def test_compare_random_color_op(degrees=None, plot=False):
"""
Compare Random Color op in Python and Cpp
"""
logger.info("test_random_color_op")
original_seed = config_get_set_seed(5)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Decode with rgb format set to True
data1 = ds.TFRecordDataset(C_DATA_DIR,
C_SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = ds.TFRecordDataset(C_DATA_DIR,
C_SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
if degrees is None:
c_op = vision.RandomColor()
p_op = F.RandomColor()
else:
c_op = vision.RandomColor(degrees)
p_op = F.RandomColor(degrees)
transforms_random_color_py = F.ComposeOp(
[lambda img: img.astype(np.uint8),
F.ToPIL(), p_op, np.array])
data1 = data1.map(input_columns=["image"],
operations=[vision.Decode(), c_op])
data2 = data2.map(input_columns=["image"], operations=[vision.Decode()])
data2 = data2.map(input_columns=["image"],
operations=transforms_random_color_py())
image_random_color_op = []
image = []
for item1, item2 in zip(data1.create_dict_iterator(),
data2.create_dict_iterator()):
actual = item1["image"]
expected = item2["image"]
image_random_color_op.append(actual)
image.append(expected)
assert actual.shape == expected.shape
mse = diff_mse(actual, expected)
logger.info("MSE= {}".format(str(np.mean(mse))))
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
if plot:
visualize_list(image, image_random_color_op)
def test_random_color(degrees=(0.1, 1.9), plot=False):
"""
Test RandomColor
"""
logger.info("Test RandomColor")
# Original Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = ds.map(input_columns="image",
operations=transforms_original())
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image, (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
# Random Color Adjusted Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_random_color = F.ComposeOp([
F.Decode(),
F.Resize((224, 224)),
F.RandomColor(degrees=degrees),
F.ToTensor()
])
ds_random_color = ds.map(input_columns="image",
operations=transforms_random_color())
ds_random_color = ds_random_color.batch(512)
for idx, (image, _) in enumerate(ds_random_color):
if idx == 0:
images_random_color = np.transpose(image, (0, 2, 3, 1))
else:
images_random_color = np.append(images_random_color,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = np.mean((images_random_color[i] - images_original[i])**2)
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize(images_original, images_random_color)
def test_random_color_md5():
"""
Test RandomColor with md5 check
"""
logger.info("Test RandomColor with md5 check")
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Generate dataset
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms = F.ComposeOp(
[F.Decode(), F.RandomColor((0.1, 1.9)),
F.ToTensor()])
data = data.map(input_columns="image", operations=transforms())
# Compare with expected md5 from images
filename = "random_color_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_uniform_augment(plot=False, num_ops=2):
"""
Test UniformAugment
"""
logger.info("Test UniformAugment")
# Original Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = ds.map(input_columns="image",
operations=transforms_original())
ds_original = ds_original.batch(512)
for idx, (image, label) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image, (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
# UniformAugment Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transform_list = [
F.RandomRotation(45),
F.RandomColor(),
F.RandomSharpness(),
F.Invert(),
F.AutoContrast(),
F.Equalize()
]
transforms_ua = F.ComposeOp([
F.Decode(),
F.Resize((224, 224)),
F.UniformAugment(transforms=transform_list, num_ops=num_ops),
F.ToTensor()
])
ds_ua = ds.map(input_columns="image", operations=transforms_ua())
ds_ua = ds_ua.batch(512)
for idx, (image, label) in enumerate(ds_ua):
if idx == 0:
images_ua = np.transpose(image, (0, 2, 3, 1))
else:
images_ua = np.append(images_ua,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = np.mean((images_ua[i] - images_original[i])**2)
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize(images_original, images_ua)