def test_random_crop_with_bbox_op_bad_padding():
"""
Test RandomCropWithBBox Op on invalid constructor parameters, expected to raise ValueError
"""
logger.info("test_random_crop_with_bbox_op_invalid_c")
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
try:
test_op = c_vision.RandomCropWithBBox([512, 512], padding=-1)
dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
columns_order=["image", "bbox"],
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 padding is not within the required interval of (0 to 2147483647)." in str(err)
try:
test_op = c_vision.RandomCropWithBBox([512, 512], padding=[16777216, 16777216, 16777216, 16777216])
dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
columns_order=["image", "bbox"],
operations=[test_op])
for _ in dataVoc2.create_dict_iterator():
break
except RuntimeError as err:
logger.info("Got an exception in DE: {}".format(str(err)))
assert "RandomCropBBoxOp padding size is too big, it\'s more than 3 times the original size." in str(err)
def c_random_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.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)
def c_random_crop_with_bbox_op_invalid():
"""
Checking for invalid params passed to Aug Constructor
"""
# Load dataset
dataVoc2 = ds.VOCDataset(DATA_DIR,
task="Detection",
mode="train",
decode=True,
shuffle=False)
try:
# 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)
# 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"
for _ in dataVoc2.create_dict_iterator():
break
except TypeError as err:
logger.info("Got an exception in DE: {}".format(str(err)))
assert "Size should be a single integer" in str(err)
def test_random_crop_with_bbox_op_coco_c(plot_vis=False):
"""
Prints images and bboxes side by side with and without RandomCropWithBBox Op applied,
Testing with Coco dataset
"""
logger.info("test_random_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.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
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")
def test_random_crop_with_bbox_op_c(plot_vis=False):
"""
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_VOC, task="Detection", mode="train", decode=True, shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
# define test OP with values to match existing Op UT
test_op = c_vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
# map to apply ops
dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
columns_order=["image", "bbox"],
operations=[test_op]) # Add column for "bbox"
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_random_crop_with_bbox_op_bad_c():
"""
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_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_random_crop_with_bbox_op_invalid_c():
"""
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_VOC,
task="Detection",
mode="train",
decode=True,
shuffle=False)
try:
# define test OP with values to match existing Op unit - test
test_op = c_vision.RandomCropWithBBox([512, 512, 375])
# map to apply ops
dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
columns_order=["image", "bbox"],
operations=[test_op]) # Add column for "bbox"
for _ in dataVoc2.create_dict_iterator():
break
except TypeError as err:
logger.info("Got an exception in DE: {}".format(str(err)))
assert "Size should be a single integer" in str(err)
def test_random_crop_with_bbox_op_edge_c(plot_vis=False):
"""
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_VOC,
task="Detection",
mode="train",
decode=True,
shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC,
task="Detection",
mode="train",
decode=True,
shuffle=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 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 c_random_crop_with_bbox_op_edge(plot_vis=False):
"""
Prints images side by side with and without Aug applied + bboxes
Testing for an Edge case
"""
# 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
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)
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_crop_with_bbox_op2_c(plot_vis=False):
"""
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_VOC,
task="Detection",
mode="train",
decode=True,
shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC,
task="Detection",
mode="train",
decode=True,
shuffle=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))
# map to apply ops
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[test_op])
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()):
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)
