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_invert_py_c(plot=False):
"""
Test Invert Cpp op and python op
"""
logger.info("Test Invert cpp and python op")
# Invert Images in cpp
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data_set = data_set.map(operations=[C.Decode(),
C.Resize((224, 224))],
input_columns=["image"])
ds_c_invert = data_set.map(operations=C.Invert(), input_columns="image")
ds_c_invert = ds_c_invert.batch(512)
for idx, (image, _) in enumerate(ds_c_invert):
if idx == 0:
images_c_invert = image.asnumpy()
else:
images_c_invert = np.append(images_c_invert,
image.asnumpy(),
axis=0)
# invert images in python
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data_set = data_set.map(operations=[C.Decode(),
C.Resize((224, 224))],
input_columns=["image"])
transforms_p_invert = mindspore.dataset.transforms.py_transforms.Compose(
[lambda img: img.astype(np.uint8),
F.ToPIL(),
F.Invert(), np.array])
ds_p_invert = data_set.map(operations=transforms_p_invert,
input_columns="image")
ds_p_invert = ds_p_invert.batch(512)
for idx, (image, _) in enumerate(ds_p_invert):
if idx == 0:
images_p_invert = image.asnumpy()
else:
images_p_invert = np.append(images_p_invert,
image.asnumpy(),
axis=0)
num_samples = images_c_invert.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_p_invert[i], images_c_invert[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_c_invert, images_p_invert, visualize_mode=2)
def test_invert_callable():
"""
Test Invert is callable
"""
logger.info("Test Invert callable")
img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8)
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
img = C.Decode()(img)
img = C.Invert()(img)
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
assert img.shape == (2268, 4032, 3)
def test_invert_c(plot=False):
"""
Test Invert Cpp op
"""
logger.info("Test Invert cpp op")
# Original Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = [C.Decode(), C.Resize(size=[224, 224])]
ds_original = data_set.map(operations=transforms_original, input_columns="image")
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = image.asnumpy()
else:
images_original = np.append(images_original,
image.asnumpy(),
axis=0)
# Invert Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transform_invert = [C.Decode(), C.Resize(size=[224, 224]),
C.Invert()]
ds_invert = data_set.map(operations=transform_invert, input_columns="image")
ds_invert = ds_invert.batch(512)
for idx, (image, _) in enumerate(ds_invert):
if idx == 0:
images_invert = image.asnumpy()
else:
images_invert = np.append(images_invert,
image.asnumpy(),
axis=0)
if plot:
visualize_list(images_original, images_invert)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_invert[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
def test_invert_md5_c():
"""
Test Invert cpp op with md5 check
"""
logger.info("Test Invert cpp op with md5 check")
# Generate dataset
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_invert = [C.Decode(),
C.Resize(size=[224, 224]),
C.Invert(),
F.ToTensor()]
data = data_set.map(operations=transforms_invert, input_columns="image")
# Compare with expected md5 from images
filename = "invert_01_result_c.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
def test_invert_one_channel():
"""
Test Invert cpp op with one channel image
"""
logger.info("Test Invert C Op With One Channel Images")
c_op = C.Invert()
try:
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224)),
lambda img: np.array(img[:, :, 0])], input_columns=["image"])
data_set.map(operations=c_op, input_columns="image")
except RuntimeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "The shape" in str(e)