dataset: Reinstate INFO logging and data verification - part1

tests/ut/cpp/dataset/c_api_dataset_album_test.cc

@@ -45,8 +45,8 @@ TEST_F(MindDataTestPipeline, TestAlbumBasic) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -100,8 +100,8 @@ TEST_F(MindDataTestPipeline, TestAlbumBasicWithPipeline) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -151,13 +151,11 @@ TEST_F(MindDataTestPipeline, TestAlbumDecode) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    /*
     auto image = row["image"];
-    auto shape = image->shape();
-    MS_LOG(INFO) << "Tensor image shape size: " << shape.Size();
-    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
-    EXPECT_GT(shape.Size(), 1);  // Verify decode=true took effect
-    */
+    auto shape = image.Shape();
+    MS_LOG(INFO) << "Tensor image shape size: " << shape.size();
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    EXPECT_GT(shape.size(), 1);  // Verify decode=true took effect
     iter->GetNextRow(&row);
   }
 
@@ -189,8 +187,8 @@ TEST_F(MindDataTestPipeline, TestAlbumNumSamplers) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 

tests/ut/cpp/dataset/c_api_dataset_cifar_test.cc

@@ -48,8 +48,8 @@ TEST_F(MindDataTestPipeline, TestCifar10Dataset) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -103,8 +103,8 @@ TEST_F(MindDataTestPipeline, TestCifar10DatasetWithPipeline) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -187,8 +187,8 @@ TEST_F(MindDataTestPipeline, TestCifar100Dataset) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 

tests/ut/cpp/dataset/c_api_dataset_clue_test.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 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.
@@ -53,12 +53,12 @@ TEST_F(MindDataTestPipeline, TestCLUEDatasetAFQMC) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["sentence1"];
+    auto text = row["sentence1"];
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
     // EXPECT_STREQ(ss.c_str(), expected_result[i].c_str());
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     iter->GetNextRow(&row);
     i++;
   }
@@ -134,8 +134,8 @@ TEST_F(MindDataTestPipeline, TestCLUEDatasetBasic) {
   EXPECT_NE(row.find("sentence1"), row.end());
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["sentence1"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["sentence1"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     i++;
     iter->GetNextRow(&row);
   }
@@ -190,8 +190,8 @@ TEST_F(MindDataTestPipeline, TestCLUEDatasetBasicWithPipeline) {
   EXPECT_NE(row.find("sentence1"), row.end());
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["sentence1"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["sentence1"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     i++;
     iter->GetNextRow(&row);
   }
@@ -242,12 +242,12 @@ TEST_F(MindDataTestPipeline, TestCLUEDatasetCMNLI) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["sentence1"];
+    auto text = row["sentence1"];
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
     // EXPECT_STREQ(ss.c_str(), expected_result[i].c_str());
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     iter->GetNextRow(&row);
     i++;
   }
@@ -283,12 +283,12 @@ TEST_F(MindDataTestPipeline, TestCLUEDatasetCSL) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["abst"];
+    auto text = row["abst"];
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
     // EXPECT_STREQ(ss.c_str(), expected_result[i].c_str());
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     iter->GetNextRow(&row);
     i++;
   }
@@ -322,8 +322,8 @@ TEST_F(MindDataTestPipeline, TestCLUEDatasetDistribution) {
   EXPECT_NE(row.find("sentence1"), row.end());
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["sentence1"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["sentence1"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     i++;
     iter->GetNextRow(&row);
   }
@@ -424,12 +424,12 @@ TEST_F(MindDataTestPipeline, TestCLUEDatasetIFLYTEK) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["sentence"];
+    auto text = row["sentence"];
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
     // EXPECT_STREQ(ss.c_str(), expected_result[i].c_str());
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     iter->GetNextRow(&row);
     i++;
   }
@@ -602,13 +602,12 @@ TEST_F(MindDataTestPipeline, TestCLUEDatasetShuffleGlobal) {
   //                                             "蚂蚁借呗等额还款能否换成先息后本"};
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["sentence1"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["sentence1"];
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
     // EXPECT_STREQ(ss.c_str(), expected_result[i].c_str());
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     i++;
     iter->GetNextRow(&row);
   }
@@ -648,12 +647,12 @@ TEST_F(MindDataTestPipeline, TestCLUEDatasetTNEWS) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["sentence"];
+    auto text = row["sentence"];
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
     // EXPECT_STREQ(ss.c_str(), expected_result[i].c_str());
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     iter->GetNextRow(&row);
     i++;
   }
@@ -690,12 +689,12 @@ TEST_F(MindDataTestPipeline, TestCLUEDatasetWSC) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["text"];
+    auto text = row["text"];
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
     // EXPECT_STREQ(ss.c_str(), expected_result[i].c_str());
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     iter->GetNextRow(&row);
     i++;
   }

tests/ut/cpp/dataset/c_api_dataset_coco_test.cc

@@ -45,12 +45,12 @@ TEST_F(MindDataTestPipeline, TestCocoDefault) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto image = row["image"];
-    // auto bbox = row["bbox"];
-    // auto category_id = row["category_id"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
-    // MS_LOG(INFO) << "Tensor bbox shape: " << bbox->shape();
-    // MS_LOG(INFO) << "Tensor category_id shape: " << category_id->shape();
+    auto image = row["image"];
+    auto bbox = row["bbox"];
+    auto category_id = row["category_id"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    MS_LOG(INFO) << "Tensor bbox shape: " << bbox.Shape();
+    MS_LOG(INFO) << "Tensor category_id shape: " << category_id.Shape();
     iter->GetNextRow(&row);
     i++;
   }
@@ -102,12 +102,12 @@ TEST_F(MindDataTestPipeline, TestCocoDefaultWithPipeline) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto image = row["image"];
-    // auto bbox = row["bbox"];
-    // auto category_id = row["category_id"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
-    // MS_LOG(INFO) << "Tensor bbox shape: " << bbox->shape();
-    // MS_LOG(INFO) << "Tensor category_id shape: " << category_id->shape();
+    auto image = row["image"];
+    auto bbox = row["bbox"];
+    auto category_id = row["category_id"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    MS_LOG(INFO) << "Tensor bbox shape: " << bbox.Shape();
+    MS_LOG(INFO) << "Tensor category_id shape: " << category_id.Shape();
     iter->GetNextRow(&row);
     i++;
   }

tests/ut/cpp/dataset/c_api_dataset_csv_test.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 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.

tests/ut/cpp/dataset/c_api_dataset_iterator_test.cc

@@ -1,4 +1,3 @@
-
 /**
  * Copyright 2020-2021 Huawei Technologies Co., Ltd
  *
@@ -18,8 +17,6 @@
 #include "minddata/dataset/include/datasets.h"
 
 using namespace mindspore::dataset;
-using mindspore::dataset::Tensor;
-using mindspore::dataset::TensorShape;
 
 class MindDataTestPipeline : public UT::DatasetOpTesting {
  protected:
@@ -43,14 +40,15 @@ TEST_F(MindDataTestPipeline, TestIteratorEmptyColumn) {
   // Iterate the dataset and get each row
   std::vector<mindspore::MSTensor> row;
   iter->GetNextRow(&row);
-  // TensorShape expect0({32, 32, 3});
-  // TensorShape expect1({});
+  std::vector<int64_t> expect_image = {32, 32, 3};
+  std::vector<int64_t> expect_label = {};
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // MS_LOG(INFO) << "row[0]:" << row[0]->shape() << ", row[1]:" << row[1]->shape();
-    // EXPECT_EQ(expect0, row[0]->shape());
-    // EXPECT_EQ(expect1, row[1]->shape());
+    MS_LOG(INFO) << "row[0]:" << row[0].Shape() << ", row[1]:" << row[1].Shape();
+    EXPECT_EQ(expect_image, row[0].Shape());
+    EXPECT_EQ(expect_label, row[1].Shape());
+
     iter->GetNextRow(&row);
     i++;
   }
@@ -82,14 +80,14 @@ TEST_F(MindDataTestPipeline, TestIteratorOneColumn) {
   // Iterate the dataset and get each row
   std::vector<mindspore::MSTensor> row;
   iter->GetNextRow(&row);
-  // TensorShape expect({2, 28, 28, 1});
+  std::vector<int64_t> expect_image = {2, 28, 28, 1};
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // for (auto &v : row) {
-    //   MS_LOG(INFO) << "image shape:" << v->shape();
-    //   EXPECT_EQ(expect, v->shape());
-    // }
+    for (auto &v : row) {
+      MS_LOG(INFO) << "image shape:" << v.Shape();
+      EXPECT_EQ(expect_image, v.Shape());
+    }
     iter->GetNextRow(&row);
     i++;
   }
@@ -120,16 +118,15 @@ TEST_F(MindDataTestPipeline, TestIteratorReOrder) {
   // Iterate the dataset and get each row
   std::vector<mindspore::MSTensor> row;
   iter->GetNextRow(&row);
-  // TensorShape expect0({32, 32, 3});
-  // TensorShape expect1({});
+  std::vector<int64_t> expect_image = {32, 32, 3};
+  std::vector<int64_t> expect_label = {};
 
-  // Check if we will catch "label" before "image" in row
-  // std::vector<std::string> expect = {"label", "image"};
+  // Check "label" before "image" in row
   uint64_t i = 0;
   while (row.size() != 0) {
-    // MS_LOG(INFO) << "row[0]:" << row[0]->shape() << ", row[1]:" << row[1]->shape();
-    // EXPECT_EQ(expect1, row[0]->shape());
-    // EXPECT_EQ(expect0, row[1]->shape());
+    MS_LOG(INFO) << "row[0]:" << row[0].Shape() << ", row[1]:" << row[1].Shape();
+    EXPECT_EQ(expect_label, row[0].Shape());
+    EXPECT_EQ(expect_image, row[1].Shape());
     iter->GetNextRow(&row);
     i++;
   }
@@ -161,20 +158,19 @@ TEST_F(MindDataTestPipeline, TestIteratorTwoColumns) {
   // Iterate the dataset and get each row
   std::vector<mindspore::MSTensor> row;
   iter->GetNextRow(&row);
-  // std::vector<TensorShape> expect = {TensorShape({173673}), TensorShape({1, 4}),   TensorShape({173673}),
-  //                                    TensorShape({1, 4}),   TensorShape({147025}), TensorShape({1, 4}),
-  //                                    TensorShape({211653}), TensorShape({1, 4})};
+  std::vector<std::vector<int64_t>> expect = {{173673}, {1, 4}, {173673}, {1, 4},
+                                              {147025}, {1, 4}, {211653}, {1, 4}};
 
   uint64_t i = 0;
-  // uint64_t j = 0;
+  uint64_t j = 0;
   while (row.size() != 0) {
-    // MS_LOG(INFO) << "row[0]:" << row[0]->shape() << ", row[1]:" << row[1]->shape();
-    // EXPECT_EQ(2, row.size());
-    // EXPECT_EQ(expect[j++], row[0]->shape());
-    // EXPECT_EQ(expect[j++], row[1]->shape());
+    MS_LOG(INFO) << "row[0]:" << row[0].Shape() << ", row[1]:" << row[1].Shape();
+    EXPECT_EQ(2, row.size());
+    EXPECT_EQ(expect[j++], row[0].Shape());
+    EXPECT_EQ(expect[j++], row[1].Shape());
     iter->GetNextRow(&row);
     i++;
-    // j = (j == expect.size()) ? 0 : j;
+    j = (j == expect.size()) ? 0 : j;
   }
 
   EXPECT_EQ(i, 8);
@@ -222,8 +218,8 @@ TEST_F(MindDataTestPipeline, TestIteratorNumEpoch) {
     EXPECT_EQ(inner_row_cnt, random_data_num_row);
   }
   EXPECT_EQ(total_row_cnt, random_data_num_row * num_epochs);
-  // this will go beyond the random_data_num_row*num_epoch limit, hence error code is expected
-  EXPECT_FALSE(iter->GetNextRow(&row));
+  // This will go beyond the random_data_num_row*num_epoch limit, hence error code is expected
+  EXPECT_ERROR(iter->GetNextRow(&row));
   // Manually terminate the pipeline
   iter->Stop();
 }

tests/ut/cpp/dataset/c_api_dataset_manifest_test.cc

@@ -43,8 +43,8 @@ TEST_F(MindDataTestPipeline, TestManifestBasic) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -95,8 +95,8 @@ TEST_F(MindDataTestPipeline, TestManifestBasicWithPipeline) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -162,11 +162,11 @@ TEST_F(MindDataTestPipeline, TestManifestDecode) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // auto shape = image->shape();
-    // MS_LOG(INFO) << "Tensor image shape size: " << shape.Size();
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
-    // EXPECT_GT(shape.Size(), 1);  // Verify decode=true took effect
+    auto image = row["image"];
+    auto shape = image.Shape();
+    MS_LOG(INFO) << "Tensor image shape size: " << shape.size();
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    EXPECT_GT(shape.size(), 1);  // Verify decode=true took effect
     iter->GetNextRow(&row);
   }
 
@@ -196,8 +196,8 @@ TEST_F(MindDataTestPipeline, TestManifestEval) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -241,8 +241,8 @@ TEST_F(MindDataTestPipeline, TestManifestClassIndex) {
   // int32_t label_idx = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     // row["label"]->GetItemAt<int32_t>(&label_idx, {});
     // MS_LOG(INFO) << "Tensor label value: " << label_idx;
     // auto label_it = std::find(expected_label.begin(), expected_label.end(), label_idx);
@@ -276,8 +276,8 @@ TEST_F(MindDataTestPipeline, TestManifestNumSamplers) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 

tests/ut/cpp/dataset/c_api_dataset_minddata_test.cc

@@ -246,7 +246,7 @@ TEST_F(MindDataTestPipeline, TestMindDataSuccess6) {
   EXPECT_NE(ds5, nullptr);
 
   std::vector<std::shared_ptr<Dataset>> ds = {ds1, ds2, ds3, ds4, ds5, ds6};
-  // std::vector<int32_t> expected_samples = {5, 5, 2, 3, 3, 2};
+  std::vector<int32_t> expected_samples = {5, 5, 2, 3, 3, 2};
 
   for (int32_t i = 0; i < ds.size(); i++) {
     // Create an iterator over the result of the above dataset
@@ -258,13 +258,13 @@ TEST_F(MindDataTestPipeline, TestMindDataSuccess6) {
     std::unordered_map<std::string, mindspore::MSTensor> row;
     iter->GetNextRow(&row);
 
-    // uint64_t j = 0;
-    // while (row.size() != 0) {
-    //   j++;
-    //   MS_LOG(INFO) << "Tensor label: " << *row["label"];
-    //   iter->GetNextRow(&row);
-    // }
-    // EXPECT_EQ(j, expected_samples[i]);
+    uint64_t j = 0;
+    while (row.size() != 0) {
+      j++;
+      // MS_LOG(INFO) << "Tensor label: " << *row["label"];
+      iter->GetNextRow(&row);
+    }
+    EXPECT_EQ(j, expected_samples[i]);
 
     // Manually terminate the pipeline
     iter->Stop();

tests/ut/cpp/dataset/c_api_dataset_ops_test.cc

@@ -101,8 +101,8 @@ TEST_F(MindDataTestPipeline, TestBatchAndRepeat) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -137,8 +137,8 @@ TEST_F(MindDataTestPipeline, TestBucketBatchByLengthSuccess1) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
   // 2 batches of size 5
@@ -174,8 +174,8 @@ TEST_F(MindDataTestPipeline, TestBucketBatchByLengthSuccess2) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
   // With 2 boundaries, 3 buckets are created
@@ -486,8 +486,8 @@ TEST_F(MindDataTestPipeline, TestConcatSuccess) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -566,8 +566,8 @@ TEST_F(MindDataTestPipeline, TestConcatSuccess2) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -780,8 +780,8 @@ TEST_F(MindDataTestPipeline, TestImageFolderBatchAndRepeat) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -917,8 +917,8 @@ TEST_F(MindDataTestPipeline, TestProjectMap) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -1055,9 +1055,9 @@ TEST_F(MindDataTestPipeline, TestProjectMapAutoInjection) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
-    // EXPECT_EQ(image->shape()[0], 30);
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    // EXPECT_EQ(image.Shape()[0], 30);
     iter->GetNextRow(&row);
   }
 
@@ -1177,8 +1177,8 @@ TEST_F(MindDataTestPipeline, TestRenameSuccess) {
 
   while (row.size() != 0) {
     i++;
-    // auto image = row["col1"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["col1"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -1221,8 +1221,8 @@ TEST_F(MindDataTestPipeline, TestRepeatDefault) {
       break;
     }
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -1260,8 +1260,8 @@ TEST_F(MindDataTestPipeline, TestRepeatOne) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -1344,8 +1344,8 @@ TEST_F(MindDataTestPipeline, TestShuffleDataset) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -1380,8 +1380,8 @@ TEST_F(MindDataTestPipeline, TestSkipDataset) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
   MS_LOG(INFO) << "Number of rows: " << i;
@@ -1425,8 +1425,8 @@ TEST_F(MindDataTestPipeline, TestSkipTakeRepeat) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
   MS_LOG(INFO) << "Number of rows: " << i;
@@ -1497,8 +1497,8 @@ TEST_F(MindDataTestPipeline, TestTakeDatasetDefault) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
   MS_LOG(INFO) << "Number of rows: " << i;
@@ -1578,8 +1578,8 @@ TEST_F(MindDataTestPipeline, TestTakeDatasetNormal) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
   MS_LOG(INFO) << "Number of rows: " << i;
@@ -1632,8 +1632,8 @@ TEST_F(MindDataTestPipeline, TestTensorOpsAndMap) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -1751,8 +1751,8 @@ TEST_F(MindDataTestPipeline, TestZipSuccess) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 
@@ -1843,8 +1843,8 @@ TEST_F(MindDataTestPipeline, TestZipSuccess2) {
   uint64_t i = 0;
   while (row.size() != 0) {
     i++;
-    // auto image = row["image"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
   }
 

tests/ut/cpp/dataset/c_api_dataset_randomdata_test.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 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.
@@ -54,10 +54,10 @@ TEST_F(MindDataTestPipeline, TestRandomDatasetBasic1) {
   // Check if RandomDataOp read correct columns
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto image = row["image"];
-    // auto label = row["label"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
-    // MS_LOG(INFO) << "Tensor label shape: " << label->shape();
+    auto image = row["image"];
+    auto label = row["label"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    MS_LOG(INFO) << "Tensor label shape: " << label.Shape();
 
     iter->GetNextRow(&row);
     i++;
@@ -112,10 +112,10 @@ TEST_F(MindDataTestPipeline, TestRandomDatasetBasicWithPipeline) {
   // Check if RandomDataOp read correct columns
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto image = row["image"];
-    // auto label = row["label"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
-    // MS_LOG(INFO) << "Tensor label shape: " << label->shape();
+    auto image = row["image"];
+    auto label = row["label"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    MS_LOG(INFO) << "Tensor label shape: " << label.Shape();
 
     iter->GetNextRow(&row);
     i++;
@@ -205,47 +205,52 @@ TEST_F(MindDataTestPipeline, TestRandomDatasetBasic3) {
   std::unordered_map<std::string, mindspore::MSTensor> row;
   iter->GetNextRow(&row);
 
+  std::vector<int64_t> expect_num = {1};
+  std::vector<int64_t> expect_1d = {2};
+  std::vector<int64_t> expect_2d = {2, 2};
+  std::vector<int64_t> expect_3d = {2, 2, 2};
+
   // Check if RandomDataOp read correct columns
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto col_sint16 = row["col_sint16"];
-    // auto col_sint32 = row["col_sint32"];
-    // auto col_sint64 = row["col_sint64"];
-    // auto col_float = row["col_float"];
-    // auto col_1d = row["col_1d"];
-    // auto col_2d = row["col_2d"];
-    // auto col_3d = row["col_3d"];
-    // auto col_binary = row["col_binary"];
-
-    // // validate shape
-    // ASSERT_EQ(col_sint16->shape(), TensorShape({1}));
-    // ASSERT_EQ(col_sint32->shape(), TensorShape({1}));
-    // ASSERT_EQ(col_sint64->shape(), TensorShape({1}));
-    // ASSERT_EQ(col_float->shape(), TensorShape({1}));
-    // ASSERT_EQ(col_1d->shape(), TensorShape({2}));
-    // ASSERT_EQ(col_2d->shape(), TensorShape({2, 2}));
-    // ASSERT_EQ(col_3d->shape(), TensorShape({2, 2, 2}));
-    // ASSERT_EQ(col_binary->shape(), TensorShape({1}));
-
-    // // validate Rank
-    // ASSERT_EQ(col_sint16->Rank(), 1);
-    // ASSERT_EQ(col_sint32->Rank(), 1);
-    // ASSERT_EQ(col_sint64->Rank(), 1);
-    // ASSERT_EQ(col_float->Rank(), 1);
-    // ASSERT_EQ(col_1d->Rank(), 1);
-    // ASSERT_EQ(col_2d->Rank(), 2);
-    // ASSERT_EQ(col_3d->Rank(), 3);
-    // ASSERT_EQ(col_binary->Rank(), 1);
-
-    // // validate type
-    // ASSERT_EQ(col_sint16->type(), DataType::DE_INT16);
-    // ASSERT_EQ(col_sint32->type(), DataType::DE_INT32);
-    // ASSERT_EQ(col_sint64->type(), DataType::DE_INT64);
-    // ASSERT_EQ(col_float->type(), DataType::DE_FLOAT32);
-    // ASSERT_EQ(col_1d->type(), DataType::DE_INT64);
-    // ASSERT_EQ(col_2d->type(), DataType::DE_INT64);
-    // ASSERT_EQ(col_3d->type(), DataType::DE_INT64);
-    // ASSERT_EQ(col_binary->type(), DataType::DE_UINT8);
+    auto col_sint16 = row["col_sint16"];
+    auto col_sint32 = row["col_sint32"];
+    auto col_sint64 = row["col_sint64"];
+    auto col_float = row["col_float"];
+    auto col_1d = row["col_1d"];
+    auto col_2d = row["col_2d"];
+    auto col_3d = row["col_3d"];
+    auto col_binary = row["col_binary"];
+
+    // Validate shape
+    ASSERT_EQ(col_sint16.Shape(), expect_num);
+    ASSERT_EQ(col_sint32.Shape(), expect_num);
+    ASSERT_EQ(col_sint64.Shape(), expect_num);
+    ASSERT_EQ(col_float.Shape(), expect_num);
+    ASSERT_EQ(col_1d.Shape(), expect_1d);
+    ASSERT_EQ(col_2d.Shape(), expect_2d);
+    ASSERT_EQ(col_3d.Shape(), expect_3d);
+    ASSERT_EQ(col_binary.Shape(), expect_num);
+
+    // Validate Rank
+    ASSERT_EQ(col_sint16.Shape().size(), 1);
+    ASSERT_EQ(col_sint32.Shape().size(), 1);
+    ASSERT_EQ(col_sint64.Shape().size(), 1);
+    ASSERT_EQ(col_float.Shape().size(), 1);
+    ASSERT_EQ(col_1d.Shape().size(), 1);
+    ASSERT_EQ(col_2d.Shape().size(), 2);
+    ASSERT_EQ(col_3d.Shape().size(), 3);
+    ASSERT_EQ(col_binary.Shape().size(), 1);
+
+    // Validate type
+    ASSERT_EQ(col_sint16.DataType(), mindspore::DataType::kNumberTypeInt16);
+    ASSERT_EQ(col_sint32.DataType(), mindspore::DataType::kNumberTypeInt32);
+    ASSERT_EQ(col_sint64.DataType(), mindspore::DataType::kNumberTypeInt64);
+    ASSERT_EQ(col_float.DataType(), mindspore::DataType::kNumberTypeFloat32);
+    ASSERT_EQ(col_1d.DataType(), mindspore::DataType::kNumberTypeInt64);
+    ASSERT_EQ(col_2d.DataType(), mindspore::DataType::kNumberTypeInt64);
+    ASSERT_EQ(col_3d.DataType(), mindspore::DataType::kNumberTypeInt64);
+    ASSERT_EQ(col_binary.DataType(), mindspore::DataType::kNumberTypeUInt8);
 
     iter->GetNextRow(&row);
     i++;
@@ -282,47 +287,52 @@ TEST_F(MindDataTestPipeline, TestRandomDatasetBasic4) {
   std::unordered_map<std::string, mindspore::MSTensor> row;
   iter->GetNextRow(&row);
 
+  std::vector<int64_t> expect_num = {1};
+  std::vector<int64_t> expect_1d = {2};
+  std::vector<int64_t> expect_2d = {2, 2};
+  std::vector<int64_t> expect_3d = {2, 2, 2};
+
   // Check if RandomDataOp read correct columns
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto col_sint16 = row["col_sint16"];
-    // auto col_sint32 = row["col_sint32"];
-    // auto col_sint64 = row["col_sint64"];
-    // auto col_float = row["col_float"];
-    // auto col_1d = row["col_1d"];
-    // auto col_2d = row["col_2d"];
-    // auto col_3d = row["col_3d"];
-    // auto col_binary = row["col_binary"];
-
-    // // validate shape
-    // ASSERT_EQ(col_sint16->shape(), TensorShape({1}));
-    // ASSERT_EQ(col_sint32->shape(), TensorShape({1}));
-    // ASSERT_EQ(col_sint64->shape(), TensorShape({1}));
-    // ASSERT_EQ(col_float->shape(), TensorShape({1}));
-    // ASSERT_EQ(col_1d->shape(), TensorShape({2}));
-    // ASSERT_EQ(col_2d->shape(), TensorShape({2, 2}));
-    // ASSERT_EQ(col_3d->shape(), TensorShape({2, 2, 2}));
-    // ASSERT_EQ(col_binary->shape(), TensorShape({1}));
-
-    // // validate Rank
-    // ASSERT_EQ(col_sint16->Rank(), 1);
-    // ASSERT_EQ(col_sint32->Rank(), 1);
-    // ASSERT_EQ(col_sint64->Rank(), 1);
-    // ASSERT_EQ(col_float->Rank(), 1);
-    // ASSERT_EQ(col_1d->Rank(), 1);
-    // ASSERT_EQ(col_2d->Rank(), 2);
-    // ASSERT_EQ(col_3d->Rank(), 3);
-    // ASSERT_EQ(col_binary->Rank(), 1);
-
-    // // validate type
-    // ASSERT_EQ(col_sint16->type(), DataType::DE_INT16);
-    // ASSERT_EQ(col_sint32->type(), DataType::DE_INT32);
-    // ASSERT_EQ(col_sint64->type(), DataType::DE_INT64);
-    // ASSERT_EQ(col_float->type(), DataType::DE_FLOAT32);
-    // ASSERT_EQ(col_1d->type(), DataType::DE_INT64);
-    // ASSERT_EQ(col_2d->type(), DataType::DE_INT64);
-    // ASSERT_EQ(col_3d->type(), DataType::DE_INT64);
-    // ASSERT_EQ(col_binary->type(), DataType::DE_UINT8);
+    auto col_sint16 = row["col_sint16"];
+    auto col_sint32 = row["col_sint32"];
+    auto col_sint64 = row["col_sint64"];
+    auto col_float = row["col_float"];
+    auto col_1d = row["col_1d"];
+    auto col_2d = row["col_2d"];
+    auto col_3d = row["col_3d"];
+    auto col_binary = row["col_binary"];
+
+    // Validate shape
+    ASSERT_EQ(col_sint16.Shape(), expect_num);
+    ASSERT_EQ(col_sint32.Shape(), expect_num);
+    ASSERT_EQ(col_sint64.Shape(), expect_num);
+    ASSERT_EQ(col_float.Shape(), expect_num);
+    ASSERT_EQ(col_1d.Shape(), expect_1d);
+    ASSERT_EQ(col_2d.Shape(), expect_2d);
+    ASSERT_EQ(col_3d.Shape(), expect_3d);
+    ASSERT_EQ(col_binary.Shape(), expect_num);
+
+    // Validate Rank
+    ASSERT_EQ(col_sint16.Shape().size(), 1);
+    ASSERT_EQ(col_sint32.Shape().size(), 1);
+    ASSERT_EQ(col_sint64.Shape().size(), 1);
+    ASSERT_EQ(col_float.Shape().size(), 1);
+    ASSERT_EQ(col_1d.Shape().size(), 1);
+    ASSERT_EQ(col_2d.Shape().size(), 2);
+    ASSERT_EQ(col_3d.Shape().size(), 3);
+    ASSERT_EQ(col_binary.Shape().size(), 1);
+
+    // Validate type
+    ASSERT_EQ(col_sint16.DataType(), mindspore::DataType::kNumberTypeInt16);
+    ASSERT_EQ(col_sint32.DataType(), mindspore::DataType::kNumberTypeInt32);
+    ASSERT_EQ(col_sint64.DataType(), mindspore::DataType::kNumberTypeInt64);
+    ASSERT_EQ(col_float.DataType(), mindspore::DataType::kNumberTypeFloat32);
+    ASSERT_EQ(col_1d.DataType(), mindspore::DataType::kNumberTypeInt64);
+    ASSERT_EQ(col_2d.DataType(), mindspore::DataType::kNumberTypeInt64);
+    ASSERT_EQ(col_3d.DataType(), mindspore::DataType::kNumberTypeInt64);
+    ASSERT_EQ(col_binary.DataType(), mindspore::DataType::kNumberTypeUInt8);
 
     iter->GetNextRow(&row);
     i++;
@@ -359,29 +369,32 @@ TEST_F(MindDataTestPipeline, TestRandomDatasetBasic5) {
   std::unordered_map<std::string, mindspore::MSTensor> row;
   iter->GetNextRow(&row);
 
+  std::vector<int64_t> expect_num = {1};
+  std::vector<int64_t> expect_1d = {2};
+
   // Check if RandomDataOp read correct columns
   uint64_t i = 0;
   while (row.size() != 0) {
     EXPECT_EQ(row.size(), 3);
 
-    // auto col_sint32 = row["col_sint32"];
-    // auto col_sint64 = row["col_sint64"];
-    // auto col_1d = row["col_1d"];
+    auto col_sint32 = row["col_sint32"];
+    auto col_sint64 = row["col_sint64"];
+    auto col_1d = row["col_1d"];
 
-    // // validate shape
-    // ASSERT_EQ(col_sint32->shape(), TensorShape({1}));
-    // ASSERT_EQ(col_sint64->shape(), TensorShape({1}));
-    // ASSERT_EQ(col_1d->shape(), TensorShape({2}));
+    // Validate shape
+    ASSERT_EQ(col_sint32.Shape(), expect_num);
+    ASSERT_EQ(col_sint64.Shape(), expect_num);
+    ASSERT_EQ(col_1d.Shape(), expect_1d);
 
-    // // validate Rank
-    // ASSERT_EQ(col_sint32->Rank(), 1);
-    // ASSERT_EQ(col_sint64->Rank(), 1);
-    // ASSERT_EQ(col_1d->Rank(), 1);
+    // Validate Rank
+    ASSERT_EQ(col_sint32.Shape().size(), 1);
+    ASSERT_EQ(col_sint64.Shape().size(), 1);
+    ASSERT_EQ(col_1d.Shape().size(), 1);
 
-    // // validate type
-    // ASSERT_EQ(col_sint32->type(), DataType::DE_INT32);
-    // ASSERT_EQ(col_sint64->type(), DataType::DE_INT64);
-    // ASSERT_EQ(col_1d->type(), DataType::DE_INT64);
+    // Validate type
+    ASSERT_EQ(col_sint32.DataType(), mindspore::DataType::kNumberTypeInt32);
+    ASSERT_EQ(col_sint64.DataType(), mindspore::DataType::kNumberTypeInt64);
+    ASSERT_EQ(col_1d.DataType(), mindspore::DataType::kNumberTypeInt64);
 
     iter->GetNextRow(&row);
     i++;

tests/ut/cpp/dataset/c_api_dataset_textfile_test.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 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.
@@ -58,8 +58,8 @@ TEST_F(MindDataTestPipeline, TestTextFileDatasetBasic) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["text"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["text"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
@@ -128,8 +128,8 @@ TEST_F(MindDataTestPipeline, TestTextFileDatasetBasicWithPipeline) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["text"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["text"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     i++;
     iter->GetNextRow(&row);
   }
@@ -315,8 +315,8 @@ TEST_F(MindDataTestPipeline, TestTextFileDatasetShuffleFalse1A) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["text"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["text"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
@@ -373,8 +373,8 @@ TEST_F(MindDataTestPipeline, TestTextFileDatasetShuffleFalse1B) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["text"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["text"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
@@ -430,8 +430,8 @@ TEST_F(MindDataTestPipeline, TestTextFileDatasetShuffleFalse4Shard) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["text"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["text"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
@@ -490,8 +490,8 @@ TEST_F(MindDataTestPipeline, TestTextFileDatasetShuffleFiles1A) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["text"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["text"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
@@ -550,8 +550,8 @@ TEST_F(MindDataTestPipeline, TestTextFileDatasetShuffleFiles1B) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["text"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["text"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
@@ -609,8 +609,8 @@ TEST_F(MindDataTestPipeline, TestTextFileDatasetShuffleFiles4) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["text"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["text"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
@@ -664,8 +664,8 @@ TEST_F(MindDataTestPipeline, TestTextFileDatasetShuffleGlobal1A) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["text"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["text"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
@@ -722,8 +722,8 @@ TEST_F(MindDataTestPipeline, TestTextFileDatasetShuffleGlobal1B) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["text"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["text"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);
@@ -780,8 +780,8 @@ TEST_F(MindDataTestPipeline, TestTextFileDatasetShuffleGlobal4) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto text = row["text"];
-    // MS_LOG(INFO) << "Tensor text shape: " << text->shape();
+    auto text = row["text"];
+    MS_LOG(INFO) << "Tensor text shape: " << text.Shape();
     // std::string_view sv;
     // text->GetItemAt(&sv, {0});
     // std::string ss(sv);

tests/ut/cpp/dataset/c_api_dataset_tfrecord_test.cc

@@ -70,9 +70,9 @@ TEST_F(MindDataTestPipeline, TestTFRecordDatasetBasic) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto image = row["image"];
+    auto image = row["image"];
 
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
     iter->GetNextRow(&row);
     i++;
   }
@@ -279,23 +279,30 @@ TEST_F(MindDataTestPipeline, TestTFRecordDatasetSchemaObj) {
   EXPECT_NE(row.find("col_float"), row.end());
   EXPECT_NE(row.find("col_2d"), row.end());
 
+  std::vector<int64_t> expect_num = {1};
+  std::vector<int64_t> expect_2d = {2, 2};
+
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto col_sint16 = row["col_sint16"];
-    // auto col_float = row["col_float"];
-    // auto col_2d = row["col_2d"];
+    auto col_sint16 = row["col_sint16"];
+    auto col_float = row["col_float"];
+    auto col_2d = row["col_2d"];
+
+    // Validate shape
+    ASSERT_EQ(col_sint16.Shape(), expect_num);
+    ASSERT_EQ(col_float.Shape(), expect_num);
+    ASSERT_EQ(col_2d.Shape(), expect_2d);
 
-    // EXPECT_EQ(col_sint16->shape(), TensorShape({1}));
-    // EXPECT_EQ(col_float->shape(), TensorShape({1}));
-    // EXPECT_EQ(col_2d->shape(), TensorShape({2, 2}));
+    // Validate Rank
+    ASSERT_EQ(col_sint16.Shape().size(), 1);
+    ASSERT_EQ(col_float.Shape().size(), 1);
+    ASSERT_EQ(col_2d.Shape().size(), 2);
 
-    // EXPECT_EQ(col_sint16->Rank(), 1);
-    // EXPECT_EQ(col_float->Rank(), 1);
-    // EXPECT_EQ(col_2d->Rank(), 2);
+    // Validate type
+    ASSERT_EQ(col_sint16.DataType(), mindspore::DataType::kNumberTypeInt16);
+    ASSERT_EQ(col_float.DataType(), mindspore::DataType::kNumberTypeFloat32);
+    ASSERT_EQ(col_2d.DataType(), mindspore::DataType::kNumberTypeInt64);
 
-    // EXPECT_EQ(col_sint16->type(), DataType::DE_INT16);
-    // EXPECT_EQ(col_float->type(), DataType::DE_FLOAT32);
-    // EXPECT_EQ(col_2d->type(), DataType::DE_INT64);
     iter->GetNextRow(&row);
     i++;
   }
@@ -331,11 +338,11 @@ TEST_F(MindDataTestPipeline, TestTFRecordDatasetNoSchema) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto image = row["image"];
-    // auto label = row["label"];
+    auto image = row["image"];
+    auto label = row["label"];
 
-    // MS_LOG(INFO) << "Shape of column [image]:" << image->shape();
-    // MS_LOG(INFO) << "Shape of column [label]:" << label->shape();
+    MS_LOG(INFO) << "Shape of column [image]:" << image.Shape();
+    MS_LOG(INFO) << "Shape of column [label]:" << label.Shape();
     iter->GetNextRow(&row);
     i++;
   }
@@ -486,16 +493,16 @@ TEST_F(MindDataTestPipeline, TestIncorrectTFSchemaObject) {
   EXPECT_NE(ds, nullptr);
   auto itr = ds->CreateIterator();
   EXPECT_NE(itr, nullptr);
-  // TensorMap mp;
-  // this will fail due to the incorrect schema used
-  // EXPECT_FALSE(itr->GetNextRow(&mp));
+  MSTensorMap mp;
+  // This will fail due to the incorrect schema used
+  EXPECT_ERROR(itr->GetNextRow(&mp));
 }
 
 TEST_F(MindDataTestPipeline, TestIncorrectTFrecordFile) {
   std::string path = datasets_root_path_ + "/test_tf_file_3_images2/datasetSchema.json";
   std::shared_ptr<Dataset> ds = TFRecord({path});
   EXPECT_NE(ds, nullptr);
-  // the tf record file is incorrect, hence validate param will fail
+  // The tf record file is incorrect, hence validate param will fail
   auto itr = ds->CreateIterator();
   EXPECT_EQ(itr, nullptr);
 }

tests/ut/cpp/dataset/c_api_dataset_voc_test.cc

@@ -55,10 +55,10 @@ TEST_F(MindDataTestPipeline, TestVOCClassIndex) {
   // uint32_t expect[] = {9, 9, 9, 1, 1, 0};
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto image = row["image"];
-    // auto label = row["label"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
-    // MS_LOG(INFO) << "Tensor label shape: " << label->shape();
+    auto image = row["image"];
+    auto label = row["label"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    MS_LOG(INFO) << "Tensor label shape: " << label.Shape();
     // expect_label->SetItemAt({0, 0}, expect[i]);
     // EXPECT_EQ(*label, *expect_label);
 
@@ -137,10 +137,10 @@ TEST_F(MindDataTestPipeline, TestVOCDetection) {
   // uint32_t expect_num[] = {5, 5, 4, 3};
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto image = row["image"];
-    // auto label = row["label"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
-    // MS_LOG(INFO) << "Tensor label shape: " << label->shape();
+    auto image = row["image"];
+    auto label = row["label"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    MS_LOG(INFO) << "Tensor label shape: " << label.Shape();
 
     // std::shared_ptr<Tensor> expect_image;
     // Tensor::CreateFromFile(folder_path + "/JPEGImages/" + expect_file[i] + ".jpg", &expect_image);
@@ -210,10 +210,10 @@ TEST_F(MindDataTestPipeline, TestVOCSegmentation) {
   // std::string expect_file[] = {"32", "33", "39", "32", "33", "39"};
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto image = row["image"];
-    // auto target = row["target"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
-    // MS_LOG(INFO) << "Tensor target shape: " << target->shape();
+    auto image = row["image"];
+    auto target = row["target"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    MS_LOG(INFO) << "Tensor target shape: " << target.Shape();
 
     // std::shared_ptr<Tensor> expect_image;
     // Tensor::CreateFromFile(folder_path + "/JPEGImages/" + expect_file[i] + ".jpg", &expect_image);

tests/ut/cpp/dataset/c_api_datasets_test.cc

@@ -93,10 +93,10 @@ TEST_F(MindDataTestPipeline, TestCelebADefault) {
   // Check if CelebAOp read correct images/attr
   uint64_t i = 0;
   while (row.size() != 0) {
-    // auto image = row["image"];
-    // auto attr = row["attr"];
-    // MS_LOG(INFO) << "Tensor image shape: " << image->shape();
-    // MS_LOG(INFO) << "Tensor attr shape: " << attr->shape();
+    auto image = row["image"];
+    auto attr = row["attr"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    MS_LOG(INFO) << "Tensor attr shape: " << attr.Shape();
 
     iter->GetNextRow(&row);
     i++;

tests/ut/cpp/dataset/ir_vision_test.cc

@@ -329,7 +329,7 @@ TEST_F(MindDataTestIRVision, TestSoftDvppDecodeResizeJpegFail) {
 
   Status rc;
 
-  // CSoftDvppDecodeResizeJpeg: size must be a vector of one or two values
+  // SoftDvppDecodeResizeJpeg: size must be a vector of one or two values
   std::shared_ptr<TensorOperation> soft_dvpp_decode_resize_jpeg_op1(new vision::SoftDvppDecodeResizeJpegOperation({}));
   rc = soft_dvpp_decode_resize_jpeg_op1->ValidateParams();
   EXPECT_ERROR(rc);