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_equalize_mnist_c(plot=False):
"""
Test Equalize C op with MNIST dataset (Grayscale images)
"""
logger.info("Test Equalize C Op With MNIST Images")
data_set = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR,
num_samples=2,
shuffle=False)
ds_equalize_c = data_set.map(operations=C.Equalize(),
input_columns="image")
ds_orig = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR,
num_samples=2,
shuffle=False)
images = []
images_trans = []
labels = []
for _, (data_orig, data_trans) in enumerate(zip(ds_orig, ds_equalize_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())
# Compare with expected md5 from images
filename = "equalize_mnist_result_c.npz"
save_and_check_md5(ds_equalize_c,
filename,
generate_golden=GENERATE_GOLDEN)
if plot:
visualize_one_channel_dataset(images, images_trans, labels)
def test_equalize_py_c(plot=False):
"""
Test Equalize Cpp op and python op
"""
logger.info("Test Equalize cpp and python op")
# equalize 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_equalize = data_set.map(operations=C.Equalize(),
input_columns="image")
ds_c_equalize = ds_c_equalize.batch(512)
for idx, (image, _) in enumerate(ds_c_equalize):
if idx == 0:
images_c_equalize = image.asnumpy()
else:
images_c_equalize = np.append(images_c_equalize,
image.asnumpy(),
axis=0)
# Equalize 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_equalize = mindspore.dataset.transforms.py_transforms.Compose(
[lambda img: img.astype(np.uint8),
F.ToPIL(),
F.Equalize(), np.array])
ds_p_equalize = data_set.map(operations=transforms_p_equalize,
input_columns="image")
ds_p_equalize = ds_p_equalize.batch(512)
for idx, (image, _) in enumerate(ds_p_equalize):
if idx == 0:
images_p_equalize = image.asnumpy()
else:
images_p_equalize = np.append(images_p_equalize,
image.asnumpy(),
axis=0)
num_samples = images_c_equalize.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_p_equalize[i], images_c_equalize[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_c_equalize, images_p_equalize, visualize_mode=2)
def test_equalize_c(plot=False):
"""
Test Equalize Cpp op
"""
logger.info("Test Equalize 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)
# Equalize Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transform_equalize = [C.Decode(), C.Resize(size=[224, 224]), C.Equalize()]
ds_equalize = data_set.map(operations=transform_equalize,
input_columns="image")
ds_equalize = ds_equalize.batch(512)
for idx, (image, _) in enumerate(ds_equalize):
if idx == 0:
images_equalize = image.asnumpy()
else:
images_equalize = np.append(images_equalize,
image.asnumpy(),
axis=0)
if plot:
visualize_list(images_original, images_equalize)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_equalize[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
def test_equalize_md5_c():
"""
Test Equalize cpp op with md5 check
"""
logger.info("Test Equalize cpp op with md5 check")
# Generate dataset
ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_equalize = [C.Decode(),
C.Resize(size=[224, 224]),
C.Equalize(),
F.ToTensor()]
data = ds.map(operations=transforms_equalize, input_columns="image")
# Compare with expected md5 from images
filename = "equalize_01_result_c.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
def test_equalize_one_channel():
"""
Test Equalize cpp op with one channel image
"""
logger.info("Test Equalize C Op With One Channel Images")
c_op = C.Equalize()
try:
ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(operations=[C.Decode(), C.Resize((224, 224)),
lambda img: np.array(img[:, :, 0])], input_columns=["image"])
ds.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)