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", usage="train", shuffle=False, decode=True)
try:
test_op = c_vision.RandomCropWithBBox([512, 512], padding=-1)
dataVoc2 = dataVoc2.map(operations=[test_op], input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
column_order=["image", "bbox"])
for _ in dataVoc2.create_dict_iterator(num_epochs=1):
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(operations=[test_op], input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
column_order=["image", "bbox"])
for _ in dataVoc2.create_dict_iterator(num_epochs=1):
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 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(operations=[test_op], input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
column_order=["image", "bbox"])
unaugSamp, augSamp = [], []
for unAug, Aug in zip(dataCoco1.create_dict_iterator(num_epochs=1, output_numpy=True),
dataCoco2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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", usage="train", shuffle=False, decode=True)
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", usage="train", shuffle=False, decode=True)
# 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(operations=[test_op], input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
column_order=["image", "bbox"]) # Add column for "bbox"
unaugSamp, augSamp = [], []
for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1, output_numpy=True),
dataVoc2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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",
usage="train",
shuffle=False,
decode=True)
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",
usage="train",
shuffle=False,
decode=True)
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",
usage="train",
shuffle=False,
decode=True)
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.NegativeXY,
"negative value")
data_voc2 = ds.VOCDataset(DATA_DIR_VOC,
task="Detection",
usage="train",
shuffle=False,
decode=True)
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",
usage="train",
shuffle=False,
decode=True)
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(operations=[test_op],
input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
column_order=["image",
"bbox"]) # Add column for "bbox"
for _ in dataVoc2.create_dict_iterator(num_epochs=1):
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",
usage="train",
shuffle=False,
decode=True)
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC,
task="Detection",
usage="train",
shuffle=False,
decode=True)
# 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(operations=[
lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]
).astype(bboxes.dtype))
],
input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
column_order=["image", "bbox"])
# Test Op added to list of Operations here
dataVoc2 = dataVoc2.map(operations=[
lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]
).astype(bboxes.dtype)), test_op
],
input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
column_order=["image", "bbox"])
unaugSamp, augSamp = [], []
for unAug, Aug in zip(
dataVoc1.create_dict_iterator(num_epochs=1, output_numpy=True),
dataVoc2.create_dict_iterator(num_epochs=1, output_numpy=True)):
unaugSamp.append(unAug)
augSamp.append(Aug)
if plot_vis:
visualize_with_bounding_boxes(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",
usage="train",
shuffle=False,
decode=True)
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC,
task="Detection",
usage="train",
shuffle=False,
decode=True)
# 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(operations=[test_op],
input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
column_order=["image", "bbox"])
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(num_epochs=1, output_numpy=True),
dataVoc2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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)
