def _randaugment_inner_for_loop(_, in_args):
"""
Loop body for for randougment.
Args:
i: loop iteration
in_args: loop body arguments
Returns:
updated loop arguments
"""
(image, geometric_transforms, random_key, available_ops, op_probs,
magnitude, cutout_const, translate_const, join_transforms,
default_replace_value) = in_args
random_keys = random.split(random_key, num=8)
random_key = random_keys[0] # keep for next iteration
op_to_select = random.choice(random_keys[1], available_ops, p=op_probs)
mask_value = jnp.where(default_replace_value > 0,
jnp.ones([image.shape[-1]]) * default_replace_value,
random.randint(random_keys[2],
[image.shape[-1]],
minval=-1, maxval=256))
random_magnitude = random.uniform(random_keys[3], [], minval=0.,
maxval=magnitude)
cutout_mask = color_transforms.get_random_cutout_mask(
random_keys[4],
image.shape,
cutout_const)
translate_vals = (random.uniform(random_keys[5], [], minval=0.0,
maxval=1.0) * translate_const,
random.uniform(random_keys[6], [], minval=0.0,
maxval=1.0) * translate_const)
negate = random.randint(random_keys[7], [], minval=0,
maxval=2).astype('bool')
args = level_to_arg(cutout_mask, translate_vals, negate,
random_magnitude, mask_value)
if DEBUG:
print(op_to_select, args[op_to_select])
image, geometric_transform = _apply_ops(image, args, op_to_select)
image, geometric_transform = jax.lax.cond(
jnp.logical_or(join_transforms, jnp.all(
jnp.not_equal(geometric_transform, jnp.identity(4)))),
lambda op: (op[0], op[1]),
lambda op: (transforms.apply_transform(op[0],
op[1],
mask_value=mask_value),
jnp.identity(4)),
(image, geometric_transform)
)
geometric_transforms = jnp.matmul(geometric_transforms, geometric_transform)
return(image, geometric_transforms, random_key, available_ops, op_probs,
magnitude, cutout_const, translate_const, join_transforms,
default_replace_value)
def distort_image_with_randaugment(image,
num_layers,
magnitude,
random_key,
cutout_const=40,
translate_const=50.0,
default_replace_value=None,
available_ops=DEFAULT_OPS,
op_probs=DEFAULT_PROBS,
join_transforms=False):
"""Applies the RandAugment policy to `image`.
RandAugment is from the paper https://arxiv.org/abs/1909.13719,
Args:
image: `Tensor` of shape [height, width, 3] representing an image.
num_layers: Integer, the number of augmentation transformations to apply
sequentially to an image. Represented as (N) in the paper. Usually best
values will be in the range [1, 3].
magnitude: Integer, shared magnitude across all augmentation operations.
Represented as (M) in the paper. Usually best values are in the range
[5, 30].
random_key: random key to do random stuff
join_transforms: reduce multiple transforms to one. Much more efficient but simpler.
cutout_const: max cutout size int
translate_const: maximum translation amount int
default_replace_value: default replacement value for pixels outside of the image
available_ops: available operations
op_probs: probabilities of operations
join_transforms: apply transformations immediately or join them
Returns:
The augmented version of `image`.
"""
geometric_transforms = jnp.identity(4)
for_i_args = (image, geometric_transforms, random_key, available_ops, op_probs,
magnitude, cutout_const, translate_const, join_transforms, default_replace_value)
if DEBUG: # un-jitted
for i in range(num_layers):
for_i_args = _randaugment_inner_for_loop(i, for_i_args)
else: # jitted
for_i_args = jax.lax.fori_loop(0, num_layers, _randaugment_inner_for_loop, for_i_args)
image, geometric_transforms = for_i_args[0], for_i_args[1]
if join_transforms:
replace_value = default_replace_value or random.randint(random_key,
[image.shape[-1]],
minval=0,
maxval=256)
image = transforms.apply_transform(image, geometric_transforms, mask_value=replace_value)
return image
def test_scale():
factor = 3
inputs = jnp.pad(jnp.ones((1, 1, 4), dtype='uint8') * 255,
((1, 1), (1, 1), (0, 0)),
constant_values=0)
targets = jnp.ones_like(rgba_img) * 255
outputs = transforms.apply_transform(jnp.pad(jnp.ones(
(1, 1, 4), dtype='uint8'), ((1, 1), (1, 1), (0, 0)),
constant_values=0) * 255,
transforms.scale_3d(scale_x=factor,
scale_y=factor),
bilinear=False)
compare(inputs, targets, outputs)
def test_rotate90():
inputs = rgba_img
targets = jnp.rot90(rgba_img, k=2)
outputs = transforms.apply_transform(rgba_img, transforms.rotate90(n=2))
compare(inputs, targets, outputs)
def test_vertical_flip():
inputs = rgba_img
targets = rgba_img[::-1]
outputs = transforms.apply_transform(
rgba_img, transforms.flip(horizontal=False, vertical=True))
compare(inputs, targets, outputs)