!3093 VOCDataset output change to multi-columns

Merge pull request !3093 from xiefangqi/md_voc_multi_columns

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

@@ -215,7 +215,7 @@ Status CocoOp::LoadTensorRow(row_id_type row_id, const std::string &image_id, Te
   auto itr = coordinate_map_.find(image_id);
   if (itr == coordinate_map_.end()) RETURN_STATUS_UNEXPECTED("Invalid image_id found :" + image_id);
 
-  std::string kImageFile = image_folder_path_ + image_id;
+  std::string kImageFile = image_folder_path_ + std::string("/") + image_id;
   RETURN_IF_NOT_OK(ReadImageToTensor(kImageFile, data_schema_->column(0), &image));
 
   auto bboxRow = itr->second;

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

@@ -34,7 +34,10 @@ namespace mindspore {
 namespace dataset {
 const char kColumnImage[] = "image";
 const char kColumnTarget[] = "target";
-const char kColumnAnnotation[] = "annotation";
+const char kColumnBbox[] = "bbox";
+const char kColumnLabel[] = "label";
+const char kColumnDifficult[] = "difficult";
+const char kColumnTruncate[] = "truncate";
 const char kJPEGImagesFolder[] = "/JPEGImages/";
 const char kSegmentationClassFolder[] = "/SegmentationClass/";
 const char kAnnotationsFolder[] = "/Annotations/";
@@ -70,7 +73,13 @@ Status VOCOp::Builder::Build(std::shared_ptr<VOCOp> *ptr) {
     RETURN_IF_NOT_OK(builder_schema_->AddColumn(
       ColDescriptor(std::string(kColumnImage), DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1)));
     RETURN_IF_NOT_OK(builder_schema_->AddColumn(
-      ColDescriptor(std::string(kColumnAnnotation), DataType(DataType::DE_FLOAT32), TensorImpl::kFlexible, 1)));
+      ColDescriptor(std::string(kColumnBbox), DataType(DataType::DE_FLOAT32), TensorImpl::kFlexible, 1)));
+    RETURN_IF_NOT_OK(builder_schema_->AddColumn(
+      ColDescriptor(std::string(kColumnLabel), DataType(DataType::DE_UINT32), TensorImpl::kFlexible, 1)));
+    RETURN_IF_NOT_OK(builder_schema_->AddColumn(
+      ColDescriptor(std::string(kColumnDifficult), DataType(DataType::DE_UINT32), TensorImpl::kFlexible, 1)));
+    RETURN_IF_NOT_OK(builder_schema_->AddColumn(
+      ColDescriptor(std::string(kColumnTruncate), DataType(DataType::DE_UINT32), TensorImpl::kFlexible, 1)));
   }
   *ptr = std::make_shared<VOCOp>(builder_task_type_, builder_task_mode_, builder_dir_, builder_labels_to_read_,
                                  builder_num_workers_, builder_rows_per_buffer_, builder_op_connector_size_,
@@ -190,14 +199,16 @@ Status VOCOp::LoadTensorRow(row_id_type row_id, const std::string &image_id, Ten
     RETURN_IF_NOT_OK(ReadImageToTensor(kTargetFile, data_schema_->column(1), &target));
     (*trow) = TensorRow(row_id, {std::move(image), std::move(target)});
   } else if (task_type_ == TaskType::Detection) {
-    std::shared_ptr<Tensor> image, annotation;
+    std::shared_ptr<Tensor> image;
+    TensorRow annotation;
     const std::string kImageFile =
       folder_path_ + std::string(kJPEGImagesFolder) + image_id + std::string(kImageExtension);
     const std::string kAnnotationFile =
       folder_path_ + std::string(kAnnotationsFolder) + image_id + std::string(kAnnotationExtension);
     RETURN_IF_NOT_OK(ReadImageToTensor(kImageFile, data_schema_->column(0), &image));
-    RETURN_IF_NOT_OK(ReadAnnotationToTensor(kAnnotationFile, data_schema_->column(1), &annotation));
+    RETURN_IF_NOT_OK(ReadAnnotationToTensor(kAnnotationFile, &annotation));
-    (*trow) = TensorRow(row_id, {std::move(image), std::move(annotation)});
+    trow->push_back(std::move(image));
+    trow->insert(trow->end(), annotation.begin(), annotation.end());
   }
   return Status::OK();
 }
@@ -271,7 +282,7 @@ Status VOCOp::ParseAnnotationIds() {
     const std::string kAnnotationName =
       folder_path_ + std::string(kAnnotationsFolder) + id + std::string(kAnnotationExtension);
     RETURN_IF_NOT_OK(ParseAnnotationBbox(kAnnotationName));
-    if (label_map_.find(kAnnotationName) != label_map_.end()) {
+    if (annotation_map_.find(kAnnotationName) != annotation_map_.end()) {
       new_image_ids.push_back(id);
     }
   }
@@ -293,7 +304,7 @@ Status VOCOp::ParseAnnotationBbox(const std::string &path) {
   if (!Path(path).Exists()) {
     RETURN_STATUS_UNEXPECTED("File is not found : " + path);
   }
-  Bbox bbox;
+  Annotation annotation;
   XMLDocument doc;
   XMLError e = doc.LoadFile(common::SafeCStr(path));
   if (e != XMLError::XML_SUCCESS) {
@@ -332,13 +343,13 @@ Status VOCOp::ParseAnnotationBbox(const std::string &path) {
     }
     if (label_name != "" && (class_index_.empty() || class_index_.find(label_name) != class_index_.end()) && xmin > 0 &&
         ymin > 0 && xmax > xmin && ymax > ymin) {
-      std::vector<float> bbox_list = {xmin, ymin, xmax - xmin, ymax - ymin, truncated, difficult};
+      std::vector<float> bbox_list = {xmin, ymin, xmax - xmin, ymax - ymin, difficult, truncated};
-      bbox.emplace_back(std::make_pair(label_name, bbox_list));
+      annotation.emplace_back(std::make_pair(label_name, bbox_list));
       label_index_[label_name] = 0;
     }
     object = object->NextSiblingElement("object");
   }
-  if (bbox.size() > 0) label_map_[path] = bbox;
+  if (annotation.size() > 0) annotation_map_[path] = annotation;
   return Status::OK();
 }
 
@@ -374,31 +385,46 @@ Status VOCOp::ReadImageToTensor(const std::string &path, const ColDescriptor &co
   return Status::OK();
 }
 
-Status VOCOp::ReadAnnotationToTensor(const std::string &path, const ColDescriptor &col,
+// When task is Detection, user can get bbox data with four columns:
-                                     std::shared_ptr<Tensor> *tensor) {
+// column ["bbox"] with datatype=float32
-  Bbox bbox_info = label_map_[path];
+// column ["label"] with datatype=uint32
-  std::vector<float> bbox_row;
+// column ["difficult"] with datatype=uint32
-  dsize_t bbox_column_num = 0, bbox_num = 0;
+// column ["truncate"] with datatype=uint32
-  for (auto box : bbox_info) {
+Status VOCOp::ReadAnnotationToTensor(const std::string &path, TensorRow *row) {
-    if (label_index_.find(box.first) != label_index_.end()) {
+  Annotation annotation = annotation_map_[path];
-      std::vector<float> bbox;
+  std::shared_ptr<Tensor> bbox, label, difficult, truncate;
-      bbox.insert(bbox.end(), box.second.begin(), box.second.end());
+  std::vector<float> bbox_data;
-      if (class_index_.find(box.first) != class_index_.end()) {
+  std::vector<uint32_t> label_data, difficult_data, truncate_data;
-        bbox.push_back(static_cast<float>(class_index_[box.first]));
+  dsize_t bbox_num = 0;
+  for (auto item : annotation) {
+    if (label_index_.find(item.first) != label_index_.end()) {
+      if (class_index_.find(item.first) != class_index_.end()) {
+        label_data.push_back(static_cast<uint32_t>(class_index_[item.first]));
       } else {
-        bbox.push_back(static_cast<float>(label_index_[box.first]));
+        label_data.push_back(static_cast<uint32_t>(label_index_[item.first]));
-      }
-      bbox_row.insert(bbox_row.end(), bbox.begin(), bbox.end());
-      if (bbox_column_num == 0) {
-        bbox_column_num = static_cast<dsize_t>(bbox.size());
       }
+      CHECK_FAIL_RETURN_UNEXPECTED(item.second.size() == 6, "annotation only support 6 parameters.");
+
+      std::vector<float> tmp_bbox = {(item.second)[0], (item.second)[1], (item.second)[2], (item.second)[3]};
+      bbox_data.insert(bbox_data.end(), tmp_bbox.begin(), tmp_bbox.end());
+      difficult_data.push_back(static_cast<uint32_t>((item.second)[4]));
+      truncate_data.push_back(static_cast<uint32_t>((item.second)[5]));
       bbox_num++;
     }
   }
-
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(&bbox, data_schema_->column(1).tensorImpl(), TensorShape({bbox_num, 4}),
-  std::vector<dsize_t> bbox_dim = {bbox_num, bbox_column_num};
+                                        data_schema_->column(1).type(),
-  RETURN_IF_NOT_OK(Tensor::CreateTensor(tensor, col.tensorImpl(), TensorShape(bbox_dim), col.type(),
+                                        reinterpret_cast<unsigned char *>(&bbox_data[0])));
-                                        reinterpret_cast<unsigned char *>(&bbox_row[0])));
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(&label, data_schema_->column(2).tensorImpl(), TensorShape({bbox_num, 1}),
+                                        data_schema_->column(2).type(),
+                                        reinterpret_cast<unsigned char *>(&label_data[0])));
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(&difficult, data_schema_->column(3).tensorImpl(), TensorShape({bbox_num, 1}),
+                                        data_schema_->column(3).type(),
+                                        reinterpret_cast<unsigned char *>(&difficult_data[0])));
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(&truncate, data_schema_->column(4).tensorImpl(), TensorShape({bbox_num, 1}),
+                                        data_schema_->column(4).type(),
+                                        reinterpret_cast<unsigned char *>(&truncate_data[0])));
+  (*row) = TensorRow({std::move(bbox), std::move(label), std::move(difficult), std::move(truncate)});
   return Status::OK();
 }
 

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/voc_op.h

@@ -40,7 +40,7 @@ namespace dataset {
 template <typename T>
 class Queue;
 
-using Bbox = std::vector<std::pair<std::string, std::vector<float>>>;
+using Annotation = std::vector<std::pair<std::string, std::vector<float>>>;
 
 class VOCOp : public ParallelOp, public RandomAccessOp {
  public:
@@ -234,10 +234,9 @@ class VOCOp : public ParallelOp, public RandomAccessOp {
   Status ReadImageToTensor(const std::string &path, const ColDescriptor &col, std::shared_ptr<Tensor> *tensor);
 
   // @param const std::string &path - path to the image file
-  // @param const ColDescriptor &col - contains tensor implementation and datatype
+  // @param TensorRow *row - return
-  // @param std::shared_ptr<Tensor> tensor - return
   // @return Status - The error code return
-  Status ReadAnnotationToTensor(const std::string &path, const ColDescriptor &col, std::shared_ptr<Tensor> *tensor);
+  Status ReadAnnotationToTensor(const std::string &path, TensorRow *row);
 
   // @param const std::vector<uint64_t> &keys - keys in ioblock
   // @param std::unique_ptr<DataBuffer> db
@@ -287,7 +286,7 @@ class VOCOp : public ParallelOp, public RandomAccessOp {
   QueueList<std::unique_ptr<IOBlock>> io_block_queues_;
   std::map<std::string, int32_t> class_index_;
   std::map<std::string, int32_t> label_index_;
-  std::map<std::string, Bbox> label_map_;
+  std::map<std::string, Annotation> annotation_map_;
 };
 }  // namespace dataset
 }  // namespace mindspore

mindspore/dataset/engine/datasets.py

@@ -4128,13 +4128,11 @@ class VOCDataset(MappableDataset):
     """
     A source dataset for reading and parsing VOC dataset.
 
-    The generated dataset has two columns :
+    The generated dataset has multi-columns :
-    task='Detection' : ['image', 'annotation'];
+
-    task='Segmentation' : ['image', 'target'].
+        - task='Detection', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['label', dtype=uint32],
-    The shape of both column 'image' and 'target' is [image_size] if decode flag is False, or [H, W, C]
+          ['difficult', dtype=uint32], ['truncate', dtype=uint32]].
-    otherwise.
+        - task='Segmentation', column: [['image', dtype=uint8], ['target',dtype=uint8]].
-    The type of both tensor 'image' and 'target' is uint8.
-    The type of tensor 'annotation' is uint32.
 
     This dataset can take in a sampler. sampler and shuffle are mutually exclusive. Table
     below shows what input args are allowed and their expected behavior.

tests/ut/python/dataset/test_bounding_box_augment.py

@@ -49,9 +49,9 @@ def test_bounding_box_augment_with_rotation_op(plot_vis=False):
     test_op = c_vision.BoundingBoxAugment(c_vision.RandomRotation(90), 1)
 
     # map to apply ops
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[test_op])
 
     filename = "bounding_box_augment_rotation_c_result.npz"
@@ -88,9 +88,9 @@ def test_bounding_box_augment_with_crop_op(plot_vis=False):
     test_op = c_vision.BoundingBoxAugment(c_vision.RandomCrop(50), 0.9)
 
     # map to apply ops
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[test_op])
 
     filename = "bounding_box_augment_crop_c_result.npz"
@@ -126,10 +126,11 @@ def test_bounding_box_augment_valid_ratio_c(plot_vis=False):
     test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 0.9)
 
     # map to apply ops
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
-                            operations=[test_op])  # Add column for "annotation"
+                            operations=[test_op])  # Add column for "bbox"
+
     filename = "bounding_box_augment_valid_ratio_c_result.npz"
     save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
 
@@ -193,20 +194,20 @@ def test_bounding_box_augment_valid_edge_c(plot_vis=False):
     test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 1)
 
     # map to apply ops
-    # Add column for "annotation"
+    # Add column for "bbox"
-    dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
+    dataVoc1 = dataVoc1.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=lambda img, bbox:
                             (img, np.array([[0, 0, img.shape[1], img.shape[0], 0, 0, 0]]).astype(np.float32)))
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=lambda img, bbox:
                             (img, np.array([[0, 0, img.shape[1], img.shape[0], 0, 0, 0]]).astype(np.float32)))
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[test_op])
     filename = "bounding_box_augment_valid_edge_c_result.npz"
     save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
@@ -237,10 +238,10 @@ def test_bounding_box_augment_invalid_ratio_c():
         # ratio range is from 0 - 1
         test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 1.5)
         # map to apply ops
-        dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+        dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                                output_columns=["image", "annotation"],
+                                output_columns=["image", "bbox"],
-                                columns_order=["image", "annotation"],
+                                columns_order=["image", "bbox"],
-                                operations=[test_op])  # Add column for "annotation"
+                                operations=[test_op])  # Add column for "bbox"
     except ValueError as error:
         logger.info("Got an exception in DE: {}".format(str(error)))
         assert "Input ratio is not within the required interval of (0.0 to 1.0)." in str(error)

tests/ut/python/dataset/test_datasets_coco.py

@@ -17,6 +17,7 @@ import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.c_transforms as vision
 
 DATA_DIR = "../data/dataset/testCOCO/train/"
+DATA_DIR_2 = "../data/dataset/testCOCO/train"
 ANNOTATION_FILE = "../data/dataset/testCOCO/annotations/train.json"
 KEYPOINT_FILE = "../data/dataset/testCOCO/annotations/key_point.json"
 PANOPTIC_FILE = "../data/dataset/testCOCO/annotations/panoptic.json"
@@ -202,6 +203,17 @@ def test_coco_case_2():
         num_iter += 1
     assert num_iter == 24
 
+def test_coco_case_3():
+    data1 = ds.CocoDataset(DATA_DIR_2, annotation_file=ANNOTATION_FILE, task="Detection", decode=True)
+    resize_op = vision.Resize((224, 224))
+
+    data1 = data1.map(input_columns=["image"], operations=resize_op)
+    data1 = data1.repeat(4)
+    num_iter = 0
+    for _ in data1.__iter__():
+        num_iter += 1
+    assert num_iter == 24
+
 def test_coco_case_exception():
     try:
         data1 = ds.CocoDataset("path_not_exist/", annotation_file=ANNOTATION_FILE, task="Detection")
@@ -271,4 +283,5 @@ if __name__ == '__main__':
     test_coco_case_0()
     test_coco_case_1()
     test_coco_case_2()
+    test_coco_case_3()
     test_coco_case_exception()

tests/ut/python/dataset/test_datasets_voc.py

@@ -36,8 +36,8 @@ def test_voc_detection():
     count = [0, 0, 0, 0, 0, 0]
     for item in data1.create_dict_iterator():
         assert item["image"].shape[0] == IMAGE_SHAPE[num]
-        for bbox in item["annotation"]:
+        for label in item["label"]:
-            count[int(bbox[6])] += 1
+            count[label[0]] += 1
         num += 1
     assert num == 9
     assert count == [3, 2, 1, 2, 4, 3]
@@ -54,9 +54,9 @@ def test_voc_class_index():
     num = 0
     count = [0, 0, 0, 0, 0, 0]
     for item in data1.create_dict_iterator():
-        for bbox in item["annotation"]:
+        for label in item["label"]:
-            assert (int(bbox[6]) == 0 or int(bbox[6]) == 1 or int(bbox[6]) == 5)
+            count[label[0]] += 1
-            count[int(bbox[6])] += 1
+            assert label[0] in (0, 1, 5)
         num += 1
     assert num == 6
     assert count == [3, 2, 0, 0, 0, 3]
@@ -72,10 +72,9 @@ def test_voc_get_class_indexing():
     num = 0
     count = [0, 0, 0, 0, 0, 0]
     for item in data1.create_dict_iterator():
-        for bbox in item["annotation"]:
+        for label in item["label"]:
-            assert (int(bbox[6]) == 0 or int(bbox[6]) == 1 or int(bbox[6]) == 2 or int(bbox[6]) == 3
+            count[label[0]] += 1
-                    or int(bbox[6]) == 4 or int(bbox[6]) == 5)
+            assert label[0] in (0, 1, 2, 3, 4, 5)
-            count[int(bbox[6])] += 1
         num += 1
     assert num == 9
     assert count == [3, 2, 1, 2, 4, 3]

tests/ut/python/dataset/test_random_crop_and_resize_with_bbox.py

@@ -48,9 +48,9 @@ def test_random_resized_crop_with_bbox_op_c(plot_vis=False):
     test_op = c_vision.RandomResizedCropWithBBox((256, 512), (0.5, 0.5), (0.5, 0.5))
 
     # map to apply ops
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[test_op])
 
     filename = "random_resized_crop_with_bbox_01_c_result.npz"
@@ -114,15 +114,15 @@ def test_random_resized_crop_with_bbox_op_edge_c(plot_vis=False):
     test_op = c_vision.RandomResizedCropWithBBox((256, 512), (0.5, 0.5), (0.5, 0.5))
 
     # maps to convert data into valid edge case data
-    dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
+    dataVoc1 = dataVoc1.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
 
     # Test Op added to list of Operations here
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
 
     unaugSamp, augSamp = [], []
@@ -149,9 +149,9 @@ def test_random_resized_crop_with_bbox_op_invalid_c():
         test_op = c_vision.RandomResizedCropWithBBox((256, 512), (1, 0.5), (0.5, 0.5))
 
         # map to apply ops
-        dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+        dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                                output_columns=["image", "annotation"],
+                                output_columns=["image", "bbox"],
-                                columns_order=["image", "annotation"],
+                                columns_order=["image", "bbox"],
                                 operations=[test_op])
 
         for _ in dataVoc2.create_dict_iterator():
@@ -175,9 +175,9 @@ def test_random_resized_crop_with_bbox_op_invalid2_c():
         test_op = c_vision.RandomResizedCropWithBBox((256, 512), (1, 1), (1, 0.5))
 
         # map to apply ops
-        dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+        dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                                output_columns=["image", "annotation"],
+                                output_columns=["image", "bbox"],
-                                columns_order=["image", "annotation"],
+                                columns_order=["image", "bbox"],
                                 operations=[test_op])
 
         for _ in dataVoc2.create_dict_iterator():
@@ -206,9 +206,9 @@ def test_random_resized_crop_with_bbox_op_bad_c():
 
 
 if __name__ == "__main__":
-    test_random_resized_crop_with_bbox_op_c(plot_vis=True)
+    test_random_resized_crop_with_bbox_op_c(plot_vis=False)
-    test_random_resized_crop_with_bbox_op_coco_c(plot_vis=True)
+    test_random_resized_crop_with_bbox_op_coco_c(plot_vis=False)
-    test_random_resized_crop_with_bbox_op_edge_c(plot_vis=True)
+    test_random_resized_crop_with_bbox_op_edge_c(plot_vis=False)
     test_random_resized_crop_with_bbox_op_invalid_c()
     test_random_resized_crop_with_bbox_op_invalid2_c()
     test_random_resized_crop_with_bbox_op_bad_c()

tests/ut/python/dataset/test_random_crop_with_bbox.py

@@ -46,10 +46,10 @@ def test_random_crop_with_bbox_op_c(plot_vis=False):
     test_op = c_vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
 
     # map to apply ops
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
-                            operations=[test_op])  # Add column for "annotation"
+                            operations=[test_op])  # Add column for "bbox"
 
     unaugSamp, augSamp = [], []
 
@@ -108,9 +108,9 @@ def test_random_crop_with_bbox_op2_c(plot_vis=False):
     test_op = c_vision.RandomCropWithBBox(512, [200, 200, 200, 200], fill_value=(255, 255, 255))
 
     # map to apply ops
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[test_op])
 
     filename = "random_crop_with_bbox_01_c_result.npz"
@@ -145,9 +145,9 @@ def test_random_crop_with_bbox_op3_c(plot_vis=False):
     test_op = c_vision.RandomCropWithBBox(512, [200, 200, 200, 200], padding_mode=mode.Border.EDGE)
 
     # map to apply ops
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[test_op])
 
     unaugSamp, augSamp = [], []
@@ -175,16 +175,16 @@ def test_random_crop_with_bbox_op_edge_c(plot_vis=False):
     test_op = c_vision.RandomCropWithBBox(512, [200, 200, 200, 200], padding_mode=mode.Border.EDGE)
 
     # maps to convert data into valid edge case data
-    dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
+    dataVoc1 = dataVoc1.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[lambda img, bboxes: (
                                 img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
 
     # Test Op added to list of Operations here
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[lambda img, bboxes: (
                                 img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
 
@@ -212,10 +212,10 @@ def test_random_crop_with_bbox_op_invalid_c():
         test_op = c_vision.RandomCropWithBBox([512, 512, 375])
 
         # map to apply ops
-        dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+        dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                                output_columns=["image", "annotation"],
+                                output_columns=["image", "bbox"],
-                                columns_order=["image", "annotation"],
+                                columns_order=["image", "bbox"],
-                                operations=[test_op])  # Add column for "annotation"
+                                operations=[test_op])  # Add column for "bbox"
 
         for _ in dataVoc2.create_dict_iterator():
             break

tests/ut/python/dataset/test_random_horizontal_flip_with_bbox.py

@@ -45,9 +45,9 @@ def test_random_horizontal_flip_with_bbox_op_c(plot_vis=False):
 
     test_op = c_vision.RandomHorizontalFlipWithBBox(1)
 
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[test_op])
 
     unaugSamp, augSamp = [], []
@@ -111,9 +111,9 @@ def test_random_horizontal_flip_with_bbox_valid_rand_c(plot_vis=False):
     test_op = c_vision.RandomHorizontalFlipWithBBox(0.6)
 
     # map to apply ops
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[test_op])
 
     filename = "random_horizontal_flip_with_bbox_01_c_result.npz"
@@ -146,20 +146,20 @@ def test_random_horizontal_flip_with_bbox_valid_edge_c(plot_vis=False):
     test_op = c_vision.RandomHorizontalFlipWithBBox(1)
 
     # map to apply ops
-    # Add column for "annotation"
+    # Add column for "bbox"
-    dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
+    dataVoc1 = dataVoc1.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=lambda img, bbox:
                             (img, np.array([[0, 0, img.shape[1], img.shape[0], 0, 0, 0]]).astype(np.float32)))
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=lambda img, bbox:
                             (img, np.array([[0, 0, img.shape[1], img.shape[0], 0, 0, 0]]).astype(np.float32)))
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[test_op])
 
     unaugSamp, augSamp = [], []
@@ -184,10 +184,10 @@ def test_random_horizontal_flip_with_bbox_invalid_prob_c():
         # Note: Valid range of prob should be [0.0, 1.0]
         test_op = c_vision.RandomHorizontalFlipWithBBox(1.5)
         # map to apply ops
-        dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+        dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                                output_columns=["image", "annotation"],
+                                output_columns=["image", "bbox"],
-                                columns_order=["image", "annotation"],
+                                columns_order=["image", "bbox"],
-                                operations=[test_op])  # Add column for "annotation"
+                                operations=[test_op])  # Add column for "bbox"
     except ValueError as error:
         logger.info("Got an exception in DE: {}".format(str(error)))
         assert "Input prob is not within the required interval of (0.0 to 1.0)." in str(error)

tests/ut/python/dataset/test_random_resize_with_bbox.py

@@ -48,9 +48,9 @@ def test_random_resize_with_bbox_op_voc_c(plot_vis=False):
     test_op = c_vision.RandomResizeWithBBox(100)
 
     # map to apply ops
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[test_op])
 
     filename = "random_resize_with_bbox_op_01_c_voc_result.npz"
@@ -129,15 +129,15 @@ def test_random_resize_with_bbox_op_edge_c(plot_vis=False):
     test_op = c_vision.RandomResizeWithBBox(500)
 
     # maps to convert data into valid edge case data
-    dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
+    dataVoc1 = dataVoc1.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[lambda img, bboxes: (
                                 img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
 
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[lambda img, bboxes: (
                                 img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
 

tests/ut/python/dataset/test_random_vertical_flip_with_bbox.py

@@ -46,9 +46,9 @@ def test_random_vertical_flip_with_bbox_op_c(plot_vis=False):
     test_op = c_vision.RandomVerticalFlipWithBBox(1)
 
     # map to apply ops
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[test_op])
 
     unaugSamp, augSamp = [], []
@@ -111,9 +111,9 @@ def test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=False):
     test_op = c_vision.RandomVerticalFlipWithBBox(0.8)
 
     # map to apply ops
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[test_op])
 
     filename = "random_vertical_flip_with_bbox_01_c_result.npz"
@@ -148,15 +148,15 @@ def test_random_vertical_flip_with_bbox_op_edge_c(plot_vis=False):
     test_op = c_vision.RandomVerticalFlipWithBBox(1)
 
     # maps to convert data into valid edge case data
-    dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
+    dataVoc1 = dataVoc1.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
 
     # Test Op added to list of Operations here
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
 
     unaugSamp, augSamp = [], []
@@ -181,9 +181,9 @@ def test_random_vertical_flip_with_bbox_op_invalid_c():
         test_op = c_vision.RandomVerticalFlipWithBBox(2)
 
         # map to apply ops
-        dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+        dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                                output_columns=["image", "annotation"],
+                                output_columns=["image", "bbox"],
-                                columns_order=["image", "annotation"],
+                                columns_order=["image", "bbox"],
                                 operations=[test_op])
 
         for _ in dataVoc2.create_dict_iterator():

tests/ut/python/dataset/test_resize_with_bbox.py

@@ -48,9 +48,9 @@ def test_resize_with_bbox_op_voc_c(plot_vis=False):
     test_op = c_vision.ResizeWithBBox(100)
 
     # map to apply ops
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[test_op])
 
     filename = "resize_with_bbox_op_01_c_voc_result.npz"
@@ -119,15 +119,15 @@ def test_resize_with_bbox_op_edge_c(plot_vis=False):
     test_op = c_vision.ResizeWithBBox(500)
 
     # maps to convert data into valid edge case data
-    dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
+    dataVoc1 = dataVoc1.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[lambda img, bboxes: (
                                 img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
 
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
-                            output_columns=["image", "annotation"],
+                            output_columns=["image", "bbox"],
-                            columns_order=["image", "annotation"],
+                            columns_order=["image", "bbox"],
                             operations=[lambda img, bboxes: (
                                 img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
 

tests/ut/python/dataset/util.py

@@ -252,13 +252,13 @@ def visualize_image(image_original, image_de, mse=None, image_lib=None):
     plt.show()
 
 
-def visualize_with_bounding_boxes(orig, aug, annot_name="annotation", plot_rows=3):
+def visualize_with_bounding_boxes(orig, aug, annot_name="bbox", plot_rows=3):
     """
-    Take a list of un-augmented and augmented images with "annotation" bounding boxes
+    Take a list of un-augmented and augmented images with "bbox" bounding boxes
     Plot images to compare test correct BBox augment functionality
     :param orig: list of original images and bboxes (without aug)
     :param aug: list of augmented images and bboxes
-    :param annot_name: the dict key for bboxes in data, e.g "bbox" (COCO) / "annotation" (VOC)
+    :param annot_name: the dict key for bboxes in data, e.g "bbox" (COCO) / "bbox" (VOC)
     :param plot_rows: number of rows on plot (rows = samples on one plot)
     :return: None
     """
@@ -337,7 +337,7 @@ def check_bad_bbox(data, test_op, invalid_bbox_type, expected_error):
     :return: None
     """
 
-    def add_bad_annotation(img, bboxes, invalid_bbox_type_):
+    def add_bad_bbox(img, bboxes, invalid_bbox_type_):
         """
         Used to generate erroneous bounding box examples on given img.
         :param img: image where the bounding boxes are.
@@ -366,15 +366,15 @@ def check_bad_bbox(data, test_op, invalid_bbox_type, expected_error):
 
     try:
         # map to use selected invalid bounding box type
-        data = data.map(input_columns=["image", "annotation"],
+        data = data.map(input_columns=["image", "bbox"],
-                        output_columns=["image", "annotation"],
+                        output_columns=["image", "bbox"],
-                        columns_order=["image", "annotation"],
+                        columns_order=["image", "bbox"],
-                        operations=lambda img, bboxes: add_bad_annotation(img, bboxes, invalid_bbox_type))
+                        operations=lambda img, bboxes: add_bad_bbox(img, bboxes, invalid_bbox_type))
         # map to apply ops
-        data = data.map(input_columns=["image", "annotation"],
+        data = data.map(input_columns=["image", "bbox"],
-                        output_columns=["image", "annotation"],
+                        output_columns=["image", "bbox"],
-                        columns_order=["image", "annotation"],
+                        columns_order=["image", "bbox"],
-                        operations=[test_op])  # Add column for "annotation"
+                        operations=[test_op])  # Add column for "bbox"
         for _, _ in enumerate(data.create_dict_iterator()):
             break
     except RuntimeError as error: