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_random_solarize_mnist(plot=False, run_golden=True):
"""
Test RandomSolarize op with MNIST dataset (Grayscale images)
"""
mnist_1 = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR,
num_samples=2,
shuffle=False)
mnist_2 = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR,
num_samples=2,
shuffle=False)
mnist_2 = mnist_2.map(operations=vision.RandomSolarize((0, 255)),
input_columns="image")
images = []
images_trans = []
labels = []
for _, (data_orig, data_trans) in enumerate(zip(mnist_1, mnist_2)):
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)
if run_golden:
filename = "random_solarize_02_result.npz"
save_and_check_md5(mnist_2, filename, generate_golden=GENERATE_GOLDEN)
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_auto_contrast_mnist_c(plot=False):
"""
Test AutoContrast C op with MNIST dataset (Grayscale images)
"""
logger.info("Test AutoContrast C Op With MNIST Images")
ds = de.MnistDataset(dataset_dir=MNIST_DATA_DIR,
num_samples=2,
shuffle=False)
ds_auto_contrast_c = ds.map(input_columns="image",
operations=C.AutoContrast(cutoff=1,
ignore=(0, 255)))
ds_orig = de.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)
labels.append(label_orig)
images_trans.append(image_trans)
# 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)
