def test_cpp_uniform_augment(plot=False, num_ops=2):
"""
Test UniformAugment
"""
logger.info("Test CPP UniformAugment")
# Original Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = [C.Decode(), C.Resize(size=[224, 224]),
F.ToTensor()]
ds_original = data_set.map(operations=transforms_original, input_columns="image")
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image.asnumpy(), (0, 2, 3, 1)),
axis=0)
# UniformAugment Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
C.RandomHorizontalFlip(),
C.RandomVerticalFlip(),
C.RandomColorAdjust(),
C.RandomRotation(degrees=45)]
uni_aug = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
transforms_all = [C.Decode(), C.Resize(size=[224, 224]),
uni_aug,
F.ToTensor()]
ds_ua = data_set.map(operations=transforms_all, input_columns="image", num_parallel_workers=1)
ds_ua = ds_ua.batch(512)
for idx, (image, _) in enumerate(ds_ua):
if idx == 0:
images_ua = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_ua = np.append(images_ua,
np.transpose(image.asnumpy(), (0, 2, 3, 1)),
axis=0)
if plot:
visualize_list(images_original, images_ua)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_ua[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
def test_rotation_diff(plot=False):
"""
Test RandomRotation op
"""
logger.info("test_random_rotation_op")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
decode_op = c_vision.Decode()
rotation_op = c_vision.RandomRotation((45, 45))
ctrans = [decode_op, rotation_op]
data1 = data1.map(operations=ctrans, input_columns=["image"])
# Second dataset
transforms = [
py_vision.Decode(),
py_vision.RandomRotation((45, 45)),
py_vision.ToTensor(),
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = data2.map(operations=transform, input_columns=["image"])
num_iter = 0
image_list_c, image_list_py = [], []
for item1, item2 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
num_iter += 1
c_image = item1["image"]
py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_list_c.append(c_image)
image_list_py.append(py_image)
logger.info("shape of c_image: {}".format(c_image.shape))
logger.info("shape of py_image: {}".format(py_image.shape))
logger.info("dtype of c_image: {}".format(c_image.dtype))
logger.info("dtype of py_image: {}".format(py_image.dtype))
mse = diff_mse(c_image, py_image)
assert mse < 0.001 # Rounding error
if plot:
visualize_list(image_list_c, image_list_py, visualize_mode=2)
def test_bounding_box_augment_with_rotation_op(plot_vis=False):
"""
Test BoundingBoxAugment op (passing rotation op as transform)
Prints images side by side with and without Aug applied + bboxes to compare and test
"""
logger.info("test_bounding_box_augment_with_rotation_op")
original_seed = config_get_set_seed(0)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
dataVoc1 = ds.VOCDataset(DATA_DIR,
task="Detection",
usage="train",
shuffle=False,
decode=True)
dataVoc2 = ds.VOCDataset(DATA_DIR,
task="Detection",
usage="train",
shuffle=False,
decode=True)
# Ratio is set to 1 to apply rotation on all bounding boxes.
test_op = c_vision.BoundingBoxAugment(c_vision.RandomRotation(90), 1)
# map to apply ops
dataVoc2 = dataVoc2.map(operations=[test_op],
input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
column_order=["image", "bbox"])
filename = "bounding_box_augment_rotation_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)
def test_cpp_uniform_augment_exception_float_numops(num_ops=2.5):
"""
Test UniformAugment invalid float number of ops
"""
logger.info("Test CPP UniformAugment invalid float num_ops exception")
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
C.RandomHorizontalFlip(),
C.RandomVerticalFlip(),
C.RandomColorAdjust(),
C.RandomRotation(degrees=45)]
try:
_ = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
except Exception as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Argument num_ops with value 2.5 is not of type (<class 'int'>,)" in str(e)
def test_cpp_uniform_augment_exception_nonpositive_numops(num_ops=0):
"""
Test UniformAugment invalid non-positive number of ops
"""
logger.info("Test CPP UniformAugment invalid non-positive num_ops exception")
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
C.RandomHorizontalFlip(),
C.RandomVerticalFlip(),
C.RandomColorAdjust(),
C.RandomRotation(degrees=45)]
try:
_ = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
except Exception as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Input num_ops must be greater than 0" in str(e)
def test_cpp_uniform_augment_exception_pyops(num_ops=2):
"""
Test UniformAugment invalid op in operations
"""
logger.info("Test CPP UniformAugment invalid OP exception")
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
C.RandomHorizontalFlip(),
C.RandomVerticalFlip(),
C.RandomColorAdjust(),
C.RandomRotation(degrees=45),
F.Invert()]
with pytest.raises(TypeError) as e:
C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Type of Transforms[5] must be c_transform" in str(e.value)
def test_random_rotation_md5():
"""
Test RandomRotation with md5 check
"""
logger.info("Test RandomRotation with md5 check")
original_seed = config_get_set_seed(5)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Fisrt dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
decode_op = c_vision.Decode()
resize_op = c_vision.RandomRotation((0, 90),
expand=True,
resample=Inter.BILINEAR,
center=(50, 50),
fill_value=150)
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=resize_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
transform2 = mindspore.dataset.transforms.py_transforms.Compose([
py_vision.Decode(),
py_vision.RandomRotation((0, 90),
expand=True,
resample=Inter.BILINEAR,
center=(50, 50),
fill_value=150),
py_vision.ToTensor()
])
data2 = data2.map(operations=transform2, input_columns=["image"])
# Compare with expected md5 from images
filename1 = "random_rotation_01_c_result.npz"
save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN)
filename2 = "random_rotation_01_py_result.npz"
save_and_check_md5(data2, filename2, generate_golden=GENERATE_GOLDEN)
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_serdes_voc_dataset(remove_json_files=True):
"""
Test serdes on VOC dataset pipeline.
"""
data_dir = "../data/dataset/testVOC2012"
original_seed = config_get_set_seed(1)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# define map operations
random_color_adjust_op = vision.RandomColorAdjust(brightness=(0.5, 0.5))
random_rotation_op = vision.RandomRotation((0, 90),
expand=True,
resample=Inter.BILINEAR,
center=(50, 50),
fill_value=150)
data1 = ds.VOCDataset(data_dir,
task="Detection",
usage="train",
decode=True)
data1 = data1.map(operations=random_color_adjust_op,
input_columns=["image"])
data1 = data1.map(operations=random_rotation_op, input_columns=["image"])
data1 = data1.skip(2)
data2 = util_check_serialize_deserialize_file(data1,
"voc_dataset_pipeline",
remove_json_files)
num_samples = 0
# Iterate and compare the data in the original pipeline (data1) against the deserialized pipeline (data2)
for item1, item2 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
np.testing.assert_array_equal(item1['image'], item2['image'])
num_samples += 1
assert num_samples == 7
# Restore configuration num_parallel_workers
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_cpp_uniform_augment_exception_pyops(num_ops=2):
"""
Test UniformAugment invalid op in operations
"""
logger.info("Test CPP UniformAugment invalid OP exception")
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
C.RandomHorizontalFlip(),
C.RandomVerticalFlip(),
C.RandomColorAdjust(),
C.RandomRotation(degrees=45),
F.Invert()]
with pytest.raises(TypeError) as e:
C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Argument transforms[5] with value" \
" <mindspore.dataset.vision.py_transforms.Invert" in str(e.value)
assert "is not of type (<class 'mindspore._c_dataengine.TensorOp'>,"\
" <class 'mindspore._c_dataengine.TensorOperation'>)" in str(e.value)
def test_random_rotation_expand():
"""
Test RandomRotation op
"""
logger.info("test_random_rotation_op")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
decode_op = c_vision.Decode()
# expand is set to be True to match output size
random_rotation_op = c_vision.RandomRotation((0, 90), expand=True)
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=random_rotation_op, input_columns=["image"])
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1):
rotation = item["image"]
logger.info("shape after rotate: {}".format(rotation.shape))
num_iter += 1
def test_random_rotation_op_c(plot=False):
"""
Test RandomRotation in c++ transformations op
"""
logger.info("test_random_rotation_op_c")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
decode_op = c_vision.Decode()
# use [90, 90] to force rotate 90 degrees, expand is set to be True to match output size
random_rotation_op = c_vision.RandomRotation((90, 90), expand=True)
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=random_rotation_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = data2.map(operations=decode_op, input_columns=["image"])
num_iter = 0
for item1, item2 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
if num_iter > 0:
break
rotation_de = item1["image"]
original = item2["image"]
logger.info("shape before rotate: {}".format(original.shape))
rotation_cv = cv2.rotate(original, cv2.ROTATE_90_COUNTERCLOCKWISE)
mse = diff_mse(rotation_de, rotation_cv)
logger.info("random_rotation_op_{}, mse: {}".format(num_iter + 1, mse))
assert mse == 0
num_iter += 1
if plot:
visualize_image(original, rotation_de, mse, rotation_cv)
