def test_random_sharpness_one_channel_c(degrees=(1.4, 1.4), plot=False):
"""
Test Random Sharpness cpp op with one channel
"""
logger.info(
"Test RandomSharpness C Op With MNIST Dataset (Grayscale images)")
c_op = C.RandomSharpness()
if degrees is not None:
c_op = C.RandomSharpness(degrees)
# RandomSharpness Images
data = de.MnistDataset(dataset_dir=MNIST_DATA_DIR,
num_samples=2,
shuffle=False)
ds_random_sharpness_c = data.map(operations=c_op, input_columns="image")
# Original images
data = de.MnistDataset(dataset_dir=MNIST_DATA_DIR,
num_samples=2,
shuffle=False)
images = []
images_trans = []
labels = []
for _, (data_orig,
data_trans) in enumerate(zip(data, ds_random_sharpness_c)):
image_orig, label_orig = data_orig
image_trans, _ = data_trans
images.append(image_orig.asnumpy())
labels.append(label_orig.asnumpy())
images_trans.append(image_trans.asnumpy())
if plot:
visualize_one_channel_dataset(images, images_trans, labels)
def test_random_sharpness_c(degrees=(1.6, 1.6), plot=False):
"""
Test RandomSharpness cpp op
"""
print(degrees)
logger.info("Test RandomSharpness cpp op")
# Original Images
data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = [C.Decode(), C.Resize((224, 224))]
ds_original = data.map(operations=transforms_original,
input_columns="image")
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(
ds_original.create_tuple_iterator(output_numpy=True)):
if idx == 0:
images_original = image
else:
images_original = np.append(images_original, image, axis=0)
# Random Sharpness Adjusted Images
data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
c_op = C.RandomSharpness()
if degrees is not None:
c_op = C.RandomSharpness(degrees)
transforms_random_sharpness = [C.Decode(), C.Resize((224, 224)), c_op]
ds_random_sharpness = data.map(operations=transforms_random_sharpness,
input_columns="image")
ds_random_sharpness = ds_random_sharpness.batch(512)
for idx, (image, _) in enumerate(
ds_random_sharpness.create_tuple_iterator(output_numpy=True)):
if idx == 0:
images_random_sharpness = image
else:
images_random_sharpness = np.append(images_random_sharpness,
image,
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_random_sharpness[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_random_sharpness)
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_random_sharpness_invalid_params():
"""
Test RandomSharpness with invalid input parameters.
"""
logger.info("Test RandomSharpness with invalid input parameters.")
try:
data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data = data.map(operations=[
C.Decode(),
C.Resize((224, 224)),
C.RandomSharpness(10)
],
input_columns=["image"])
except TypeError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "tuple" in str(error)
try:
data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data = data.map(operations=[
C.Decode(),
C.Resize((224, 224)),
C.RandomSharpness((-10, 10))
],
input_columns=["image"])
except ValueError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "interval" in str(error)
try:
data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data = data.map(operations=[
C.Decode(),
C.Resize((224, 224)),
C.RandomSharpness((10, 5))
],
input_columns=["image"])
except ValueError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "(min,max)" in str(error)
def test_random_sharpness_c_md5():
"""
Test RandomSharpness cpp op with md5 comparison
"""
logger.info("Test RandomSharpness cpp op with md5 comparison")
original_seed = config_get_set_seed(200)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# define map operations
transforms = [C.Decode(), C.RandomSharpness((10.0, 15.0))]
# Generate dataset
data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data = data.map(operations=transforms, input_columns=["image"])
# check results with md5 comparison
filename = "random_sharpness_cpp_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_sharpness_c_py(degrees=(1.0, 1.0), plot=False):
"""
Test Random Sharpness C and python Op
"""
logger.info("Test RandomSharpness C and python Op")
# RandomSharpness Images
data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data = data.map(operations=[C.Decode(), C.Resize((200, 300))],
input_columns=["image"])
python_op = F.RandomSharpness(degrees)
c_op = C.RandomSharpness(degrees)
transforms_op = mindspore.dataset.transforms.py_transforms.Compose(
[lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array])
ds_random_sharpness_py = data.map(operations=transforms_op,
input_columns="image")
ds_random_sharpness_py = ds_random_sharpness_py.batch(512)
for idx, (image, _) in enumerate(
ds_random_sharpness_py.create_tuple_iterator(output_numpy=True)):
if idx == 0:
images_random_sharpness_py = image
else:
images_random_sharpness_py = np.append(images_random_sharpness_py,
image,
axis=0)
data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data = data.map(operations=[C.Decode(), C.Resize((200, 300))],
input_columns=["image"])
ds_images_random_sharpness_c = data.map(operations=c_op,
input_columns="image")
ds_images_random_sharpness_c = ds_images_random_sharpness_c.batch(512)
for idx, (image, _) in enumerate(
ds_images_random_sharpness_c.create_tuple_iterator(
output_numpy=True)):
if idx == 0:
images_random_sharpness_c = image
else:
images_random_sharpness_c = np.append(images_random_sharpness_c,
image,
axis=0)
num_samples = images_random_sharpness_c.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_random_sharpness_c[i],
images_random_sharpness_py[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_random_sharpness_c,
images_random_sharpness_py,
visualize_mode=2)