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_choice_comp(plot=False):
"""
Test RandomChoice and compare with single CenterCrop results
"""
logger.info("test_random_choice_comp")
# define map operations
transforms_list = [py_vision.CenterCrop(64)]
transforms1 = [
py_vision.Decode(),
py_transforms.RandomChoice(transforms_list),
py_vision.ToTensor()
]
transform1 = py_transforms.Compose(transforms1)
transforms2 = [
py_vision.Decode(),
py_vision.CenterCrop(64),
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_choice = []
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_choice.append(image1)
image_original.append(image2)
mse = diff_mse(image1, image2)
assert mse == 0
if plot:
visualize_list(image_original, image_choice)
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_transforms.RandomChoice(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"])
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)