fix num_sample in sequentialSampler and randomSampler

mindspore/ccsrc/minddata/mindrecord/meta/shard_sequential_sample.cc

@@ -35,33 +35,33 @@ int64_t ShardSequentialSample::GetNumSamples(int64_t dataset_size, int64_t num_c
   if (per_ > kEpsilon && per_ <= 1.0f) {
     return dataset_size * kEpsilon;
   }
-  return no_of_samples_;
+  return std::min(static_cast<int64_t>(no_of_samples_), dataset_size);
 }
 
 MSRStatus ShardSequentialSample::Execute(ShardTask &tasks) {
-  int total_no = static_cast<int>(tasks.Size());
-  int taking;
+  int64_t total_no = static_cast<int64_t>(tasks.Size());
+  int64_t taking;
   if (no_of_samples_ == 0 && (per_ >= -kEpsilon && per_ <= kEpsilon)) {
     taking = total_no;
   } else if (per_ > kEpsilon && per_ <= 1.0f) {
     taking = total_no * kEpsilon;
   } else {
-    taking = no_of_samples_;
+    taking = std::min(static_cast<int64_t>(no_of_samples_), total_no);
   }
 
   if (tasks.permutation_.empty()) {
     ShardTask new_tasks;
-    total_no = static_cast<int>(tasks.Size());
-    for (int i = offset_; i < taking + offset_; ++i) {
+    total_no = static_cast<int64_t>(tasks.Size());
+    for (size_t i = offset_; i < taking + offset_; ++i) {
       new_tasks.InsertTask(tasks.GetTaskByID(i % total_no));
     }
     std::swap(tasks, new_tasks);
   } else {  // shuffled
     ShardTask new_tasks;
-    if (taking > static_cast<int>(tasks.permutation_.size())) {
+    if (taking > static_cast<int64_t>(tasks.permutation_.size())) {
       return FAILED;
     }
-    total_no = static_cast<int>(tasks.permutation_.size());
+    total_no = static_cast<int64_t>(tasks.permutation_.size());
     for (size_t i = offset_; i < taking + offset_; ++i) {
       new_tasks.InsertTask(tasks.GetTaskByID(tasks.permutation_[i % total_no]));
     }

mindspore/ccsrc/minddata/mindrecord/meta/shard_shuffle.cc

@@ -39,7 +39,7 @@ int64_t ShardShuffle::GetNumSamples(int64_t dataset_size, int64_t num_classes) {
   if (replacement_) {
     return no_of_samples_ == 0 ? dataset_size : no_of_samples_;
   }
-  return dataset_size;
+  return no_of_samples_ == 0 ? dataset_size : std::min(dataset_size, no_of_samples_);
 }
 
 MSRStatus ShardShuffle::Execute(ShardTask &tasks) {
@@ -67,6 +67,14 @@ MSRStatus ShardShuffle::Execute(ShardTask &tasks) {
       std::swap(tasks, new_tasks);
     } else {
       std::shuffle(tasks.permutation_.begin(), tasks.permutation_.end(), std::default_random_engine(shuffle_seed_));
+      auto total_no = static_cast<int64_t>(tasks.Size());
+      if (no_of_samples_ > 0 && no_of_samples_ < total_no) {
+        ShardTask new_tasks;
+        for (size_t i = 0; i < no_of_samples_; ++i) {
+          new_tasks.InsertTask(tasks.GetTaskByID(i));
+        }
+        std::swap(tasks, new_tasks);
+      }
     }
   } else {  // shuffle unit like: (a1, b1, c1),(a2, b2, c2),..., (an, bn, cn)
     uint32_t individual_size = tasks.Size() / tasks.categories;

mindspore/dataset/transforms/c_transforms.py

@@ -311,7 +311,7 @@ class Unique(cde.UniqueOp):
         Call batch op before calling this function.
 
     Examples:
-    >>> import mindspore.dataset.transforms.c_transforms as c_transforms
+        >>> import mindspore.dataset.transforms.c_transforms as c_transforms
         >>>
         >>> # Data before
         >>> # |  x                 |

tests/ut/cpp/dataset/c_api_dataset_mindrecord.cc

@@ -208,7 +208,7 @@ TEST_F(MindDataTestPipeline, TestMindDataSuccess6) {
   std::string file_path1 = datasets_root_path_ + "/../mindrecord/testMindDataSet/testImageNetData/imagenet.mindrecord0";
   std::vector<std::string> file_list = {file_path1};
 
-  // Check sequential sampler, output number is 10, with duplicate samples(a little weird, wait to fix)
+  // Check sequential sampler, output number is 5
   std::shared_ptr<Dataset> ds1 = MindData(file_list, {}, SequentialSampler(0, 10));
   EXPECT_NE(ds1, nullptr);
 
@@ -229,7 +229,7 @@ TEST_F(MindDataTestPipeline, TestMindDataSuccess6) {
   EXPECT_NE(ds5, nullptr);
 
   std::vector<std::shared_ptr<Dataset>> ds = {ds1, ds2, ds3, ds4, ds5};
-  std::vector<int32_t> expected_samples = {10, 5, 2, 3, 3};
+  std::vector<int32_t> expected_samples = {5, 5, 2, 3, 3};
 
   for (int32_t i = 0; i < ds.size(); i++) {
     // Create an iterator over the result of the above dataset

tests/ut/python/dataset/test_minddataset_sampler.py

@@ -412,6 +412,46 @@ def test_cv_minddataset_random_sampler_replacement(add_and_remove_cv_file):
         num_iter += 1
     assert num_iter == 5
 
+def test_cv_minddataset_random_sampler_replacement_false_1(add_and_remove_cv_file):
+    columns_list = ["data", "file_name", "label"]
+    num_readers = 4
+    sampler = ds.RandomSampler(replacement=False, num_samples=2)
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
+                              sampler=sampler)
+    assert data_set.get_dataset_size() == 2
+    num_iter = 0
+    for item in data_set.create_dict_iterator(num_epochs=1, output_numpy=True):
+        logger.info(
+            "-------------- cv reader basic: {} ------------------------".format(num_iter))
+        logger.info(
+            "-------------- item[data]: {}  -----------------------------".format(item["data"]))
+        logger.info(
+            "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+        logger.info(
+            "-------------- item[label]: {} ----------------------------".format(item["label"]))
+        num_iter += 1
+    assert num_iter == 2
+
+def test_cv_minddataset_random_sampler_replacement_false_2(add_and_remove_cv_file):
+    columns_list = ["data", "file_name", "label"]
+    num_readers = 4
+    sampler = ds.RandomSampler(replacement=False, num_samples=20)
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
+                              sampler=sampler)
+    assert data_set.get_dataset_size() == 10
+    num_iter = 0
+    for item in data_set.create_dict_iterator(num_epochs=1, output_numpy=True):
+        logger.info(
+            "-------------- cv reader basic: {} ------------------------".format(num_iter))
+        logger.info(
+            "-------------- item[data]: {}  -----------------------------".format(item["data"]))
+        logger.info(
+            "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+        logger.info(
+            "-------------- item[label]: {} ----------------------------".format(item["label"]))
+        num_iter += 1
+    assert num_iter == 10
+
 
 def test_cv_minddataset_sequential_sampler_basic(add_and_remove_cv_file):
     data = get_data(CV_DIR_NAME, True)
@@ -437,7 +477,7 @@ def test_cv_minddataset_sequential_sampler_basic(add_and_remove_cv_file):
     assert num_iter == 4
 
 
-def test_cv_minddataset_sequential_sampler_exceed_size(add_and_remove_cv_file):
+def test_cv_minddataset_sequential_sampler_offeset(add_and_remove_cv_file):
     data = get_data(CV_DIR_NAME, True)
     columns_list = ["data", "file_name", "label"]
     num_readers = 4
@@ -461,6 +501,30 @@ def test_cv_minddataset_sequential_sampler_exceed_size(add_and_remove_cv_file):
         num_iter += 1
     assert num_iter == 10
 
+def test_cv_minddataset_sequential_sampler_exceed_size(add_and_remove_cv_file):
+    data = get_data(CV_DIR_NAME, True)
+    columns_list = ["data", "file_name", "label"]
+    num_readers = 4
+    sampler = ds.SequentialSampler(2, 20)
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
+                              sampler=sampler)
+    dataset_size = data_set.get_dataset_size()
+    assert dataset_size == 10
+    num_iter = 0
+    for item in data_set.create_dict_iterator(num_epochs=1, output_numpy=True):
+        logger.info(
+            "-------------- cv reader basic: {} ------------------------".format(num_iter))
+        logger.info(
+            "-------------- item[data]: {}  -----------------------------".format(item["data"]))
+        logger.info(
+            "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+        logger.info(
+            "-------------- item[label]: {} ----------------------------".format(item["label"]))
+        assert item['file_name'] == np.array(
+            data[(num_iter + 2) % dataset_size]['file_name'], dtype='S')
+        num_iter += 1
+    assert num_iter == 10
+
 
 def test_cv_minddataset_split_basic(add_and_remove_cv_file):
     data = get_data(CV_DIR_NAME, True)