def test_auto_contrast_invalid_cutoff_param_c():
"""
Test AutoContrast C Op with invalid cutoff parameter
"""
logger.info("Test AutoContrast C Op with invalid cutoff parameter")
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"])
# invalid ignore
data_set = data_set.map(operations=C.AutoContrast(cutoff=-10.0), input_columns="image")
except ValueError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Input cutoff is not within the required interval of (0 to 100)." in str(error)
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"])
# invalid ignore
data_set = data_set.map(operations=C.AutoContrast(cutoff=120.0), input_columns="image")
except ValueError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Input cutoff is not within the required interval of (0 to 100)." in str(error)
def test_auto_contrast_invalid_ignore_param_c():
"""
Test AutoContrast C Op with invalid ignore parameter
"""
logger.info("Test AutoContrast C Op with invalid ignore parameter")
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"])
# invalid ignore
data_set = data_set.map(operations=C.AutoContrast(ignore=255.5),
input_columns="image")
except TypeError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Argument ignore with value 255.5 is not of type" in str(error)
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"])
# invalid ignore
data_set = data_set.map(operations=C.AutoContrast(ignore=(10, 100)),
input_columns="image")
except TypeError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Argument ignore with value (10,100) is not of type" in str(
error)
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_auto_contrast_mnist_c(plot=False):
"""
Test AutoContrast C op with MNIST dataset (Grayscale images)
"""
logger.info("Test AutoContrast C Op With MNIST Images")
data_set = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False)
ds_auto_contrast_c = data_set.map(operations=C.AutoContrast(cutoff=1, ignore=(0, 255)), 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_auto_contrast_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 = "autocontrast_mnist_result_c.npz"
save_and_check_md5(ds_auto_contrast_c, filename, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_one_channel_dataset(images, images_trans, labels)
def test_auto_contrast_c(plot=False):
"""
Test AutoContrast C Op
"""
logger.info("Test AutoContrast C Op")
# AutoContrast Images
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"])
python_op = F.AutoContrast(cutoff=10.0, ignore=[10, 20])
c_op = C.AutoContrast(cutoff=10.0, ignore=[10, 20])
transforms_op = mindspore.dataset.transforms.py_transforms.Compose([lambda img: F.ToPIL()(img.astype(np.uint8)),
python_op,
np.array])
ds_auto_contrast_py = data_set.map(operations=transforms_op, input_columns="image")
ds_auto_contrast_py = ds_auto_contrast_py.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast_py):
if idx == 0:
images_auto_contrast_py = image.asnumpy()
else:
images_auto_contrast_py = np.append(images_auto_contrast_py,
image.asnumpy(),
axis=0)
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_auto_contrast_c = data_set.map(operations=c_op, input_columns="image")
ds_auto_contrast_c = ds_auto_contrast_c.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast_c):
if idx == 0:
images_auto_contrast_c = image.asnumpy()
else:
images_auto_contrast_c = np.append(images_auto_contrast_c,
image.asnumpy(),
axis=0)
num_samples = images_auto_contrast_c.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_auto_contrast_c[i], images_auto_contrast_py[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
np.testing.assert_equal(np.mean(mse), 0.0)
# Compare with expected md5 from images
filename = "autocontrast_01_result_c.npz"
save_and_check_md5(ds_auto_contrast_c, filename, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2)
def test_auto_contrast_one_channel_c(plot=False):
"""
Test AutoContrast C op with one channel
"""
logger.info("Test AutoContrast C Op With One Channel Images")
# AutoContrast Images
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"])
python_op = F.AutoContrast()
c_op = C.AutoContrast()
# not using F.ToTensor() since it converts to floats
transforms_op = mindspore.dataset.transforms.py_transforms.Compose(
[lambda img: (np.array(img)[:, :, 0]).astype(np.uint8),
F.ToPIL(),
python_op,
np.array])
ds_auto_contrast_py = data_set.map(operations=transforms_op, input_columns="image")
ds_auto_contrast_py = ds_auto_contrast_py.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast_py):
if idx == 0:
images_auto_contrast_py = image.asnumpy()
else:
images_auto_contrast_py = np.append(images_auto_contrast_py,
image.asnumpy(),
axis=0)
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"])
ds_auto_contrast_c = data_set.map(operations=c_op, input_columns="image")
ds_auto_contrast_c = ds_auto_contrast_c.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast_c):
if idx == 0:
images_auto_contrast_c = image.asnumpy()
else:
images_auto_contrast_c = np.append(images_auto_contrast_c,
image.asnumpy(),
axis=0)
num_samples = images_auto_contrast_c.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_auto_contrast_c[i], images_auto_contrast_py[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
np.testing.assert_equal(np.mean(mse), 0.0)
if plot:
visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2)