def test_random_crop_op_py(plot=False):
"""
Test RandomCrop op in py transforms
"""
logger.info("test_random_crop_op_py")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms1 = [
py_vision.Decode(),
py_vision.RandomCrop([512, 512], [200, 200, 200, 200]),
py_vision.ToTensor()
]
transform1 = py_vision.ComposeOp(transforms1)
data1 = data1.map(input_columns=["image"], operations=transform1())
# Second dataset
# Second dataset for comparison
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms2 = [
py_vision.Decode(),
py_vision.ToTensor()
]
transform2 = py_vision.ComposeOp(transforms2)
data2 = data2.map(input_columns=["image"], operations=transform2())
crop_images = []
original_images = []
for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
crop = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
crop_images.append(crop)
original_images.append(original)
if plot:
visualize(original_images, crop_images)
def test_random_crop_comp(plot=False):
"""
Test RandomCrop and compare between python and c image augmentation
"""
logger.info("Test RandomCrop with c_transform and py_transform comparison")
cropped_size = 512
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
random_crop_op = c_vision.RandomCrop(cropped_size)
decode_op = c_vision.Decode()
data1 = data1.map(input_columns=["image"], operations=decode_op)
data1 = data1.map(input_columns=["image"], operations=random_crop_op)
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.RandomCrop(cropped_size),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
data2 = data2.map(input_columns=["image"], operations=transform())
image_c_cropped = []
image_py_cropped = []
for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
c_image = item1["image"]
py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_c_cropped.append(c_image)
image_py_cropped.append(py_image)
if plot:
visualize(image_c_cropped, image_py_cropped)
def test_random_crop_04_py():
"""
Test RandomCrop op with py_transforms:
input image size < crop size, expected to fail
"""
logger.info("test_random_crop_04_py")
ds.config.set_seed(0)
ds.config.set_num_parallel_workers(1)
try:
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# Note: The size of the image is 4032*2268
transforms = [
py_vision.Decode(),
py_vision.RandomCrop([2268, 4033]),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
image_list = []
for item in data.create_dict_iterator():
image = (item["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_list.append(image.shape)
except Exception as e:
logger.info("Got an exception in DE: {}".format(str(e)))
def test_random_crop_08_py():
"""
Test RandomCrop op with py_transforms: padding_mode is Border.EDGE,
expected to pass
"""
logger.info("test_random_crop_08_py")
original_seed = config_get_set_seed(0)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# Note: The padding_mode is Border.EDGE.
transforms = [
py_vision.Decode(),
py_vision.RandomCrop(512, [200, 200, 200, 200],
padding_mode=mode.Border.EDGE),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
filename = "random_crop_08_py_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
# Restore config setting
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_crop_05_py():
"""
Test RandomCrop op with py_transforms:
input image size < crop size but pad_if_needed is enabled,
expected to pass
"""
logger.info("test_random_crop_05_py")
original_seed = config_get_set_seed(0)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# Note: The size of the image is 4032*2268
transforms = [
py_vision.Decode(),
py_vision.RandomCrop([2268, 4033], [200, 200, 200, 200],
pad_if_needed=True),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
filename = "random_crop_05_py_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
# Restore config setting
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_crop_02_py():
"""
Test RandomCrop op with py_transforms: size is a list/tuple with length 2, expected to pass
"""
logger.info("test_random_crop_02_py")
original_seed = config_get_set_seed(0)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# Note: If size is a sequence of length 2, it should be (height, width).
transforms = [
py_vision.Decode(),
py_vision.RandomCrop([512, 375]),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
filename = "random_crop_02_py_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
# Restore config setting
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_crop_04_py():
"""
Test RandomCrop op with py_transforms:
input image size < crop size, expected to fail
"""
logger.info("test_random_crop_04_py")
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# Note: The size of the image is 4032*2268
transforms = [
py_vision.Decode(),
py_vision.RandomCrop([2268, 4033]),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
try:
data.create_dict_iterator().get_next()
except RuntimeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Crop size" in str(e)
def test_random_choice_exception_random_crop_badinput():
"""
Test RandomChoice: hit error in RandomCrop with greater crop size,
expected to raise error
"""
logger.info("test_random_choice_exception_random_crop_badinput")
# define map operations
# note: crop size[5000, 5000] > image size[4032, 2268]
transforms_list = [py_vision.RandomCrop(5000)]
transforms = [
py_vision.Decode(),
py_vision.RandomChoice(transforms_list),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data = data.map(input_columns=["image"], operations=transform())
try:
_ = data.create_dict_iterator().get_next()
except RuntimeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Crop size" in str(e)
def test_random_crop_01_py():
"""
Test RandomCrop op with py_transforms: size is a single integer, expected to pass
"""
logger.info("test_random_crop_01_py")
original_seed = config_get_set_seed(0)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# Note: If size is an int, a square crop of size (size, size) is returned.
transforms = [
py_vision.Decode(),
py_vision.RandomCrop(512),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
filename = "random_crop_01_py_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
# Restore config setting
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_crop_07_py():
"""
Test RandomCrop op with py_transforms:
padding_mode is Border.CONSTANT and fill_value is 255 (White),
expected to pass
"""
logger.info("test_random_crop_07_py")
ds.config.set_seed(0)
ds.config.set_num_parallel_workers(1)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# Note: The padding_mode is default as Border.CONSTANT and set filling color to be white.
transforms = [
py_vision.Decode(),
py_vision.RandomCrop(512, [200, 200, 200, 200],
fill_value=(255, 255, 255)),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
filename = "random_crop_07_py_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
def test_random_crop_09():
"""
Test RandomCrop op: invalid type of input image (not PIL), expected to raise TypeError
"""
logger.info("test_random_crop_09")
ds.config.set_seed(0)
ds.config.set_num_parallel_workers(1)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.ToTensor(),
# Note: if input is not PIL image, TypeError will raise
py_vision.RandomCrop(512)
]
transform = py_vision.ComposeOp(transforms)
try:
data = data.map(input_columns=["image"], operations=transform())
image_list = []
for item in data.create_dict_iterator():
image = item["image"]
image_list.append(image.shape)
except Exception as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "should be PIL Image" in str(e)
def test_random_apply_exception_random_crop_badinput():
"""
Test RandomApply: test invalid input for one of the transform functions,
expected to raise error
"""
logger.info("test_random_apply_exception_random_crop_badinput")
original_seed = config_get_set_seed(200)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# define map operations
transforms_list = [
py_vision.Resize([32, 32]),
py_vision.RandomCrop(100), # crop size > image size
py_vision.RandomRotation(30)
]
transforms = [
py_vision.Decode(),
py_vision.RandomApply(transforms_list, prob=0.6),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data = data.map(input_columns=["image"], operations=transform())
try:
_ = data.create_dict_iterator().get_next()
except RuntimeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Crop size" in str(e)
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_crop_06_py():
"""
Test RandomCrop op with py_transforms:
invalid size, expected to raise TypeError
"""
logger.info("test_random_crop_06_py")
ds.config.set_seed(0)
ds.config.set_num_parallel_workers(1)
try:
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# Note: if size is neither an int nor a list of length 2, an exception will raise
transforms = [
py_vision.Decode(),
py_vision.RandomCrop([512, 512, 375]),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
image_list = []
for item in data.create_dict_iterator():
image = (item["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_list.append(image.shape)
except TypeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Size" in str(e)
def test_random_order_md5():
"""
Test RandomOrder op with md5 check
"""
logger.info("test_random_order_md5")
original_seed = config_get_set_seed(8)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# define map operations
transforms_list = [py_vision.RandomCrop(64), py_vision.RandomRotation(30)]
transforms = [
py_vision.Decode(),
py_vision.RandomOrder(transforms_list),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data = data.map(input_columns=["image"], operations=transform())
# check results with md5 comparison
filename = "random_order_01_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers((original_num_parallel_workers))
def test_deterministic_python_seed_multi_thread():
"""
Test deterministic execution with seed in python, this fails with multi-thread pyfunc run
"""
logger.info("test_deterministic_python_seed_multi_thread")
# Save original configuration values
num_parallel_workers_original = ds.config.get_num_parallel_workers()
seed_original = ds.config.get_seed()
ds.config.set_num_parallel_workers(3)
ds.config.set_seed(0)
# when we set the seed all operations within our dataset should be deterministic
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.RandomCrop([512, 512], [200, 200, 200, 200]),
py_vision.ToTensor(),
]
transform = py_vision.ComposeOp(transforms)
data1 = data1.map(input_columns=["image"],
operations=transform(),
python_multiprocessing=True)
data1_output = []
# config.set_seed() calls random.seed()
for data_one in data1.create_dict_iterator():
data1_output.append(data_one["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# If seed is set up on constructor
data2 = data2.map(input_columns=["image"],
operations=transform(),
python_multiprocessing=True)
# config.set_seed() calls random.seed()
ds.config.set_seed(0)
data2_output = []
for data_two in data2.create_dict_iterator():
data2_output.append(data_two["image"])
try:
np.testing.assert_equal(data1_output, data2_output)
except Exception as e:
# expect output to not match during multi-threaded excution
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Array" in str(e)
# Restore original configuration values
ds.config.set_num_parallel_workers(num_parallel_workers_original)
ds.config.set_seed(seed_original)
def test_deterministic_python_seed():
"""
Test deterministic execution with seed in python
"""
logger.info("test_deterministic_python_seed")
# Save original configuration values
num_parallel_workers_original = ds.config.get_num_parallel_workers()
seed_original = ds.config.get_seed()
ds.config.set_seed(0)
ds.config.set_num_parallel_workers(1)
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.RandomCrop([512, 512], [200, 200, 200, 200]),
py_vision.ToTensor(),
]
transform = py_vision.ComposeOp(transforms)
data1 = data1.map(input_columns=["image"], operations=transform())
data1_output = []
# config.set_seed() calls random.seed()
for data_one in data1.create_dict_iterator():
data1_output.append(data_one["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = data2.map(input_columns=["image"], operations=transform())
# config.set_seed() calls random.seed(), resets seed for next dataset iterator
ds.config.set_seed(0)
data2_output = []
for data_two in data2.create_dict_iterator():
data2_output.append(data_two["image"])
np.testing.assert_equal(data1_output, data2_output)
# Restore original configuration values
ds.config.set_num_parallel_workers(num_parallel_workers_original)
ds.config.set_seed(seed_original)
def test_random_crop_06_py():
"""
Test RandomCrop op with py_transforms:
invalid size, expected to raise TypeError
"""
logger.info("test_random_crop_06_py")
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
try:
# Note: if size is neither an int nor a list of length 2, an exception will raise
transforms = [
py_vision.Decode(),
py_vision.RandomCrop([512, 512, 375]),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
except TypeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Size should be a single integer" in str(e)
def test_random_crop_09():
"""
Test RandomCrop op: invalid type of input image (not PIL), expected to raise TypeError
"""
logger.info("test_random_crop_09")
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.ToTensor(),
# Note: if input is not PIL image, TypeError will raise
py_vision.RandomCrop(512)
]
transform = py_vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
try:
data.create_dict_iterator().get_next()
except RuntimeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "should be PIL Image" in str(e)
def test_random_crop_03_py():
"""
Test RandomCrop op with py_transforms: input image size == crop size, expected to pass
"""
logger.info("test_random_crop_03_py")
ds.config.set_seed(0)
ds.config.set_num_parallel_workers(1)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# Note: The size of the image is 4032*2268
transforms = [
py_vision.Decode(),
py_vision.RandomCrop([2268, 4032]),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
filename = "random_crop_03_py_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
