updated UT test for Python (3) AugOps with BBox - MD5 checks + imrpv comments

mindspore/ccsrc/dataset/kernels/image/random_crop_and_resize_with_bbox_op.cc

@@ -30,8 +30,7 @@ Status RandomCropAndResizeWithBBoxOp::Compute(const TensorRow &input, TensorRow
   BOUNDING_BOX_CHECK(input);
   CHECK_FAIL_RETURN_UNEXPECTED(input[0]->shape().Size() >= 2, "The shape of input is abnormal");
 
-  (*output).push_back(nullptr);  // init memory for return vector
-  (*output).push_back(nullptr);
+  output->resize(2);
   (*output)[1] = std::move(input[1]);  // move boxes over to output
 
   size_t bboxCount = input[1]->shape()[0];  // number of rows in bbox tensor

mindspore/ccsrc/dataset/kernels/image/random_crop_with_bbox_op.cc

@@ -36,8 +36,7 @@ Status RandomCropWithBBoxOp::Compute(const TensorRow &input, TensorRow *output)
   int32_t padded_image_h;
   int32_t padded_image_w;
 
-  (*output).push_back(nullptr);
-  (*output).push_back(nullptr);
+  output->resize(2);
   (*output)[1] = std::move(input[1]);  // since some boxes may be removed
 
   bool crop_further = true;  // Whether further cropping will be required or not, true unless required size matches

mindspore/ccsrc/dataset/kernels/image/random_vertical_flip_with_bbox_op.cc

@@ -45,8 +45,7 @@ Status RandomVerticalFlipWithBBoxOp::Compute(const TensorRow &input, TensorRow *
       RETURN_IF_NOT_OK(input[1]->SetItemAt({i, 1}, newBoxCorner_y));
     }
 
-    (*output).push_back(nullptr);
-    (*output).push_back(nullptr);
+    output->resize(2);
     (*output)[1] = std::move(input[1]);
 
     return VerticalFlip(input[0], &(*output)[0]);

tests/ut/python/dataset/test_random_crop_and_resize_with_bbox.py

@@ -13,17 +13,17 @@
 # limitations under the License.
 # ==============================================================================
 """
-Testing RandomCropAndResizeWithBBox op
+Testing RandomCropAndResizeWithBBox op in DE
 """
 import numpy as np
-
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.c_transforms as c_vision
 
 from mindspore import log as logger
+from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox, \
+    config_get_set_seed, config_get_set_num_parallel_workers, save_and_check_md5
+
+GENERATE_GOLDEN = False
 
 # updated VOC dataset with correct annotations
 DATA_DIR = "../data/dataset/testVOC2012_2"
@@ -31,8 +31,7 @@ DATA_DIR = "../data/dataset/testVOC2012_2"
 
 def fix_annotate(bboxes):
     """
-    Update Current VOC dataset format to Proposed HQ BBox format
-
+    Fix annotations to format followed by mindspore.
     :param bboxes: in [label, x_min, y_min, w, h, truncate, difficult] format
     :return: annotation in [x_min, y_min, w, h, label, truncate, difficult] format
     """
@@ -46,112 +45,22 @@ def fix_annotate(bboxes):
     return bboxes
 
 
-def add_bounding_boxes(ax, bboxes):
-    for bbox in bboxes:
-        rect = patches.Rectangle((bbox[0], bbox[1]),
-                                 bbox[2], bbox[3],
-                                 linewidth=1, edgecolor='r', facecolor='none')
-        # Add the patch to the Axes
-        ax.add_patch(rect)
-
-
-def vis_check(orig, aug):
-    if not isinstance(orig, list) or not isinstance(aug, list):
-        return False
-    if len(orig) != len(aug):
-        return False
-    return True
-
-
-def visualize(orig, aug):
-
-    if not vis_check(orig, aug):
-        return
-
-    plotrows = 3
-    compset = int(len(orig)/plotrows)
-
-    orig, aug = np.array(orig), np.array(aug)
-
-    orig = np.split(orig[:compset*plotrows], compset) + [orig[compset*plotrows:]]
-    aug = np.split(aug[:compset*plotrows], compset) + [aug[compset*plotrows:]]
-
-    for ix, allData in enumerate(zip(orig, aug)):
-        base_ix = ix * plotrows  # will signal what base level we're on
-        fig, axs = plt.subplots(len(allData[0]), 2)
-        fig.tight_layout(pad=1.5)
-
-        for x, (dataA, dataB) in enumerate(zip(allData[0], allData[1])):
-            cur_ix = base_ix + x
-
-            axs[x, 0].imshow(dataA["image"])
-            add_bounding_boxes(axs[x, 0], dataA["annotation"])
-            axs[x, 0].title.set_text("Original" + str(cur_ix+1))
-            print("Original **\n ", str(cur_ix+1), " :", dataA["annotation"])
-
-            axs[x, 1].imshow(dataB["image"])
-            add_bounding_boxes(axs[x, 1], dataB["annotation"])
-            axs[x, 1].title.set_text("Augmented" + str(cur_ix+1))
-            print("Augmented **\n", str(cur_ix+1), " ", dataB["annotation"], "\n")
-
-        plt.show()
-
-# Functions to pass to Gen for creating invalid bounding boxes
-
-
-def gen_bad_bbox_neg_xy(im, bbox):
-    im_h, im_w = im.shape[0], im.shape[1]
-    bbox[0][:4] = [-50, -50, im_w - 10, im_h - 10]
-    return im, bbox
-
-
-def gen_bad_bbox_overflow_width(im, bbox):
-    im_h, im_w = im.shape[0], im.shape[1]
-    bbox[0][:4] = [0, 0, im_w + 10, im_h - 10]
-    return im, bbox
-
-
-def gen_bad_bbox_overflow_height(im, bbox):
-    im_h, im_w = im.shape[0], im.shape[1]
-    bbox[0][:4] = [0, 0, im_w - 10, im_h + 10]
-    return im, bbox
-
-
-def gen_bad_bbox_wrong_shape(im, bbox):
-    bbox = np.array([[0, 0, 0]]).astype(bbox.dtype)
-    return im, bbox
-
-
-badGenFuncs = [gen_bad_bbox_neg_xy,
-               gen_bad_bbox_overflow_width,
-               gen_bad_bbox_overflow_height,
-               gen_bad_bbox_wrong_shape]
-
-
-assertVal = ["min_x",
-             "is out of bounds of the image",
-             "is out of bounds of the image",
-             "4 features"]
-
-
-# Gen Edge case BBox
-def gen_bbox_edge(im, bbox):
-    im_h, im_w = im.shape[0], im.shape[1]
-    bbox[0][:4] = [0, 0, im_w, im_h]
-    return im, bbox
-
-
-def test_c_random_resized_crop_with_bbox_op(plot_vis=False):
+def test_random_resized_crop_with_bbox_op_c(plot_vis=False):
     """
-     Prints images side by side with and without Aug applied + bboxes to compare and test
+     Prints images and bboxes side by side with and without RandomResizedCropWithBBox Op applied,
+     tests with MD5 check, expected to pass
     """
+    logger.info("test_random_resized_crop_with_bbox_op_c")
+
+    original_seed = config_get_set_seed(23415)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+
     # Load dataset
     dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
     dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
 
     test_op = c_vision.RandomResizedCropWithBBox((256, 512), (0.5, 0.5), (0.5, 0.5))
 
-    # maps to fix annotations to HQ standard
     dataVoc1 = dataVoc1.map(input_columns=["annotation"],
                             output_columns=["annotation"],
                             operations=fix_annotate)
@@ -164,6 +73,9 @@ def test_c_random_resized_crop_with_bbox_op(plot_vis=False):
                             columns_order=["image", "annotation"],
                             operations=[test_op])  # Add column for "annotation"
 
+    filename = "random_resized_crop_with_bbox_01_c_result.npz"
+    save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
+
     unaugSamp, augSamp = [], []
 
     for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
@@ -171,20 +83,26 @@ def test_c_random_resized_crop_with_bbox_op(plot_vis=False):
         augSamp.append(Aug)
 
     if plot_vis:
-        visualize(unaugSamp, augSamp)
+        visualize_with_bounding_boxes(unaugSamp, augSamp)
+
+    # Restore config setting
+    ds.config.set_seed(original_seed)
+    ds.config.set_num_parallel_workers(original_num_parallel_workers)
 
 
-def test_c_random_resized_crop_with_bbox_op_edge(plot_vis=False):
+def test_random_resized_crop_with_bbox_op_edge_c(plot_vis=False):
     """
-     Prints images side by side with and without Aug applied + bboxes to compare and test
+     Prints images and bboxes side by side with and without RandomResizedCropWithBBox Op applied,
+     tests on dynamically generated edge case, expected to pass
     """
+    logger.info("test_random_resized_crop_with_bbox_op_edge_c")
+
     # Load dataset
     dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
     dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
 
     test_op = c_vision.RandomResizedCropWithBBox((256, 512), (0.5, 0.5), (0.5, 0.5))
 
-    # maps to fix annotations to HQ standard
     dataVoc1 = dataVoc1.map(input_columns=["annotation"],
                             output_columns=["annotation"],
                             operations=fix_annotate)
@@ -192,17 +110,17 @@ def test_c_random_resized_crop_with_bbox_op_edge(plot_vis=False):
                             output_columns=["annotation"],
                             operations=fix_annotate)
 
-    # Modify BBoxes to serve as valid edge cases
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    # maps to convert data into valid edge case data
+    dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
                             output_columns=["image", "annotation"],
                             columns_order=["image", "annotation"],
-                            operations=[gen_bbox_edge])
+                            operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
 
-    # map to apply ops
+    # Test Op added to list of Operations here
     dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
                             output_columns=["image", "annotation"],
                             columns_order=["image", "annotation"],
-                            operations=[test_op])  # Add column for "annotation"
+                            operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
 
     unaugSamp, augSamp = [], []
 
@@ -211,21 +129,22 @@ def test_c_random_resized_crop_with_bbox_op_edge(plot_vis=False):
         augSamp.append(Aug)
 
     if plot_vis:
-        visualize(unaugSamp, augSamp)
+        visualize_with_bounding_boxes(unaugSamp, augSamp)
 
 
-def test_c_random_resized_crop_with_bbox_op_invalid():
+def test_random_resized_crop_with_bbox_op_invalid_c():
     """
-     Prints images side by side with and without Aug applied + bboxes to compare and test
+     Tests RandomResizedCropWithBBox on invalid constructor parameters, expected to raise ValueError
     """
-    # Load dataset # only loading the to AugDataset as test will fail on this
+    logger.info("test_random_resized_crop_with_bbox_op_invalid_c")
+
+    # Load dataset, only Augmented Dataset as test will raise ValueError
     dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
 
     try:
         # If input range of scale is not in the order of (min, max), ValueError will be raised.
         test_op = c_vision.RandomResizedCropWithBBox((256, 512), (1, 0.5), (0.5, 0.5))
 
-        # maps to fix annotations to HQ standard
         dataVoc2 = dataVoc2.map(input_columns=["annotation"],
                                 output_columns=["annotation"],
                                 operations=fix_annotate)
@@ -243,10 +162,11 @@ def test_c_random_resized_crop_with_bbox_op_invalid():
         assert "Input range is not valid" in str(err)
 
 
-def test_c_random_resized_crop_with_bbox_op_invalid2():
+def test_random_resized_crop_with_bbox_op_invalid2_c():
     """
-     Prints images side by side with and without Aug applied + bboxes to compare and test
+     Tests RandomResizedCropWithBBox Op on invalid constructor parameters, expected to raise ValueError
     """
+    logger.info("test_random_resized_crop_with_bbox_op_invalid2_c")
     # Load dataset # only loading the to AugDataset as test will fail on this
     dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
 
@@ -254,7 +174,6 @@ def test_c_random_resized_crop_with_bbox_op_invalid2():
         # If input range of ratio is not in the order of (min, max), ValueError will be raised.
         test_op = c_vision.RandomResizedCropWithBBox((256, 512), (1, 1), (1, 0.5))
 
-        # maps to fix annotations to HQ standard
         dataVoc2 = dataVoc2.map(input_columns=["annotation"],
                                 output_columns=["annotation"],
                                 operations=fix_annotate)
@@ -272,41 +191,26 @@ def test_c_random_resized_crop_with_bbox_op_invalid2():
         assert "Input range is not valid" in str(err)
 
 
-def test_c_random_resized_crop_with_bbox_op_bad():
-    # Should Fail - Errors logged to logger
-    for ix, badFunc in enumerate(badGenFuncs):
-        try:
-            dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
-                                     decode=True, shuffle=False)
-
-            test_op = c_vision.RandomVerticalFlipWithBBox(1)
-
-            dataVoc2 = dataVoc2.map(input_columns=["annotation"],
-                                    output_columns=["annotation"],
-                                    operations=fix_annotate)
-
-            dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
-                                    output_columns=["image", "annotation"],
-                                    columns_order=["image", "annotation"],
-                                    operations=[badFunc])
-
-            # map to apply ops
-            dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
-                                    output_columns=["image", "annotation"],
-                                    columns_order=["image", "annotation"],
-                                    operations=[test_op])
-
-            for _ in dataVoc2.create_dict_iterator():
-                break  # first sample will cause exception
+def test_random_resized_crop_with_bbox_op_bad_c():
+    """
+    Test RandomCropWithBBox op with invalid bounding boxes, expected to catch multiple errors.
+    """
+    logger.info("test_random_resized_crop_with_bbox_op_bad_c")
+    test_op = c_vision.RandomResizedCropWithBBox((256, 512), (0.5, 0.5), (0.5, 0.5))
 
-        except RuntimeError as err:
-            logger.info("Got an exception in DE: {}".format(str(err)))
-            assert assertVal[ix] in str(err)
+    data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
+    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image")
+    data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
+    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image")
+    data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
+    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.NegativeXY, "min_x")
+    data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
+    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features")
 
 
 if __name__ == "__main__":
-    test_c_random_resized_crop_with_bbox_op(plot_vis=True)
-    test_c_random_resized_crop_with_bbox_op_edge(plot_vis=True)
-    test_c_random_resized_crop_with_bbox_op_invalid()
-    test_c_random_resized_crop_with_bbox_op_invalid2()
-    test_c_random_resized_crop_with_bbox_op_bad()
+    test_random_resized_crop_with_bbox_op_c(plot_vis=True)
+    test_random_resized_crop_with_bbox_op_edge_c(plot_vis=True)
+    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

@@ -13,18 +13,18 @@
 # limitations under the License.
 # ==============================================================================
 """
-Testing RandomCropWithBBox op
+Testing RandomCropWithBBox op in DE
 """
-
 import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.c_transforms as c_vision
 import mindspore.dataset.transforms.vision.utils as mode
 
 from mindspore import log as logger
+from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox, \
+    config_get_set_seed, config_get_set_num_parallel_workers, save_and_check_md5
+
+GENERATE_GOLDEN = False
 
 # updated VOC dataset with correct annotations
 DATA_DIR = "../data/dataset/testVOC2012_2"
@@ -32,8 +32,7 @@ DATA_DIR = "../data/dataset/testVOC2012_2"
 
 def fix_annotate(bboxes):
     """
-    Update Current VOC dataset format to Proposed HQ BBox format
-
+    Fix annotations to format followed by mindspore.
     :param bboxes: in [label, x_min, y_min, w, h, truncate, difficult] format
     :return: annotation in [x_min, y_min, w, h, label, truncate, difficult] format
     """
@@ -47,113 +46,19 @@ def fix_annotate(bboxes):
     return bboxes
 
 
-def add_bounding_boxes(ax, bboxes):
-    for bbox in bboxes:
-        rect = patches.Rectangle((bbox[0], bbox[1]),
-                                 bbox[2], bbox[3],
-                                 linewidth=1, edgecolor='r', facecolor='none')
-        # Add the patch to the Axes
-        ax.add_patch(rect)
-
-
-def vis_check(orig, aug):
-    if not isinstance(orig, list) or not isinstance(aug, list):
-        return False
-    if len(orig) != len(aug):
-        return False
-    return True
-
-
-def visualize(orig, aug):
-
-    if not vis_check(orig, aug):
-        return
-
-    plotrows = 3
-    compset = int(len(orig)/plotrows)
-
-    orig, aug = np.array(orig), np.array(aug)
-
-    orig = np.split(orig[:compset*plotrows], compset) + [orig[compset*plotrows:]]
-    aug = np.split(aug[:compset*plotrows], compset) + [aug[compset*plotrows:]]
-
-    for ix, allData in enumerate(zip(orig, aug)):
-        base_ix = ix * plotrows  # will signal what base level we're on
-        fig, axs = plt.subplots(len(allData[0]), 2)
-        fig.tight_layout(pad=1.5)
-
-        for x, (dataA, dataB) in enumerate(zip(allData[0], allData[1])):
-            cur_ix = base_ix + x
-
-            axs[x, 0].imshow(dataA["image"])
-            add_bounding_boxes(axs[x, 0], dataA["annotation"])
-            axs[x, 0].title.set_text("Original" + str(cur_ix+1))
-            print("Original **\n ", str(cur_ix+1), " :", dataA["annotation"])
-
-            axs[x, 1].imshow(dataB["image"])
-            add_bounding_boxes(axs[x, 1], dataB["annotation"])
-            axs[x, 1].title.set_text("Augmented" + str(cur_ix+1))
-            print("Augmented **\n", str(cur_ix+1), " ", dataB["annotation"], "\n")
-
-        plt.show()
-
-# Functions to pass to Gen for creating invalid bounding boxes
-
-
-def gen_bad_bbox_neg_xy(im, bbox):
-    im_h, im_w = im.shape[0], im.shape[1]
-    bbox[0][:4] = [-50, -50, im_w - 10, im_h - 10]
-    return im, bbox
-
-
-def gen_bad_bbox_overflow_width(im, bbox):
-    im_h, im_w = im.shape[0], im.shape[1]
-    bbox[0][:4] = [0, 0, im_w + 10, im_h - 10]
-    return im, bbox
-
-
-def gen_bad_bbox_overflow_height(im, bbox):
-    im_h, im_w = im.shape[0], im.shape[1]
-    bbox[0][:4] = [0, 0, im_w - 10, im_h + 10]
-    return im, bbox
-
-
-def gen_bad_bbox_wrong_shape(im, bbox):
-    bbox = np.array([[0, 0, 0]]).astype(bbox.dtype)
-    return im, bbox
-
-
-badGenFuncs = [gen_bad_bbox_neg_xy,
-               gen_bad_bbox_overflow_width,
-               gen_bad_bbox_overflow_height,
-               gen_bad_bbox_wrong_shape]
-
-
-assertVal = ["min_x",
-             "is out of bounds of the image",
-             "is out of bounds of the image",
-             "4 features"]
-
-
-# Gen Edge case BBox
-def gen_bbox_edge(im, bbox):
-    im_h, im_w = im.shape[0], im.shape[1]
-    bbox[0][:4] = [0, 0, im_w, im_h]
-    return im, bbox
-
-
 def test_random_crop_with_bbox_op_c(plot_vis=False):
     """
-     Prints images side by side with and without Aug applied + bboxes
+     Prints images and bboxes side by side with and without RandomCropWithBBox Op applied
     """
+    logger.info("test_random_crop_with_bbox_op_c")
+
     # Load dataset
     dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
     dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
 
-    # define test OP with values to match existing Op unit - test
+    # define test OP with values to match existing Op UT
     test_op = c_vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
 
-    # maps to fix annotations to HQ standard
     dataVoc1 = dataVoc1.map(input_columns=["annotation"],
                             output_columns=["annotation"],
                             operations=fix_annotate)
@@ -173,14 +78,17 @@ def test_random_crop_with_bbox_op_c(plot_vis=False):
         augSamp.append(Aug)
 
     if plot_vis:
-        visualize(unaugSamp, augSamp)
+        visualize_with_bounding_boxes(unaugSamp, augSamp)
 
 
 def test_random_crop_with_bbox_op2_c(plot_vis=False):
     """
-     Prints images side by side with and without Aug applied + bboxes
-     With Fill Value
+     Prints images and bboxes side by side with and without RandomCropWithBBox Op applied,
+     with md5 check, expected to pass
     """
+    logger.info("test_random_crop_with_bbox_op2_c")
+    original_seed = config_get_set_seed(593447)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
 
     # Load dataset
     dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
@@ -189,7 +97,6 @@ def test_random_crop_with_bbox_op2_c(plot_vis=False):
     # define test OP with values to match existing Op unit - test
     test_op = c_vision.RandomCropWithBBox(512, [200, 200, 200, 200], fill_value=(255, 255, 255))
 
-    # maps to fix annotations to HQ standard
     dataVoc1 = dataVoc1.map(input_columns=["annotation"],
                             output_columns=["annotation"],
                             operations=fix_annotate)
@@ -202,6 +109,9 @@ def test_random_crop_with_bbox_op2_c(plot_vis=False):
                             columns_order=["image", "annotation"],
                             operations=[test_op])  # Add column for "annotation"
 
+    filename = "random_crop_with_bbox_01_c_result.npz"
+    save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
+
     unaugSamp, augSamp = [], []
 
     for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
@@ -209,14 +119,20 @@ def test_random_crop_with_bbox_op2_c(plot_vis=False):
         augSamp.append(Aug)
 
     if plot_vis:
-        visualize(unaugSamp, augSamp)
+        visualize_with_bounding_boxes(unaugSamp, augSamp)
+
+    # Restore config setting
+    ds.config.set_seed(original_seed)
+    ds.config.set_num_parallel_workers(original_num_parallel_workers)
 
 
 def test_random_crop_with_bbox_op3_c(plot_vis=False):
     """
-     Prints images side by side with and without Aug applied + bboxes
-     With Padding Mode passed
+     Prints images and bboxes side by side with and without RandomCropWithBBox Op applied,
+     with Padding Mode explicitly passed
     """
+    logger.info("test_random_crop_with_bbox_op3_c")
+
     # Load dataset
     dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
     dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
@@ -224,7 +140,6 @@ def test_random_crop_with_bbox_op3_c(plot_vis=False):
     # define test OP with values to match existing Op unit - test
     test_op = c_vision.RandomCropWithBBox(512, [200, 200, 200, 200], padding_mode=mode.Border.EDGE)
 
-    # maps to fix annotations to HQ standard
     dataVoc1 = dataVoc1.map(input_columns=["annotation"],
                             output_columns=["annotation"],
                             operations=fix_annotate)
@@ -244,14 +159,16 @@ def test_random_crop_with_bbox_op3_c(plot_vis=False):
         augSamp.append(Aug)
 
     if plot_vis:
-        visualize(unaugSamp, augSamp)
+        visualize_with_bounding_boxes(unaugSamp, augSamp)
 
 
 def test_random_crop_with_bbox_op_edge_c(plot_vis=False):
     """
-     Prints images side by side with and without Aug applied + bboxes
-     Testing for an Edge case
+     Prints images and bboxes side by side with and without RandomCropWithBBox Op applied,
+     applied on dynamically generated edge case, expected to pass
     """
+    logger.info("test_random_crop_with_bbox_op_edge_c")
+
     # Load dataset
     dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
     dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
@@ -259,7 +176,6 @@ def test_random_crop_with_bbox_op_edge_c(plot_vis=False):
     # define test OP with values to match existing Op unit - test
     test_op = c_vision.RandomCropWithBBox(512, [200, 200, 200, 200], padding_mode=mode.Border.EDGE)
 
-    # maps to fix annotations to HQ standard
     dataVoc1 = dataVoc1.map(input_columns=["annotation"],
                             output_columns=["annotation"],
                             operations=fix_annotate)
@@ -267,17 +183,17 @@ def test_random_crop_with_bbox_op_edge_c(plot_vis=False):
                             output_columns=["annotation"],
                             operations=fix_annotate)
 
-    # Modify BBoxes to serve as valid edge cases
-    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+    # maps to convert data into valid edge case data
+    dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
                             output_columns=["image", "annotation"],
                             columns_order=["image", "annotation"],
-                            operations=[gen_bbox_edge])
+                            operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
 
-    # map to apply ops
+    # Test Op added to list of Operations here
     dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
                             output_columns=["image", "annotation"],
                             columns_order=["image", "annotation"],
-                            operations=[test_op])  # Add column for "annotation"
+                            operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
 
     unaugSamp, augSamp = [], []
 
@@ -286,13 +202,15 @@ def test_random_crop_with_bbox_op_edge_c(plot_vis=False):
         augSamp.append(Aug)
 
     if plot_vis:
-        visualize(unaugSamp, augSamp)
+        visualize_with_bounding_boxes(unaugSamp, augSamp)
 
 
 def test_random_crop_with_bbox_op_invalid_c():
     """
-    Checking for invalid params passed to Aug Constructor
+    Test RandomCropWithBBox Op on invalid constructor parameters, expected to raise ValueError
     """
+    logger.info("test_random_crop_with_bbox_op_invalid_c")
+
     # Load dataset
     dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
 
@@ -300,8 +218,6 @@ def test_random_crop_with_bbox_op_invalid_c():
         # define test OP with values to match existing Op unit - test
         test_op = c_vision.RandomCropWithBBox([512, 512, 375])
 
-        # maps to fix annotations to HQ standard
-
         dataVoc2 = dataVoc2.map(input_columns=["annotation"],
                                 output_columns=["annotation"],
                                 operations=fix_annotate)
@@ -320,35 +236,20 @@ def test_random_crop_with_bbox_op_invalid_c():
 
 
 def test_random_crop_with_bbox_op_bad_c():
-    # Should Fail - Errors logged to logger
-    for ix, badFunc in enumerate(badGenFuncs):
-        try:
-            dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
-                                     decode=True, shuffle=False)
-
-            test_op = c_vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
-
-            dataVoc2 = dataVoc2.map(input_columns=["annotation"],
-                                    output_columns=["annotation"],
-                                    operations=fix_annotate)
-
-            dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
-                                    output_columns=["image", "annotation"],
-                                    columns_order=["image", "annotation"],
-                                    operations=[badFunc])
-
-            # map to apply ops
-            dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
-                                    output_columns=["image", "annotation"],
-                                    columns_order=["image", "annotation"],
-                                    operations=[test_op])
-
-            for _ in dataVoc2.create_dict_iterator():
-                break  # first sample will cause exception
-
-        except RuntimeError as err:
-            logger.info("Got an exception in DE: {}".format(str(err)))
-            assert assertVal[ix] in str(err)
+    """
+    Tests RandomCropWithBBox Op with invalid bounding boxes, expected to catch multiple errors.
+    """
+    logger.info("test_random_crop_with_bbox_op_bad_c")
+    test_op = c_vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
+
+    data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
+    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image")
+    data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
+    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image")
+    data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
+    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.NegativeXY, "min_x")
+    data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
+    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features")
 
 
 if __name__ == "__main__":

tests/ut/python/dataset/test_random_vertical_flip_with_bbox.py

@@ -13,14 +13,17 @@
 # limitations under the License.
 # ==============================================================================
 """
-Testing RandomVerticalFlipWithBBox op
+Testing RandomVerticalFlipWithBBox op in DE
 """
-
+import numpy as np
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.c_transforms as c_vision
 
 from mindspore import log as logger
-from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox
+from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox, \
+    config_get_set_seed, config_get_set_num_parallel_workers, save_and_check_md5
+
+GENERATE_GOLDEN = False
 
 # updated VOC dataset with correct annotations
 DATA_DIR = "../data/dataset/testVOC2012_2"
@@ -28,10 +31,9 @@ DATA_DIR = "../data/dataset/testVOC2012_2"
 
 def fix_annotate(bboxes):
     """
-    Update Current VOC dataset format to Proposed HQ BBox format
-
-    :param bboxes: as [label, x_min, y_min, w, h, truncate, difficult]
-    :return: annotation as [x_min, y_min, w, h, label, truncate, difficult]
+    Fix annotations to format followed by mindspore.
+    :param bboxes: in [label, x_min, y_min, w, h, truncate, difficult] format
+    :return: annotation in [x_min, y_min, w, h, label, truncate, difficult] format
     """
     for bbox in bboxes:
         tmp = bbox[0]
@@ -45,9 +47,9 @@ def fix_annotate(bboxes):
 
 def test_random_vertical_flip_with_bbox_op_c(plot_vis=False):
     """
-     Prints images side by side with and without Aug applied + bboxes to
-     compare and test
+     Prints images and bboxes side by side with and without RandomVerticalFlipWithBBox Op applied
     """
+    logger.info("test_random_vertical_flip_with_bbox_op_c")
     # Load dataset
     dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
                              decode=True, shuffle=False)
@@ -57,7 +59,6 @@ def test_random_vertical_flip_with_bbox_op_c(plot_vis=False):
 
     test_op = c_vision.RandomVerticalFlipWithBBox(1)
 
-    # maps to fix annotations to HQ standard
     dataVoc1 = dataVoc1.map(input_columns=["annotation"],
                             output_columns=["annotation"],
                             operations=fix_annotate)
@@ -82,9 +83,12 @@ def test_random_vertical_flip_with_bbox_op_c(plot_vis=False):
 
 def test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=False):
     """
-     Prints images side by side with and without Aug applied + bboxes to
-     compare and test
+     Prints images and bboxes side by side with and without RandomVerticalFlipWithBBox Op applied,
+     tests with MD5 check, expected to pass
     """
+    logger.info("test_random_vertical_flip_with_bbox_op_rand_c")
+    original_seed = config_get_set_seed(29847)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
 
     # Load dataset
     dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
@@ -93,9 +97,8 @@ def test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=False):
     dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
                              decode=True, shuffle=False)
 
-    test_op = c_vision.RandomVerticalFlipWithBBox(0.6)
+    test_op = c_vision.RandomVerticalFlipWithBBox(0.8)
 
-    # maps to fix annotations to HQ standard
     dataVoc1 = dataVoc1.map(input_columns=["annotation"],
                             output_columns=["annotation"],
                             operations=fix_annotate)
@@ -108,6 +111,56 @@ def test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=False):
                             columns_order=["image", "annotation"],
                             operations=[test_op])
 
+    filename = "random_vertical_flip_with_bbox_01_c_result.npz"
+    save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
+
+    unaugSamp, augSamp = [], []
+
+    for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
+        unaugSamp.append(unAug)
+        augSamp.append(Aug)
+
+    if plot_vis:
+        visualize_with_bounding_boxes(unaugSamp, augSamp)
+
+    # Restore config setting
+    ds.config.set_seed(original_seed)
+    ds.config.set_num_parallel_workers(original_num_parallel_workers)
+
+
+def test_random_vertical_flip_with_bbox_op_edge_c(plot_vis=False):
+    """
+     Prints images and bboxes side by side with and without RandomVerticalFlipWithBBox Op applied,
+    applied on dynamically generated edge case, expected to pass
+    """
+    logger.info("test_random_vertical_flip_with_bbox_op_edge_c")
+    dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
+                             decode=True, shuffle=False)
+
+    dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
+                             decode=True, shuffle=False)
+
+    test_op = c_vision.RandomVerticalFlipWithBBox(1)
+
+    dataVoc1 = dataVoc1.map(input_columns=["annotation"],
+                            output_columns=["annotation"],
+                            operations=fix_annotate)
+    dataVoc2 = dataVoc2.map(input_columns=["annotation"],
+                            output_columns=["annotation"],
+                            operations=fix_annotate)
+
+    # maps to convert data into valid edge case data
+    dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
+                            output_columns=["image", "annotation"],
+                            columns_order=["image", "annotation"],
+                            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"],
+                            output_columns=["image", "annotation"],
+                            columns_order=["image", "annotation"],
+                            operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
+
     unaugSamp, augSamp = [], []
 
     for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
@@ -119,16 +172,15 @@ def test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=False):
 
 
 def test_random_vertical_flip_with_bbox_op_invalid_c():
-    # Should Fail
-    # Load dataset
+    """
+     Test RandomVerticalFlipWithBBox Op on invalid constructor parameters, expected to raise ValueError
+    """
+    logger.info("test_random_vertical_flip_with_bbox_op_invalid_c")
     dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
                              decode=True, shuffle=False)
 
     try:
         test_op = c_vision.RandomVerticalFlipWithBBox(2)
-
-        # maps to fix annotations to HQ standard
-
         dataVoc2 = dataVoc2.map(input_columns=["annotation"],
                                 output_columns=["annotation"],
                                 operations=fix_annotate)
@@ -148,9 +200,9 @@ def test_random_vertical_flip_with_bbox_op_invalid_c():
 
 def test_random_vertical_flip_with_bbox_op_bad_c():
     """
-    Test RandomHorizontalFlipWithBBox op with invalid bounding boxes
+    Tests RandomVerticalFlipWithBBox Op with invalid bounding boxes, expected to catch multiple errors
     """
-    logger.info("test_random_horizontal_bbox_invalid_bounds_c")
+    logger.info("test_random_vertical_flip_with_bbox_op_bad_c")
     test_op = c_vision.RandomVerticalFlipWithBBox(1)
 
     data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
@@ -166,5 +218,6 @@ def test_random_vertical_flip_with_bbox_op_bad_c():
 if __name__ == "__main__":
     test_random_vertical_flip_with_bbox_op_c(plot_vis=True)
     test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=True)
+    test_random_vertical_flip_with_bbox_op_edge_c(plot_vis=True)
     test_random_vertical_flip_with_bbox_op_invalid_c()
     test_random_vertical_flip_with_bbox_op_bad_c()

tests/ut/python/dataset/util.py

@@ -312,34 +312,39 @@ def visualize_with_bounding_boxes(orig, aug, plot_rows=3):
     if len(orig) != len(aug) or not orig:
         return
 
-    comp_set = int(len(orig)/plot_rows)
+    batch_size = int(len(orig)/plot_rows)  # creates batches of images to plot together
+    split_point = batch_size * plot_rows
+
     orig, aug = np.array(orig), np.array(aug)
 
     if len(orig) > plot_rows:
-        orig = np.split(orig[:comp_set*plot_rows], comp_set) + [orig[comp_set*plot_rows:]]
-        aug = np.split(aug[:comp_set*plot_rows], comp_set) + [aug[comp_set*plot_rows:]]
+        # Create batches of required size and add remainder to last batch
+        orig = np.split(orig[:split_point], batch_size) + ([orig[split_point:]] if (split_point < orig.shape[0]) else [])  # check to avoid empty arrays being added
+        aug = np.split(aug[:split_point], batch_size) + ([aug[split_point:]] if (split_point < aug.shape[0]) else [])
     else:
         orig = [orig]
         aug = [aug]
 
     for ix, allData in enumerate(zip(orig, aug)):
-        base_ix = ix * plot_rows  # will signal what base level we're on
+        base_ix = ix * plot_rows  # current batch starting index
+        curPlot = len(allData[0])
 
-        sub_plot_count = 2 if (len(allData[0]) < 2) else len(allData[0])  # if 1 image remains, create subplot for 2 to simplify axis selection
-        fig, axs = plt.subplots(sub_plot_count, 2)
+        fig, axs = plt.subplots(curPlot, 2)
         fig.tight_layout(pad=1.5)
 
         for x, (dataA, dataB) in enumerate(zip(allData[0], allData[1])):
             cur_ix = base_ix + x
+            (axA, axB) = (axs[x, 0], axs[x, 1]) if (curPlot > 1) else (axs[0], axs[1])  # select plotting axes based on number of image rows on plot - else case when 1 row
 
-            axs[x, 0].imshow(dataA["image"])
-            add_bounding_boxes(axs[x, 0], dataA["annotation"])
-            axs[x, 0].title.set_text("Original" + str(cur_ix+1))
-            logger.info("Original **\n{} : {}".format(str(cur_ix+1), dataA["annotation"]))
+            axA.imshow(dataA["image"])
+            add_bounding_boxes(axA, dataA["annotation"])
+            axA.title.set_text("Original" + str(cur_ix+1))
 
-            axs[x, 1].imshow(dataB["image"])
-            add_bounding_boxes(axs[x, 1], dataB["annotation"])
-            axs[x, 1].title.set_text("Augmented" + str(cur_ix+1))
+            axB.imshow(dataB["image"])
+            add_bounding_boxes(axB, dataB["annotation"])
+            axB.title.set_text("Augmented" + str(cur_ix+1))
+
+            logger.info("Original **\n{} : {}".format(str(cur_ix+1), dataA["annotation"]))
             logger.info("Augmented **\n{} : {}\n".format(str(cur_ix+1), dataB["annotation"]))
 
         plt.show()