def __init__(self,
value=0,
per_channel=False,
name=None,
deterministic=False,
random_state=None):
super(AddCPU, self).__init__(name=name,
deterministic=deterministic,
random_state=random_state)
#elif ia.is_iterable(value):
# ia.do_assert(len(value) == 2, "Expected tuple/list with 2 entries, got %d entries." % (len(value),))
if isinstance(value, numbers.Integral):
ch.do_assert(
-255 <= value <= 255,
"Expected value to have range [-255, 255], got value %d." %
(value, ))
self.value = randomizer.Deterministic(value)
else:
self.value = randomizer.DiscreteUniform(value[0], value[1])
#elif isinstance(value, StochasticParameter):
# self.value = value
#else:
# raise Exception("Expected float or int, tuple/list with 2 entries or StochasticParameter. Got %s." % (type(value),))
if per_channel in [True, False, 0, 1, 0.0, 1.0]:
self.per_channel = randomizer.Deterministic(int(per_channel))
else: # ia.is_single_number(per_channel):
#ia.do_assert(0 <= per_channel <= 1.0, "Expected bool, or number in range [0, 1.0] for per_channel, got %s." % (type(per_channel),))
self.per_channel = randomizer.Binomial(per_channel)
def __call__(self, images):
result = images
nb_images = len(images)
seeds = randomizer.current_random_state().randint(
0, 10**6, (nb_images, ))
for i in sm.xrange(nb_images):
image = images[i]
rs_image = randomizer.new_random_state(seeds[i])
per_channel = self.per_channel.draw_sample(random_state=rs_image)
if per_channel == 1:
nb_channels = image.shape[2]
samples = self.mul.draw_samples((nb_channels, ),
random_state=rs_image)
for c, sample in enumerate(samples):
ch.do_assert(sample >= 0)
image = F.multiply(image[c, ...],
torch.ones(1) * float(sample))
else:
sample = self.mul.draw_sample(random_state=rs_image)
ch.do_assert(sample >= 0)
image = F.multiply(image, torch.ones(1) * float(sample))
#image = meta.clip_augmented_image_(image, 0, 255) # TODO make value range more flexible
#image = meta.restore_augmented_image_dtype_(image, input_dtypes[i])
result[i] = image
return result
def __call__(self, images):
result = images
nb_images = len(images)
# Generate new seeds con
seeds = randomizer.current_random_state().randint(0, 10**6, (nb_images,))
rs_image = randomizer.new_random_state(seeds[0])
per_channel = self.per_channel.draw_sample(random_state=rs_image)
if per_channel == 1:
nb_channels = images.shape[1] #Considering (NXCXMXN)
for c in range(nb_channels):
samples = self.mul.draw_samples((nb_images,), random_state=rs_image).astype(
np.float32)
ch.do_assert(samples.all() >= 0)
# This is ugly how can I fix this transposition ? Put it somewhere else.
# TODO: document how to do the transposition.
result[ c:(c+1), ...] = F.multiply(images[c:(c+1), ...], torch.cuda.FloatTensor(samples))
else:
samples = self.mul.draw_samples((nb_images,), random_state=rs_image).astype(np.float32)
ch.do_assert(samples.all() >= 0)
result = F.multiply(images, torch.cuda.FloatTensor(samples))#torch.from_numpy(sample))
return result
def __call__(self, images):
#input_dtypes = copy_dtypes_for_restore(images, force_list=True)
result = images
nb_images = len(images)
random_seeds = randomizer.current_random_state().randint(0, 10 ** 6, (nb_images,))
for i in sm.xrange(nb_images):
image = images[i]
rs_image = randomizer.new_random_state(random_seeds[i])
per_channel = self.per_channel.draw_sample(random_state=rs_image)
if per_channel == 1:
nb_channels = image.shape[2]
samples = self.value.draw_samples((nb_channels,), random_state=rs_image)
for c, sample in enumerate(samples):
# TODO make value range more flexible
ch.do_assert(-255 <= sample <= 255)
image = F.add(image[c, ...].cuda(), torch.ones(1).cuda() * float(sample))
else:
sample = self.value.draw_sample(random_state=rs_image)
ch.do_assert(-255 <= sample <= 255) # TODO make value range more flexible
image = F.add(image.cuda(), torch.ones(1).cuda() * float(sample))
#image = meta.clip_augmented_image_(image, 0, 255) # TODO make value range more flexible
#image = meta.restore_augmented_image_dtype_(image, input_dtypes[i])
result[i] = image
return result
def __init__(self,
mul=1.0,
per_channel=False,
name=None,
deterministic=False,
random_state=None):
super(MultiplyCPU, self).__init__(name=name,
deterministic=deterministic,
random_state=random_state)
if isinstance(mul, numbers.Real) or isinstance(mul, numbers.Integral):
ch.do_assert(
mul >= 0.0,
"Expected multiplier to have range [0, inf), got value %.4f." %
(mul, ))
self.mul = randomizer.Deterministic(mul)
else:
ch.do_assert(
len(mul) == 2,
"Expected tuple/list with 2 entries, got %d entries." %
(len(mul), ))
self.mul = randomizer.Uniform(mul[0], mul[1])
if per_channel in [True, False, 0, 1, 0.0, 1.0]:
self.per_channel = randomizer.Deterministic(int(per_channel))
else:
ch.do_assert(0 <= per_channel <= 1.0)
self.per_channel = randomizer.Binomial(per_channel)
def __init__(self, mul=1.0, per_channel=False, name=None, deterministic=False, random_state=None):
super(MultiplyElementwise, self).__init__(name=name, deterministic=deterministic, random_state=random_state)
if ch.is_single_number(mul):
ch.do_assert(mul >= 0.0, "Expected multiplier to have range [0, inf), got value %.4f." % (mul,))
self.mul = randomizer.Deterministic(mul)
elif ch.is_iterable(mul):
ch.do_assert(len(mul) == 2, "Expected tuple/list with 2 entries, got %d entries." % (len(mul),))
self.mul = randomizer.Uniform(mul[0], mul[1])
else:
raise Exception("Expected float or int, tuple/list with 2 entries or StochasticParameter. Got %s." % (type(mul),))
if per_channel in [True, False, 0, 1, 0.0, 1.0]:
self.per_channel = randomizer.Deterministic(int(per_channel))
elif ch.is_single_number(per_channel):
ch.do_assert(0 <= per_channel <= 1.0)
self.per_channel = randomizer.Binomial(per_channel)
else:
raise Exception("Expected per_channel to be boolean or number or StochasticParameter")
def Dropout(p=0, per_channel=False, name=None, deterministic=False,
random_state=None):
"""
Augmenter that sets a certain fraction of pixels in images to zero.
Parameters
----------
p : float or tuple of two floats or StochasticParameter, optional(default=0)
The probability of any pixel being dropped (i.e. set to
zero).
* If a float, then that value will be used for all images. A value
of 1.0 would mean that all pixels will be dropped and 0.0 that
no pixels would be dropped. A value of 0.05 corresponds to 5
percent of all pixels dropped.
* If a tuple (a, b), then a value p will be sampled from the
range a <= p <= b per image and be used as the pixel's dropout
probability.
* If a StochasticParameter, then this parameter will be used to
determine per pixel whether it should be dropped (sampled value
of 0) or shouldn't (sampled value of 1).
per_channel : bool or float, optional(default=False)
Whether to use the same value (is dropped / is not dropped)
for all channels of a pixel (False) or to sample a new value for each
channel (True).
If this value is a float p, then for p percent of all images
`per_channel` will be treated as True, otherwise as False.
name : string, optional(default=None)
See `Augmenter.__init__()`
deterministic : bool, optional(default=False)
See `Augmenter.__init__()`
random_state : int or np.random.RandomState or None, optional(default=None)
See `Augmenter.__init__()`
Examples
--------
>>> aug = iaa.Dropout(0.02)
drops 2 percent of all pixels.
>>> aug = iaa.Dropout((0.0, 0.05))
drops in each image a random fraction of all pixels, where the fraction
is in the range 0.0 <= x <= 0.05.
>>> aug = iaa.Dropout(0.02, per_channel=True)
drops 2 percent of all pixels in a channel-wise fashion, i.e. it is unlikely
for any pixel to have all channels set to zero (black pixels).
>>> aug = iaa.Dropout(0.02, per_channel=0.5)
same as previous example, but the `per_channel` feature is only active
for 50 percent of all images.
"""
if ch.is_single_number(p):
p2 = randomizer.Binomial(1 - p)
elif isinstance(p, (tuple, list)):
ch.do_assert(len(p) == 2)
ch.do_assert(p[0] < p[1])
ch.do_assert(0 <= p[0] <= 1.0)
ch.do_assert(0 <= p[1] <= 1.0)
p2 = randomizer.Binomial(randomizer.Uniform(1 - p[1], 1 - p[0]))
else:
raise Exception("Expected p to be float or int or StochasticParameter, got %s." % (type(p),))
return MultiplyElementwise(p2, per_channel=per_channel, name=name, deterministic=deterministic, random_state=random_state)