def test_random_apply_md5():
"""
Test RandomApply op with md5 check
"""
logger.info("test_random_apply_md5")
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# define map operations
transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)]
transforms = [
py_vision.Decode(),
# Note: using default value "prob=0.5"
py_vision.RandomApply(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_apply_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_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_apply_op(plot=False):
"""
Test RandomApply in python transformations
"""
logger.info("test_random_apply_op")
# define map operations
transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)]
transforms1 = [
py_vision.Decode(),
py_vision.RandomApply(transforms_list, prob=0.6),
py_vision.ToTensor()
]
transform1 = py_vision.ComposeOp(transforms1)
transforms2 = [py_vision.Decode(), py_vision.ToTensor()]
transform2 = py_vision.ComposeOp(transforms2)
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data1 = data1.map(input_columns=["image"], operations=transform1())
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = data2.map(input_columns=["image"], operations=transform2())
image_apply = []
image_original = []
for item1, item2 in zip(data1.create_dict_iterator(),
data2.create_dict_iterator()):
image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_apply.append(image1)
image_original.append(image2)
if plot:
visualize(image_original, image_apply)