def test_random_color_c_errors():
"""
Test that Cpp RandomColor errors with bad input
"""
with pytest.raises(TypeError) as error_info:
vision.RandomColor((12))
assert "degrees must be either a tuple or a list." in str(error_info.value)
with pytest.raises(TypeError) as error_info:
vision.RandomColor(("col", 3))
assert "Argument degrees[0] with value col is not of type (<class 'int'>, <class 'float'>)." in str(
error_info.value)
with pytest.raises(ValueError) as error_info:
vision.RandomColor((0.9, 0.1))
assert "degrees should be in (min,max) format. Got (max,min)." in str(error_info.value)
with pytest.raises(ValueError) as error_info:
vision.RandomColor((0.9,))
assert "degrees must be a sequence with length 2." in str(error_info.value)
# RandomColor Cpp Op will fail with one channel input
mnist_ds = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False)
mnist_ds = mnist_ds.map(operations=vision.RandomColor(), input_columns="image")
with pytest.raises(RuntimeError) as error_info:
for _ in enumerate(mnist_ds):
pass
assert "Invalid number of channels in input image" in str(error_info.value)
def test_compare_random_color_op(degrees=None, plot=False):
"""
Compare Random Color op in Python and Cpp
"""
logger.info("test_random_color_op")
original_seed = config_get_set_seed(5)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Decode with rgb format set to True
data1 = ds.TFRecordDataset(C_DATA_DIR,
C_SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = ds.TFRecordDataset(C_DATA_DIR,
C_SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
if degrees is None:
c_op = vision.RandomColor()
p_op = F.RandomColor()
else:
c_op = vision.RandomColor(degrees)
p_op = F.RandomColor(degrees)
transforms_random_color_py = mindspore.dataset.transforms.py_transforms.Compose(
[lambda img: img.astype(np.uint8),
F.ToPIL(), p_op, np.array])
data1 = data1.map(operations=[vision.Decode(), c_op],
input_columns=["image"])
data2 = data2.map(operations=[vision.Decode()], input_columns=["image"])
data2 = data2.map(operations=transforms_random_color_py,
input_columns=["image"])
image_random_color_op = []
image = []
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)):
actual = item1["image"]
expected = item2["image"]
image_random_color_op.append(actual)
image.append(expected)
assert actual.shape == expected.shape
mse = diff_mse(actual, expected)
logger.info("MSE= {}".format(str(np.mean(mse))))
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
if plot:
visualize_list(image, image_random_color_op)
def test_random_color_c(degrees=(0.1, 1.9), plot=False, run_golden=True):
"""
Test Cpp RandomColor
"""
logger.info("test_random_color_op")
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Decode with rgb format set to True
data1 = ds.TFRecordDataset(C_DATA_DIR,
C_SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = ds.TFRecordDataset(C_DATA_DIR,
C_SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# Serialize and Load dataset requires using vision.Decode instead of vision.Decode().
if degrees is None:
c_op = vision.RandomColor()
else:
c_op = vision.RandomColor(degrees)
data1 = data1.map(operations=[vision.Decode()], input_columns=["image"])
data2 = data2.map(operations=[vision.Decode(), c_op],
input_columns=["image"])
image_random_color_op = []
image = []
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)):
actual = item1["image"]
expected = item2["image"]
image.append(actual)
image_random_color_op.append(expected)
if run_golden:
# Compare with expected md5 from images
filename = "random_color_op_02_result.npz"
save_and_check_md5(data2, filename, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(image, image_random_color_op)
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers((original_num_parallel_workers))
def test_serdes_uniform_augment(remove_json_files=True):
"""
Test serdes on uniform augment.
"""
data_dir = "../data/dataset/testPK/data"
data = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
ds.config.set_seed(1)
transforms_ua = [
vision.RandomHorizontalFlip(),
vision.RandomVerticalFlip(),
vision.RandomColor(),
vision.RandomSharpness(),
vision.Invert(),
vision.AutoContrast(),
vision.Equalize()
]
transforms_all = [
vision.Decode(),
vision.Resize(size=[224, 224]),
vision.UniformAugment(transforms=transforms_ua, num_ops=5)
]
data = data.map(operations=transforms_all,
input_columns="image",
num_parallel_workers=1)
util_check_serialize_deserialize_file(data, "uniform_augment_pipeline",
remove_json_files)
def test_c_py_compose_vision_module(plot=False, run_golden=True):
"""
Test combining Python and C++ vision transforms
"""
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
def test_config(plot, file_name, op_list):
data_dir = "../data/dataset/testImageNetData/train/"
data1 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
data1 = data1.map(operations=op_list, input_columns=["image"])
data2 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
data2 = data2.map(operations=c_vision.Decode(), input_columns=["image"])
original_images = []
transformed_images = []
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
transformed_images.append(item["image"])
for item in data2.create_dict_iterator(num_epochs=1, output_numpy=True):
original_images.append(item["image"])
if run_golden:
# Compare with expected md5 from images
save_and_check_md5(data1, file_name, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(original_images, transformed_images)
test_config(op_list=[c_vision.Decode(),
py_vision.ToPIL(),
py_vision.Resize((224, 224)),
np.array],
plot=plot, file_name="compose_c_py_1.npz")
test_config(op_list=[c_vision.Decode(),
c_vision.Resize((224, 244)),
py_vision.ToPIL(),
np.array,
c_vision.Resize((24, 24))],
plot=plot, file_name="compose_c_py_2.npz")
test_config(op_list=[py_vision.Decode(),
py_vision.Resize((224, 224)),
np.array,
c_vision.RandomColor()],
plot=plot, file_name="compose_c_py_3.npz")
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers((original_num_parallel_workers))
