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))
data_voc2 = ds.VOCDataset(DATA_DIR_VOC,
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_VOC,
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_VOC,
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_VOC,
task="Detection",
mode="train",
decode=True,
shuffle=False)
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape,
"4 features")
def test_c_random_resized_crop_with_bbox_op_invalid2():
"""
Prints images side by side with and without Aug applied + bboxes to compare and test
"""
# 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)
try:
# 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)
# 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
except ValueError as err:
logger.info("Got an exception in DE: {}".format(str(err)))
assert "Input range is not valid" in str(err)
def test_random_resized_crop_with_bbox_op_invalid2_c():
"""
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_VOC,
task="Detection",
mode="train",
decode=True,
shuffle=False)
try:
# 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))
# 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
except ValueError as err:
logger.info("Got an exception in DE: {}".format(str(err)))
assert "Input is not within the required interval of (0 to 16777216)." in str(
err)
def test_random_resized_crop_with_bbox_op_edge_c(plot_vis=False):
"""
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_VOC,
task="Detection",
mode="train",
decode=True,
shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC,
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 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()):
unaugSamp.append(unAug)
augSamp.append(Aug)
if plot_vis:
visualize_with_bounding_boxes(unaugSamp, augSamp)
def test_c_random_resized_crop_with_bbox_op_edge(plot_vis=False):
"""
Prints images side by side with and without Aug applied + bboxes to compare and test
"""
# 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)
dataVoc2 = dataVoc2.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
# Modify BBoxes to serve as valid edge cases
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[gen_bbox_edge])
# map to apply ops
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[test_op
]) # Add column for "annotation"
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(unaugSamp, augSamp)
def test_random_resized_crop_with_bbox_op_c(plot_vis=False):
"""
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_VOC,
task="Detection",
mode="train",
decode=True,
shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC,
task="Detection",
mode="train",
decode=True,
shuffle=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"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[test_op])
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()):
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_resized_crop_with_bbox_op_coco_c(plot_vis=False):
"""
Prints images and bboxes side by side with and without RandomResizedCropWithBBox Op applied,
Testing with Coco dataset
"""
logger.info("test_random_resized_crop_with_bbox_op_coco_c")
# load dataset
dataCoco1 = ds.CocoDataset(DATA_DIR_COCO[0],
annotation_file=DATA_DIR_COCO[1],
task="Detection",
decode=True,
shuffle=False)
dataCoco2 = ds.CocoDataset(DATA_DIR_COCO[0],
annotation_file=DATA_DIR_COCO[1],
task="Detection",
decode=True,
shuffle=False)
test_op = c_vision.RandomResizedCropWithBBox((512, 512), (0.5, 1),
(0.5, 1))
dataCoco2 = dataCoco2.map(input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
columns_order=["image", "bbox"],
operations=[test_op])
unaugSamp, augSamp = [], []
for unAug, Aug in zip(dataCoco1.create_dict_iterator(),
dataCoco2.create_dict_iterator()):
unaugSamp.append(unAug)
augSamp.append(Aug)
if plot_vis:
visualize_with_bounding_boxes(unaugSamp, augSamp, "bbox")