def test_py_transforms_with_c_vision():
"""
These examples will fail, as py_transforms.Random(Apply/Choice/Order) expect callable functions
"""
ds.config.set_seed(0)
def test_config(op_list):
data_dir = "../data/dataset/testImageNetData/train/"
data = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
data = data.map(operations=op_list)
res = []
for i in data.create_dict_iterator(output_numpy=True):
for col_name in output_cols:
res.append(i[col_name].tolist())
return res
with pytest.raises(ValueError) as error_info:
test_config(py_transforms.RandomApply([c_vision.Resize(200)]))
assert "transforms[0] is not callable." in str(error_info.value)
with pytest.raises(ValueError) as error_info:
test_config(py_transforms.RandomChoice([c_vision.Resize(200)]))
assert "transforms[0] is not callable." in str(error_info.value)
with pytest.raises(ValueError) as error_info:
test_config(py_transforms.RandomOrder([np.array,
c_vision.Resize(200)]))
assert "transforms[1] is not callable." in str(error_info.value)
with pytest.raises(RuntimeError) as error_info:
test_config([py_transforms.OneHotOp(20, 0.1)])
assert "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" in str(
error_info.value)
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_transforms.RandomApply(transforms_list),
py_vision.ToTensor()
]
transform = py_transforms.Compose(transforms)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data = data.map(operations=transform, input_columns=["image"])
# 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_transforms.RandomApply(transforms_list, prob=0.6),
py_vision.ToTensor()
]
transform = py_transforms.Compose(transforms)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data = data.map(operations=transform, input_columns=["image"])
try:
_ = data.create_dict_iterator(num_epochs=1).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_transforms.RandomApply(transforms_list, prob=0.6),
py_vision.ToTensor()
]
transform1 = py_transforms.Compose(transforms1)
transforms2 = [py_vision.Decode(), py_vision.ToTensor()]
transform2 = py_transforms.Compose(transforms2)
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data1 = data1.map(operations=transform1, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = data2.map(operations=transform2, input_columns=["image"])
image_apply = []
image_original = []
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)):
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_list(image_original, image_apply)