assert s1_output == [0, 1, 3, 4] * num_epochs
     assert s2_output == [2] * num_epochs
 
+
 def test_mappable_sharding():
     # set arbitrary seed for repeatability for shard after split
-    # the labels outputted by ManifestDataset for seed 53 is [0, 1, 3, 4]
+    # the labels outputted by ManifestDataset for seed 53 is [0, 1, 3, 4, 2]
     ds.config.set_seed(53)
 
     num_epochs = 5
@@ -336,12 +478,94 @@ def test_mappable_sharding():
     assert s2_output == [2]
     assert d2s2_output == [2]
 
+
+def test_mappable_get_dataset_size():
+    d = ds.ManifestDataset(manifest_file, shuffle=False)
+    s1, s2 = d.split([4, 1])
+
+    assert d.get_dataset_size() == 5
+    assert s1.get_dataset_size() == 4
+    assert s2.get_dataset_size() == 1
+
+
+def test_mappable_multi_split():
+    # the labels outputted by ManifestDataset for seed 53 is [0, 1, 3, 4, 2]
+    ds.config.set_seed(53)
+
+    d = ds.ManifestDataset(manifest_file, shuffle=False)
+    s1, s2 = d.split([4, 1])
+
+    s1_correct_output = [0, 1, 3, 4]
+
+    s1_output = []
+    for item in s1.create_dict_iterator():
+        s1_output.append(manifest_map[(item["image"].shape[0], item["label"].item())])
+    assert s1_output == s1_correct_output
+
+    # no randomize in second split
+    s1s1, s1s2, s1s3 = s1.split([1, 2, 1], randomize=False)
+
+    s1s1_output = []
+    for item in s1s1.create_dict_iterator():
+        s1s1_output.append(manifest_map[(item["image"].shape[0], item["label"].item())])
+
+    s1s2_output = []
+    for item in s1s2.create_dict_iterator():
+        s1s2_output.append(manifest_map[(item["image"].shape[0], item["label"].item())])
+
+    s1s3_output = []
+    for item in s1s3.create_dict_iterator():
+        s1s3_output.append(manifest_map[(item["image"].shape[0], item["label"].item())])
+
+    assert s1s1_output == [s1_correct_output[0]]
+    assert s1s2_output == [s1_correct_output[1], s1_correct_output[2]]
+    assert s1s3_output == [s1_correct_output[3]]
+
+    s2_output = []
+    for item in s2.create_dict_iterator():
+        s2_output.append(manifest_map[(item["image"].shape[0], item["label"].item())])
+    assert s2_output == [2]
+
+    # randomize in second split
+    # the labels outputted by the RandomSampler for seed 53 is [3, 1, 2, 0]
+    random_sampler_ids = [3, 1, 2, 0]
+
+    s1s1, s1s2, s1s3 = s1.split([1, 2, 1])
+
+    s1s1_output = []
+    for item in s1s1.create_dict_iterator():
+        s1s1_output.append(manifest_map[(item["image"].shape[0], item["label"].item())])
+
+    s1s2_output = []
+    for item in s1s2.create_dict_iterator():
+        s1s2_output.append(manifest_map[(item["image"].shape[0], item["label"].item())])
+
+    s1s3_output = []
+    for item in s1s3.create_dict_iterator():
+        s1s3_output.append(manifest_map[(item["image"].shape[0], item["label"].item())])
+
+    assert s1s1_output == [s1_correct_output[random_sampler_ids[0]]]
+    assert s1s2_output == [s1_correct_output[random_sampler_ids[1]], s1_correct_output[random_sampler_ids[2]]]
+    assert s1s3_output == [s1_correct_output[random_sampler_ids[3]]]
+
+    s2_output = []
+    for item in s2.create_dict_iterator():
+        s2_output.append(manifest_map[(item["image"].shape[0], item["label"].item())])
+    assert s2_output == [2]
+
+
 if __name__ == '__main__':
     test_unmappable_invalid_input()
     test_unmappable_split()
+    test_unmappable_randomize_deterministic()
+    test_unmappable_randomize_repeatable()
+    test_unmappable_get_dataset_size()
+    test_unmappable_multi_split()
     test_mappable_invalid_input()
     test_mappable_split_general()
     test_mappable_split_optimized()
     test_mappable_randomize_deterministic()
     test_mappable_randomize_repeatable()
     test_mappable_sharding()
+    test_mappable_get_dataset_size()
+    test_mappable_multi_split()