def calc_metric(candidate_img_blurred: np.ndarray, candidate_kpts: np.ndarray, candidate_length: int,
url_img_blurred: np.ndarray, url_kpts: np.ndarray, url_length: int, kpt_pos: tuple) -> float:
candidate_kpts, url_kpts = serial.deserialize_keypoints(candidate_kpts), serial.deserialize_keypoints(url_kpts)
m, n = kpt_pos[0], kpt_pos[1]
#print((m,n))
if(m == -1):
return 1
candidate_img_blurred, url_img_blurred = image.adjust_size(candidate_img_blurred, url_img_blurred)
c_x, c_y = candidate_img_blurred.shape
u_x, u_y = url_img_blurred.shape
x, y = np.array(candidate_kpts[n].pt) - np.array(url_kpts[m].pt)
if(x + c_x < 0 or x + u_x < 0):
return 1
if(y + c_y < 0 or y + u_y < 0):
return 1
candidate_img_blurred = image.align_text(candidate_img_blurred, (int(x), int(y)))
candidate_img_blurred, url_img_blurred = candidate_img_blurred.astype(int), url_img_blurred.astype(int)
img_diff = abs(candidate_img_blurred - url_img_blurred)
img_diff = img_diff.astype(int)
divisor = max(candidate_img_blurred.size, url_img_blurred.size)
diff = len(np.where(img_diff > 10)[0]) / float(divisor)
return diff
penalty = abs((float(candidate_length) - url_length)) / max(candidate_length, url_length)
diff = diff / (1.0 - penalty * 10)
return abs(diff)
def pol2cart(pts: np.ndarray, degrees: bool=False) -> np.ndarray:
"""Convert polar or cylindrical coordinates to Cartesian coordinates.
:param ndarray pts: array of polar points (rho, theta) or cylindrical \
points (rho, theta, phi)
:param bool degrees: if true results will be presented in degrees \
(default: False)
:returns: [x, y, (*z*)]
:rtype: ndarray
>>> pol2cart(np.array([[2**0.5, 45], [1, 90]]), degrees=True)
array([[ 1.00000000e+00, 1.00000000e+00],
[ 6.12323400e-17, 1.00000000e+00]])
>>> pol2cart(np.array([[2**0.5, 45, 1], [1, 90, 2]]), degrees=True)
array([[ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00],
[ 6.12323400e-17, 1.00000000e+00, 2.00000000e+00]])
"""
dim = element_dimension(pts, [2, 3])
if degrees:
pts = pts.astype(float)
pts[:, 1] = np.radians(pts[:, 1])
x = pts[:, 0] * np.cos(pts[:, 1])
y = pts[:, 0] * np.sin(pts[:, 1])
if dim == 2:
return np.c_[x, y]
else:
return np.c_[x, y, pts[:, 2]]
def find_stones(self, img: np.ndarray, rs=0, re=gsize, cs=0, ce=gsize, **kwargs):
""" The stones detection main algorithm, which is based on k-means pixel clustering.
Note: the three colors (E, B, W) must be present in the image for this statistical method to work.
Args:
img: ndarray
The Goban image.
rs: int - inclusive
re: int - exclusive
Row start and end indexes. Can be used to restrain check to a subregion.
cs: int - inclusive
ce: int - exclusive
Column start and end indexes. Can be used to restrain check to a subregion.
kwargs:
Allowing for keyword args enables multiple find methods to be called indifferently. See SfMeta.
Returns stones: ndarray
A matrix containing the detected stones in the desired subregion of the image,
or None if the result could not be trusted or something failed.
"""
if img.dtype is not np.float32:
img = img.astype(np.float32)
ratios, centers = self.cluster_colors(img, rs=rs, re=re, cs=cs, ce=ce)
stones = self.interpret_ratios(ratios, centers, r_start=rs, r_end=re, c_start=cs, c_end=ce)
if not self.check_density(stones):
return None # don't trust this result
return stones
def sphere2cart(pts: np.ndarray, degrees: bool=False) -> np.ndarray:
"""Convert spherical coordinates to Cartesian coordinates.
:param ndarray pts: array of spherical coordinates
:param bool degrees: if true results will be presented in degrees \
(default: False)
:returns: [x, y, z]
:rtype: ndarray
>>> sphere2cart(np.array([[1, 0, 90], [1, 90, 90]]), degrees=True)
array([[ 1.00000000e+00, 0.00000000e+00, 6.12323400e-17],
[ 6.12323400e-17, 1.00000000e+00, 6.12323400e-17]])
"""
element_dimension(pts, 3)
if degrees:
pts = pts.astype(float)
pts[:, 1:3] = np.radians(pts[:, 1:3])
r = pts[:, 0]
theta = pts[:, 1]
phi = pts[:, 2]
x = r * np.sin(phi) * np.cos(theta)
y = r * np.sin(phi) * np.sin(theta)
z = r * np.cos(phi)
return np.c_[x, y, z]
def _apply_single(self, data : np.ndarray):
"""
Read an image and returns it as a floating point array.
The optional page number allows images from files containing multiple
images to be addressed. Byte and short arrays are rescaled to
the range 0...1 (unsigned) or -1...1 (signed).
:rtype np.array in range 0...1 (unsigned) or -1...1 (signed)
"""
if data.dtype == np.dtype('uint8'):
data = data / 255.0
if data.dtype == np.dtype('int8'):
data = data / 127.0
elif data.dtype == np.dtype('uint16'):
data = data / 65536.0
elif data.dtype == np.dtype('int16'):
data = data / 32767.0
elif data.dtype in [np.dtype('f'), np.dtype('float32'), np.dtype('float64')]:
pass
elif data.dtype == bool:
data = data.astype(np.float32)
else:
raise Exception("unknown image type: {}".format(data.dtype))
if data.ndim == 3:
data = np.mean(data, axis=2)
return data, None
def __init__(self,
orientation: Quaternion = Quaternion(),
position: ndarray = array([0, 0, 0], dtype=float)) -> None:
self.orientation = orientation
assert position.shape == (3,) or position.shape == (3, 1), \
"The position should be an array of size (3,1)"
self.position = position.astype(dtype=float).reshape(3, 1)
def __init__(self,
sample_name: str,
ploidy_j: np.ndarray,
ploidy_genotyping_quality_j: np.ndarray,
contig_list: List[str],
check_germline_contig_ploidy_for_homo_sapiens: bool = True):
assert ploidy_j.ndim == 1
assert ploidy_j.size == len(contig_list)
assert ploidy_genotyping_quality_j.ndim == 1
assert ploidy_genotyping_quality_j.size == len(contig_list)
self.sample_name = sample_name
self.contig_list = contig_list
self.ploidy_j = ploidy_j.astype(types.small_uint)
self.ploidy_genotyping_quality_j = ploidy_genotyping_quality_j.astype(types.floatX)
self._contig_map = {contig: j for j, contig in enumerate(contig_list)}
if check_germline_contig_ploidy_for_homo_sapiens:
self.check_germline_contig_ploidy_for_homo_sapiens()
def save_pfm_texture(filename: str, tex: np.ndarray):
if tex.dtype != np.float32:
print('Input is not 32 bit precision: converting to 32 bits.')
tex = tex.astype(np.float32)
height, width = tex.shape[0], tex.shape[1]
with open(filename, 'wb+') as f:
f.write('{}\n'.format(HEADER_MAGIC).encode())
f.write('{} {}\n'.format(width, height).encode())
f.write('-1.0\n'.encode())
f.write(tex.tobytes())
def __init__(self,
name: str,
pose: Pose = Pose(),
init_pose: Pose = Pose(),
vel: ndarray = array([0.0, 0.0, 0.0]),
ang_vel: ndarray = array([0.0, 0.0, 0.0]),
ref_frame: str = None,
mass: float = 1.0,
inertia: matrix = identity(3),
ambient_color: list = None,
diffuse_color: list = None):
self.name = name
self.pose = pose
# the init pose is deep copied to be sure it won't be modified involuntary
self.init_pose = cp.deepcopy(init_pose)
self.vel = vel.astype(dtype=float).reshape(3, 1)
self.ang_vel = ang_vel.astype(dtype=float).reshape(3, 1)
if ref_frame is None:
self.ref_frame = None
self.frame = None
else:
self.ref_frame = ref_frame
self.frame = Frame("frame_{0}".format(self.name),
self.pose,
self.ref_frame)
self.mass = mass
self.inertia = inertia
if ambient_color is None:
self.ambient_color = [0.0, 0.0, 0.0, 0.0]
else:
self.ambient_color = ambient_color
if diffuse_color is None:
self.diffuse_color = [0.0, 0.0, 0.0, 0.0]
else:
self.diffuse_color = diffuse_color
def convert_to_feature(
self,
spectrogram: numpy.ndarray,
acoustic_feature: AcousticFeature,
):
acoustic_feature = acoustic_feature.astype_only_float(numpy.float64)
f_out = AcousticFeature(
f0=acoustic_feature.f0,
spectrogram=spectrogram.astype(numpy.float64),
aperiodicity=acoustic_feature.aperiodicity,
mfcc=acoustic_feature.mfcc,
voiced=acoustic_feature.voiced,
)
return f_out
def convert_to_audio(
self,
input: numpy.ndarray,
acoustic_feature: AcousticFeature,
sampling_rate: int,
):
acoustic_feature = acoustic_feature.astype_only_float(numpy.float64)
out = pyworld.synthesize(
f0=acoustic_feature.f0.ravel(),
spectrogram=input.astype(numpy.float64),
aperiodicity=acoustic_feature.aperiodicity,
fs=sampling_rate,
frame_period=self._param.acoustic_feature_param.frame_period,
)
return Wave(out, sampling_rate=sampling_rate)
def __init__(self,
sample_name: str,
n_j: np.ndarray,
contig_list: List[str]):
assert n_j.ndim == 1
assert n_j.size == len(contig_list)
self.sample_name = sample_name
self.contig_list = contig_list
# total count per contig
self.n_j = n_j.astype(types.med_uint)
# total count
self.n_total = np.sum(self.n_j)
self._contig_map = {contig: j for j, contig in enumerate(contig_list)}
def wgs_predict(self, questions: np.ndarray, labels: np.ndarray=None, y: np.ndarray=None):
"""Dumps the coordinate predictions into a text file"""
from csxdata.utilities.nputils import haversine
preds = self.network.predict(questions)
dist = haversine(self.dataframe.upscale(preds), y) if y is not None else None
preds = self.dataframe.upscale(preds).astype(str)
labels = np.atleast_2d(labels).T
preds = np.concatenate((labels, preds), axis=1)
if y is not None:
preds = np.concatenate((preds, y.astype(str), np.atleast_2d(dist).T.astype(str)), axis=1)
preds.tolist()
preds = ["\t".join(pr.tolist()) for pr in preds]
chain = "\n".join(preds)
chain = chain.replace(".", ",")
header = ["Azon", "Y", "X"]
if y is not None:
header += ["real_Y", "real_X", "Haversine"]
chain = "\t".join(header) + "\n" + chain
with open("logs/" + self.name + "_predictions.csv", "w") as f:
f.write(chain)
f.close()
def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
"""
Generate adversarial samples and return them in an array.
:param x: An array with the original inputs to be attacked.
:param y: An array with the original labels to be predicted.
:return: An array holding the adversarial examples.
"""
x = x.astype(ART_NUMPY_DTYPE)
preds = self.estimator.predict(x, batch_size=self.batch_size)
if self.estimator.nb_classes == 2 and preds.shape[1] == 1:
raise ValueError(
"This attack has not yet been tested for binary classification with a single output classifier."
)
if divmod(x.shape[2] - self.freq_dim, self.stride)[1] != 0:
raise ValueError(
"Incompatible value combination in image height/width, freq_dim and stride detected. "
"Adapt these parameters to fulfill the following conditions: "
"divmod(image_height - freq_dim, stride)[1] == 0 "
"and "
"divmod(image_width - freq_dim, stride)[1] == 0"
)
if y is None:
if self.targeted:
raise ValueError("Target labels `y` need to be provided for a targeted attack.")
# Use model predictions as correct outputs
logger.info("Using the model prediction as the correct label for SimBA.")
y_i = np.argmax(preds, axis=1)
else:
y_i = np.argmax(y, axis=1)
desired_label = y_i[0]
current_label = np.argmax(preds, axis=1)[0]
last_prob = preds.reshape(-1)[desired_label]
if self.estimator.channels_first:
nb_channels = x.shape[1]
else:
nb_channels = x.shape[3]
n_dims = np.prod(x.shape)
if self.attack == "px":
if self.order == "diag":
indices = self.diagonal_order(x.shape[2], nb_channels)[: self.max_iter]
elif self.order == "random":
indices = np.random.permutation(n_dims)[: self.max_iter]
indices_size = len(indices)
while indices_size < self.max_iter:
if self.order == "diag":
tmp_indices = self.diagonal_order(x.shape[2], nb_channels)
elif self.order == "random":
tmp_indices = np.random.permutation(n_dims)
indices = np.hstack((indices, tmp_indices))[: self.max_iter]
indices_size = len(indices)
elif self.attack == "dct":
indices = self._block_order(x.shape[2], nb_channels, initial_size=self.freq_dim, stride=self.stride)[
: self.max_iter
]
indices_size = len(indices)
while indices_size < self.max_iter:
tmp_indices = self._block_order(x.shape[2], nb_channels, initial_size=self.freq_dim, stride=self.stride)
indices = np.hstack((indices, tmp_indices))[: self.max_iter]
indices_size = len(indices)
def trans(var_z):
return self._block_idct(var_z, block_size=x.shape[2])
clip_min = -np.inf
clip_max = np.inf
if self.estimator.clip_values is not None:
clip_min, clip_max = self.estimator.clip_values
term_flag = 1
if self.targeted:
if desired_label != current_label:
term_flag = 0
else:
if desired_label == current_label:
term_flag = 0
nb_iter = 0
while term_flag == 0 and nb_iter < self.max_iter:
diff = np.zeros(n_dims).astype(ART_NUMPY_DTYPE)
diff[indices[nb_iter]] = self.epsilon
if self.attack == "dct":
left_preds = self.estimator.predict(
np.clip(x - trans(diff.reshape(x.shape)), clip_min, clip_max), batch_size=self.batch_size
)
elif self.attack == "px":
left_preds = self.estimator.predict(
np.clip(x - diff.reshape(x.shape), clip_min, clip_max), batch_size=self.batch_size
)
left_prob = left_preds.reshape(-1)[desired_label]
if self.attack == "dct":
right_preds = self.estimator.predict(
np.clip(x + trans(diff.reshape(x.shape)), clip_min, clip_max), batch_size=self.batch_size
)
elif self.attack == "px":
right_preds = self.estimator.predict(
np.clip(x + diff.reshape(x.shape), clip_min, clip_max), batch_size=self.batch_size
)
right_prob = right_preds.reshape(-1)[desired_label]
# Use (2 * int(self.targeted) - 1) to shorten code?
if self.targeted:
if left_prob > last_prob:
if left_prob > right_prob:
if self.attack == "dct":
x = np.clip(x - trans(diff.reshape(x.shape)), clip_min, clip_max)
elif self.attack == "px":
x = np.clip(x - diff.reshape(x.shape), clip_min, clip_max)
last_prob = left_prob
current_label = np.argmax(left_preds, axis=1)[0]
else:
if self.attack == "dct":
x = np.clip(x + trans(diff.reshape(x.shape)), clip_min, clip_max)
elif self.attack == "px":
x = np.clip(x + diff.reshape(x.shape), clip_min, clip_max)
last_prob = right_prob
current_label = np.argmax(right_preds, axis=1)[0]
else:
if right_prob > last_prob:
if self.attack == "dct":
x = np.clip(x + trans(diff.reshape(x.shape)), clip_min, clip_max)
elif self.attack == "px":
x = np.clip(x + diff.reshape(x.shape), clip_min, clip_max)
last_prob = right_prob
current_label = np.argmax(right_preds, axis=1)[0]
else:
if left_prob < last_prob:
if left_prob < right_prob:
if self.attack == "dct":
x = np.clip(x - trans(diff.reshape(x.shape)), clip_min, clip_max)
elif self.attack == "px":
x = np.clip(x - diff.reshape(x.shape), clip_min, clip_max)
last_prob = left_prob
current_label = np.argmax(left_preds, axis=1)[0]
else:
if self.attack == "dct":
x = np.clip(x + trans(diff.reshape(x.shape)), clip_min, clip_max)
elif self.attack == "px":
x = np.clip(x + diff.reshape(x.shape), clip_min, clip_max)
last_prob = right_prob
current_label = np.argmax(right_preds, axis=1)[0]
else:
if right_prob < last_prob:
if self.attack == "dct":
x = np.clip(x + trans(diff.reshape(x.shape)), clip_min, clip_max)
elif self.attack == "px":
x = np.clip(x + diff.reshape(x.shape), clip_min, clip_max)
last_prob = right_prob
current_label = np.argmax(right_preds, axis=1)[0]
if self.targeted:
if desired_label == current_label:
term_flag = 1
else:
if desired_label != current_label:
term_flag = 1
nb_iter = nb_iter + 1
if nb_iter < self.max_iter:
logger.info("SimBA (%s) %s attack succeed", self.attack, ["non-targeted", "targeted"][int(self.targeted)])
else:
logger.info("SimBA (%s) %s attack failed", self.attack, ["non-targeted", "targeted"][int(self.targeted)])
return x
def _float_to_int16(data: np.ndarray) -> np.ndarray:
amp = 2**15 - 1 # default amp for .DSB
max_data = np.abs(data).max()
min_data = np.abs(data).min()
data = (data - min_data) / (max_data - min_data) * amp
return data.astype(np.int16)
def __init__(self, arr: ndarray, prefs: ndarray):
assert arr.shape == prefs.shape
self._arr = arr.astype('bool')
self._prefs = prefs
self._score = None
def _cvt_ktoc_ndarray(self, data: np.ndarray) -> np.ndarray:
return (data.astype(np.float32) - 27315.0) / 100.0
def __scaleAndAdjustMinimum(self, unscaled: np.ndarray):
return unscaled.astype('float64') / self.__maxrange + self.__minimum
def get_raw_predictions(self, raw_embedding: ndarray) -> ndarray:
raw_embedding = raw_embedding.astype(numpy.float32) # For T5 fp16
embedding = torch.tensor(raw_embedding).to(self._device)
# Pass embeddings to model to produce predictions
yhat_conservation = self._conservation_model(embedding)
return yhat_conservation
def write(self, frame: np.ndarray):
self.process.stdin.write(frame.astype(np.uint8).tobytes())
def make_chromosome(ndarray: np.ndarray) -> Chromosome:
chromosome = Chromosome(ndarray.size)
chromosome.genes = np.copy(ndarray.astype(np.bool_))
return chromosome
def plot_confusion_matrix(
cm: np.ndarray,
class_names=None,
normalize=False,
title="confusion matrix",
fname=None,
show=True,
figsize=12,
fontsize=32,
colormap="Blues",
):
"""Render the confusion matrix and return matplotlib"s figure with it.
Normalization can be applied by setting `normalize=True`.
Args:
cm: numpy confusion matrix
class_names: class names
normalize: boolean flag to normalize confusion matrix
title: title
fname: filename to save confusion matrix
show: boolean flag for preview
figsize: matplotlib figure size
fontsize: matplotlib font size
colormap: matplotlib color map
Returns:
matplotlib figure
"""
plt.ioff()
cmap = plt.cm.__dict__[colormap]
if class_names is None:
class_names = [str(i) for i in range(len(np.diag(cm)))]
if normalize:
cm = cm.astype(np.float32) / cm.sum(axis=1)[:, np.newaxis]
plt.rcParams.update({"font.size": int(fontsize / np.log2(len(class_names)))})
figure = plt.figure(figsize=(figsize, figsize))
plt.title(title)
plt.imshow(cm, interpolation="nearest", cmap=cmap)
plt.colorbar()
tick_marks = np.arange(len(class_names))
plt.xticks(tick_marks, class_names, rotation=45, ha="right")
plt.yticks(tick_marks, class_names)
fmt = ".2f" if normalize else "d"
thresh = cm.max() / 2.0
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(
j,
i,
format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black",
)
plt.tight_layout()
plt.ylabel("True label")
plt.xlabel("Predicted label")
if fname is not None:
plt.savefig(fname=fname)
if show:
plt.show()
plt.ion()
return figure
def generate(self,
x: np.ndarray,
y: Optional[np.ndarray] = None,
**kwargs) -> np.ndarray:
"""
Generate adversarial samples and return them in an array.
:param x: An array with the original inputs.
:param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
(nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
(explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
:param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
features for which the mask is zero will not be adversarially perturbed.
:type mask: `np.ndarray`
:return: An array holding the adversarial examples.
"""
import tensorflow as tf # lgtm [py/repeated-import]
mask = self._get_mask(x, **kwargs)
# Ensure eps is broadcastable
self._check_compatibility_input_and_eps(x=x)
# Check whether random eps is enabled
self._random_eps()
# Set up targets
targets = self._set_targets(x, y)
# Create dataset
if mask is not None:
# Here we need to make a distinction: if the masks are different for each input, we need to index
# those for the current batch. Otherwise (i.e. mask is meant to be broadcasted), keep it as it is.
if len(mask.shape) == len(x.shape):
dataset = tf.data.Dataset.from_tensor_slices((
x.astype(ART_NUMPY_DTYPE),
targets.astype(ART_NUMPY_DTYPE),
mask.astype(ART_NUMPY_DTYPE),
)).batch(self.batch_size, drop_remainder=False)
else:
dataset = tf.data.Dataset.from_tensor_slices((
x.astype(ART_NUMPY_DTYPE),
targets.astype(ART_NUMPY_DTYPE),
np.array([mask.astype(ART_NUMPY_DTYPE)] * x.shape[0]),
)).batch(self.batch_size, drop_remainder=False)
else:
dataset = tf.data.Dataset.from_tensor_slices((
x.astype(ART_NUMPY_DTYPE),
targets.astype(ART_NUMPY_DTYPE),
)).batch(self.batch_size, drop_remainder=False)
# Start to compute adversarial examples
adv_x = x.astype(ART_NUMPY_DTYPE)
data_loader = iter(dataset)
# Compute perturbation with batching
for (batch_id, batch_all) in enumerate(
tqdm(data_loader,
desc="PGD - Batches",
leave=False,
disable=not self.verbose)):
if mask is not None:
(batch, batch_labels,
mask_batch) = batch_all[0], batch_all[1], batch_all[2]
else:
(batch, batch_labels,
mask_batch) = batch_all[0], batch_all[1], None
batch_index_1, batch_index_2 = batch_id * self.batch_size, (
batch_id + 1) * self.batch_size
# Compute batch_eps and batch_eps_step
if isinstance(self.eps, np.ndarray):
if len(self.eps.shape) == len(
x.shape) and self.eps.shape[0] == x.shape[0]:
batch_eps = self.eps[batch_index_1:batch_index_2]
batch_eps_step = self.eps_step[batch_index_1:batch_index_2]
else:
batch_eps = self.eps
batch_eps_step = self.eps_step
else:
batch_eps = self.eps
batch_eps_step = self.eps_step
for rand_init_num in range(max(1, self.num_random_init)):
if rand_init_num == 0:
# first iteration: use the adversarial examples as they are the only ones we have now
adv_x[batch_index_1:batch_index_2] = self._generate_batch(
x=batch,
targets=batch_labels,
mask=mask_batch,
eps=batch_eps,
eps_step=batch_eps_step)
else:
adversarial_batch = self._generate_batch(
x=batch,
targets=batch_labels,
mask=mask_batch,
eps=batch_eps,
eps_step=batch_eps_step)
attack_success = compute_success_array(
self.estimator,
batch,
batch_labels,
adversarial_batch,
self.targeted,
batch_size=self.batch_size,
)
# return the successful adversarial examples
adv_x[batch_index_1:batch_index_2][
attack_success] = adversarial_batch[attack_success]
logger.info(
"Success rate of attack: %.2f%%",
100 * compute_success(self.estimator,
x,
targets,
adv_x,
self.targeted,
batch_size=self.batch_size),
)
return adv_x
def from_note_representation(
array: ndarray,
resolution: int = DEFAULT_RESOLUTION,
program: int = 0,
is_drum: bool = False,
use_start_end: bool = False,
encode_velocity: bool = True,
default_velocity: int = 64,
) -> Music:
"""Decode note-based representation into a Music object.
Parameters
----------
array : ndarray
Array in note-based representation to decode. Will be casted to
integer if not of integer type.
resolution : int
Time steps per quarter note. Defaults to `muspy.DEFAULT_RESOLUTION`.
program : int, optional
Program number according to General MIDI specification [1].
Acceptable values are 0 to 127. Defaults to 0 (Acoustic Grand
Piano).
is_drum : bool, optional
A boolean indicating if it is a percussion track. Defaults to
False.
use_start_end : bool
Whether to use 'start' and 'end' to encode the timing rather than
'time' and 'duration'. Defaults to False.
encode_velocity : bool
Whether to encode note velocities. Defaults to True.
default_velocity : int
Default velocity value to use when decoding if `encode_velocity` is
False. Defaults to 64.
Returns
-------
:class:`muspy.Music` object
Decoded Music object.
References
----------
[1] https://www.midi.org/specifications/item/gm-level-1-sound-set
"""
if not np.issubdtype(array.dtype, np.integer):
array = array.astype(np.int)
notes = []
velocity = default_velocity
for note_tuple in array:
if encode_velocity:
velocity = note_tuple[3]
note = Note(
time=note_tuple[1],
duration=note_tuple[2] -
note_tuple[1] if use_start_end else note_tuple[2],
pitch=note_tuple[0],
velocity=velocity,
)
notes.append(note)
# Sort the notes
notes.sort(key=attrgetter("time", "pitch", "duration", "velocity"))
# Create the Track and Music objects
track = Track(program=program, is_drum=is_drum, notes=notes)
music = Music(resolution=resolution, tracks=[track])
return music
def do_pruning(self, in_channel_mask: np.ndarray, pruner: Pruner):
"""
Prune the block in place
:param in_channel_mask: a 0-1 vector indicates whether the corresponding channel should be pruned (0) or not (1)
:param pruner: the method to determinate the pruning threshold.
the pruner accepts a torch.Tensor as input and return a threshold
"""
# prune the pixel-wise conv layer
if self.pw:
pw_layer = self.conv[0]
in_channel_mask, input_gate_mask = prune_conv_layer(conv_layer=pw_layer[0],
bn_layer=pw_layer[1],
sparse_layer_in=self.input_gate if self.has_input_mask else None,
sparse_layer_out=pw_layer.sparse_layer,
in_channel_mask=None if self.has_input_mask else in_channel_mask,
pruner=pruner,
prune_output_mode="prune",
prune_mode='default')
if not np.any(in_channel_mask) or not np.any(input_gate_mask):
# no channel left
self._prune_whole_layer()
return self.output_channel
channel_select_idx = np.squeeze(np.argwhere(np.asarray(input_gate_mask)))
if len(channel_select_idx.shape) == 0:
# expand the single scalar to array
channel_select_idx = np.expand_dims(channel_select_idx, 0)
elif len(channel_select_idx.shape) == 1 and channel_select_idx.shape[0] == 0:
# nothing left
raise NotImplementedError("No layer left in input channel")
self.select.idx = channel_select_idx
if self.has_input_mask:
self.input_gate.do_pruning(input_gate_mask)
# update the hidden dim
self.hidden_dim = int(in_channel_mask.astype(np.int).sum())
# prune the output of the dw layer
# this in_channel_mask is supposed unchanged
dw_layer = self.conv[-5]
in_channel_mask, _ = prune_conv_layer(conv_layer=dw_layer[0],
bn_layer=dw_layer[1],
sparse_layer_in=None,
sparse_layer_out=dw_layer.sparse_layer,
in_channel_mask=in_channel_mask,
pruner=pruner,
prune_output_mode="same",
prune_mode='default')
# prune input of the dw-linear layer (the last layer)
out_channel_mask, _ = prune_conv_layer(conv_layer=self.conv[-4],
bn_layer=self.conv[-3],
sparse_layer_in=None,
sparse_layer_out=self.conv[-2] if isinstance(self.conv[-2],
SparseGate) else self.conv[
-3],
in_channel_mask=in_channel_mask,
pruner=pruner,
prune_output_mode="prune",
prune_mode='default')
# update output_channel
self.output_channel = int(out_channel_mask.astype(np.int).sum())
# if self.use_res_connect:
# do padding allowing adding with residual connection
# the output dim is unchanged
expander: ChannelExpand = self.conv[-1]
# note that the idx of the expander might be set in a pruned model
original_expander_idx = expander.idx
assert len(original_expander_idx) == len(out_channel_mask), "the output channel should be consistent"
pruned_expander_idx = original_expander_idx[out_channel_mask]
idx = np.squeeze(pruned_expander_idx)
expander.idx = idx
pass
# return the output dim
# the output dim is kept unchanged
return expander.channel_num
def _linear_disc_mat_eig(
cls,
N: np.ndarray,
y: np.ndarray,
ddof: int = 1,
classes: t.Optional[np.ndarray] = None,
class_freqs: t.Optional[np.ndarray] = None,
) -> t.Tuple[np.ndarray, np.ndarray]:
"""Compute eigenvalues/vecs of the Linear Discriminant Analysis Matrix.
More specifically, the eigenvalues and eigenvectors are calculated from
matrix S = (Scatter_Within_Mat)^(-1) * (Scatter_Between_Mat).
Check ``ft_can_cor`` documentation for more in-depth information about
this matrix.
Parameters
----------
ddof : :obj:`int`, optional
Degrees of freedom of covariance matrix calculated during LDA.
classes : :obj:`np.ndarray`, optional
Distinct classes of ``y``.
class_freqs : :obj:`np.ndarray`, optional
Absolute class frequencies of ``y``.
Returns
-------
:obj:`tuple`(:obj:`np.ndarray`, :obj:`np.ndarray`)
Eigenvalues and eigenvectors (in this order) of Linear
Discriminant Analysis Matrix.
"""
def compute_scatter_within(
N: np.ndarray,
y: np.ndarray,
class_val_freq: t.Tuple[np.ndarray, np.ndarray],
ddof: int = 1) -> np.ndarray:
"""Compute Scatter Within matrix. Check doc above for more info."""
scatter_within = np.array(
[(cl_frq - 1.0) * np.cov(
N[y == cl_val, :], rowvar=False, ddof=ddof)
for cl_val, cl_frq in zip(*class_val_freq)]).sum(axis=0)
return scatter_within
def compute_scatter_between(
N: np.ndarray, y: np.ndarray,
class_val_freq: t.Tuple[np.ndarray, np.ndarray]) -> np.ndarray:
"""Compute Scatter Between matrix. The doc above has more info."""
class_vals, class_freqs = class_val_freq
class_means = np.array(
[N[y == cl_val, :].mean(axis=0) for cl_val in class_vals])
relative_centers = class_means - N.mean(axis=0)
scatter_between = np.array([
cl_frq * np.outer(rc, rc)
for cl_frq, rc in zip(class_freqs, relative_centers)
]).sum(axis=0)
return scatter_between
if classes is None or class_freqs is None:
class_val_freq = np.unique(y, return_counts=True)
else:
class_val_freq = (classes, class_freqs)
N = N.astype(float)
scatter_within = compute_scatter_within(
N, y, class_val_freq, ddof=ddof)
scatter_between = compute_scatter_between(N, y, class_val_freq)
try:
scatter_within_inv = np.linalg.inv(scatter_within)
return np.linalg.eig(
np.matmul(scatter_within_inv, scatter_between))
except (np.linalg.LinAlgError, ValueError):
return np.array([np.nan]), np.array([np.nan])
def score(D:np.ndarray, target:np.ndarray, k=5,
metric:str='distance', test_set_ind:np.ndarray=None, verbose:int=0):
"""Perform `k`-nearest neighbor classification.
Use the ``n x n`` symmetric distance matrix `D` and target class
labels `target` to perform a `k`-NN experiment (leave-one-out
cross-validation or evaluation of test set; see parameter `test_set_ind`).
Ties are broken by the nearest neighbor.
Parameters
----------
D : ndarray
The ``n x n`` symmetric distance (similarity) matrix.
target : ndarray (of dtype=int)
The ``n x 1`` target class labels (ground truth).
k : int or array_like (of dtype=int), optional (default: 5)
Neighborhood size for `k`-NN classification.
For each value in `k`, one `k`-NN experiment is performed.
HINT: Providing more than one value for `k` is a cheap means to perform
multiple `k`-NN experiments at once. Try e.g. ``k=[1, 5, 20]``.
metric : {'distance', 'similarity'}, optional (default: 'distance')
Define, whether matrix `D` is a distance or similarity matrix
test_sed_ind : ndarray, optional (default: None)
Define data points to be hold out as part of a test set. Can be:
- None : Perform a LOO-CV experiment
- ndarray : Hold out points indexed in this array as test set. Fit
model to remaining data. Evaluate model on test set.
verbose : int, optional (default: 0)
Increasing level of output (progress report).
Returns
-------
acc : ndarray (shape=(n_k x 1), dtype=float)
Classification accuracy (`n_k`... number of items in parameter `k`)
HINT: Refering to the above example...
... ``acc[0]`` gives the accuracy of the ``k=1`` experiment.
corr : ndarray (shape=(n_k x n), dtype=int)
Raw vectors of correctly classified items
HINT: ... ``corr[1, :]`` gives these items for the ``k=5`` experiment.
cmat : ndarray (shape=(n_k x n_t x n_t), dtype=int)
Confusion matrix (``n_t`` number of unique items in parameter target)
HINT: ... ``cmat[2, :, :]`` gives the confusion matrix of
the ``k=20`` experiment.
"""
# Check input sanity
log = Logging.ConsoleLogging()
IO._check_distance_matrix_shape(D)
IO._check_distance_matrix_shape_fits_labels(D, target)
IO._check_valid_metric_parameter(metric)
if metric == 'distance':
d_self = np.inf
sort_order = 1
if metric == 'similarity':
d_self = -np.inf
sort_order = -1
# Copy, because data is changed
D = D.copy()
target = target.astype(int)
if verbose:
log.message("Start k-NN experiment.")
# Handle LOO-CV vs. test set mode
if test_set_ind is None:
n = D.shape[0]
test_set_ind = range(n) # dummy
train_set_ind = n # dummy
else:
# number of points to be classified
n = test_set_ind.size
# Indices of training examples
train_set_ind = np.setdiff1d(np.arange(n), test_set_ind)
# Number of k-NN parameters
try:
k_length = k.size
except AttributeError as e:
if isinstance(k, int):
k = np.array([k])
k_length = k.size
elif isinstance(k, list):
k = np.array(k)
k_length = k.size
else:
raise e
acc = np.zeros((k_length, 1))
corr = np.zeros((k_length, D.shape[0]))
cl = np.sort(np.unique(target))
cmat = np.zeros((k_length, len(cl), len(cl)))
classes = target.copy()
for idx, cur_class in enumerate(cl):
# change labels to 0, 1, ..., len(cl)-1
classes[target == cur_class] = idx
cl = range(len(cl))
# Classify each point in test set
for i in test_set_ind:
seed_class = classes[i]
if issparse(D):
row = D.getrow(i).toarray().ravel()
else:
row = D[i, :]
row[i] = d_self
# Sort points in training set according to distance
# Randomize, in case there are several points of same distance
# (this is especially relevant for SNN rescaling)
rp = train_set_ind
rp = np.random.permutation(rp)
d2 = row[rp]
d2idx = np.argsort(d2, axis=0)[::sort_order]
idx = rp[d2idx]
# More than one k is useful for cheap multiple k-NN experiments at once
for j in range(k_length):
nn_class = classes[idx[0:k[j]]]
cs = np.bincount(nn_class.astype(int))
max_cs = np.where(cs == np.max(cs))[0]
# "tie": use nearest neighbor
if len(max_cs) > 1:
if seed_class == nn_class[0]:
acc[j] += 1/n
corr[j, i] = 1
cmat[j, seed_class, nn_class[0]] += 1
# majority vote
else:
if cl[max_cs[0]] == seed_class:
acc[j] += 1/n
corr[j, i] = 1
cmat[j, seed_class, cl[max_cs[0]]] += 1
if verbose:
log.message("Finished k-NN experiment.")
return acc, corr, cmat
def validate_by_template_matching(img: np.ndarray):
""" Detect 3d black boxes by template matching.
1. binarize the image. the voxels inside the black box will be false, and the outside will be true
2. The template is 7x7x2 with one section true and the other false.
3. sliding the template through the array, and detect the matching regions.
4. rotate the template to be 7x2x7 and 2x7x7, do the same detection.
5. if we can find multiple matchings in all the x,y,z direction, there is probably a black box.
Note that this is always effective. If the black box is large enough to reach both sides,
the detection will fail.
Parameters
-----------
img:
3D image volume.
"""
logging.info("validation by template matching...")
if np.issubdtype(img.dtype, np.floating):
logging.warning(
'do not support image with floating data type, will skip the validation.'
)
return True
img = img.astype(dtype=bool)
score_threshold = 0.9
num_threshold = 100
evidence_point = 0
temp = np.zeros((7, 7, 2), dtype=bool)
temp[:, :, 0] = True
result = match_template(img, temp)
if np.count_nonzero(result > score_threshold) > num_threshold:
evidence_point += 1
temp = np.zeros((7, 7, 2), dtype=bool)
temp[:, :, 1] = True
result = match_template(img, temp)
if np.count_nonzero(result > score_threshold) > num_threshold:
evidence_point += 1
temp = np.zeros((2, 7, 7), dtype=bool)
temp[0, :, :] = True
result = match_template(img, temp)
if np.count_nonzero(result > score_threshold) > num_threshold:
evidence_point += 1
temp = np.zeros((2, 7, 7), dtype=bool)
temp[1, :, :] = True
result = match_template(img, temp)
if np.count_nonzero(result > score_threshold) > num_threshold:
evidence_point += 1
temp = np.zeros((7, 2, 7), dtype=bool)
temp[:, 0, :] = True
result = match_template(img, temp)
if np.count_nonzero(result > score_threshold) > num_threshold:
evidence_point += 1
temp = np.zeros((7, 2, 7), dtype=bool)
temp[:, 1, :] = True
result = match_template(img, temp)
if np.count_nonzero(result > score_threshold) > num_threshold:
evidence_point += 1
if evidence_point > 4:
return False
else:
return True
def set_position(self, pos: ndarray) -> None:
self.pose.position = pos.astype(dtype=float).reshape(3, 1)
def generate(self,
x: np.ndarray,
y: Optional[np.ndarray] = None,
**kwargs) -> np.ndarray:
"""
Generate adversarial samples and return them in a Numpy array.
:param x: An array with the original inputs to be attacked.
:param y: An array with the original labels to be predicted.
:return: An array holding the adversarial examples.
"""
x_adv = x.astype(ART_NUMPY_DTYPE)
# Initialize variables
y_pred = self.estimator.predict(x, batch_size=self.batch_size)
pred_class = np.argmax(y_pred, axis=1)
if self.estimator.nb_classes == 2 and y_pred.shape[1] == 1:
raise ValueError(
"This attack has not yet been tested for binary classification with a single output classifier."
)
# Compute perturbation with implicit batching
for batch_id in trange(int(
np.ceil(x_adv.shape[0] / float(self.batch_size))),
desc="NewtonFool",
disable=not self.verbose):
batch_index_1, batch_index_2 = batch_id * self.batch_size, (
batch_id + 1) * self.batch_size
batch = x_adv[batch_index_1:batch_index_2]
# Main algorithm for each batch
norm_batch = np.linalg.norm(np.reshape(batch,
(batch.shape[0], -1)),
axis=1)
l_batch = pred_class[batch_index_1:batch_index_2]
l_b = to_categorical(l_batch,
self.estimator.nb_classes).astype(bool)
# Main loop of the algorithm
for _ in range(self.max_iter):
# Compute score
score = self.estimator.predict(batch)[l_b]
# Compute the gradients and norm
grads = self.estimator.class_gradient(batch, label=l_batch)
if grads.shape[1] == 1:
grads = np.squeeze(grads, axis=1)
norm_grad = np.linalg.norm(np.reshape(grads,
(batch.shape[0], -1)),
axis=1)
# Theta
theta = self._compute_theta(norm_batch, score, norm_grad)
# Perturbation
di_batch = self._compute_pert(theta, grads, norm_grad)
# Update xi and perturbation
batch += di_batch
# Apply clip
if self.estimator.clip_values is not None:
clip_min, clip_max = self.estimator.clip_values
x_adv[batch_index_1:batch_index_2] = np.clip(
batch, clip_min, clip_max)
else:
x_adv[batch_index_1:batch_index_2] = batch
logger.info(
"Success rate of NewtonFool attack: %.2f%%",
100 * compute_success(
self.estimator, x, y, x_adv, batch_size=self.batch_size),
)
return x_adv
def leaky_relu(values: np.ndarray, negative_slope: float):
values = values.astype(float)
for index, x in np.ndenumerate(values):
if x < 0:
values[index] = negative_slope * x
return values
def transform(image: np.ndarray) \
-> np.ndarray:
image = image.astype(np.float32)
image -= np.array([104, 117, 123], dtype=np.float32).reshape(1, 1, -1)
image = image.transpose((2, 0, 1))
return image
def generate(self,
x: np.ndarray,
y: Optional[np.ndarray] = None,
**kwargs) -> np.ndarray:
"""
Generate adversarial samples and return them in an array.
:param x: An array with the original inputs.
:param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
(nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
(explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
:return: An array holding the adversarial examples.
"""
y = check_and_transform_label_format(y, self.estimator.nb_classes)
if y is None:
if self.targeted:
raise ValueError(
"Target labels `y` need to be provided for a targeted attack."
)
y = get_labels_np_array(
self.estimator.predict(x, batch_size=self.batch_size)).astype(
np.int32)
x_adv = x.astype(ART_NUMPY_DTYPE)
for _ in trange(max(1, self.nb_random_init),
desc="AutoPGD - restart",
disable=not self.verbose):
# Determine correctly predicted samples
y_pred = self.estimator.predict(x_adv)
if self.targeted:
sample_is_robust = np.argmax(y_pred, axis=1) != np.argmax(
y, axis=1)
elif not self.targeted:
sample_is_robust = np.argmax(y_pred,
axis=1) == np.argmax(y, axis=1)
if np.sum(sample_is_robust) == 0:
break
x_robust = x_adv[sample_is_robust]
y_robust = y[sample_is_robust]
x_init = x[sample_is_robust]
n = x_robust.shape[0]
m = np.prod(x_robust.shape[1:]).item()
random_perturbation = (random_sphere(
n, m, self.eps,
self.norm).reshape(x_robust.shape).astype(ART_NUMPY_DTYPE))
x_robust = x_robust + random_perturbation
if self.estimator.clip_values is not None:
clip_min, clip_max = self.estimator.clip_values
x_robust = np.clip(x_robust, clip_min, clip_max)
perturbation = projection(x_robust - x_init, self.eps, self.norm)
x_robust = x_init + perturbation
# Compute perturbation with implicit batching
for batch_id in trange(
int(np.ceil(x_robust.shape[0] / float(self.batch_size))),
desc="AutoPGD - batch",
leave=False,
disable=not self.verbose,
):
self.eta = 2 * self.eps_step
batch_index_1, batch_index_2 = batch_id * self.batch_size, (
batch_id + 1) * self.batch_size
x_k = x_robust[batch_index_1:batch_index_2].astype(
ART_NUMPY_DTYPE)
x_init_batch = x_init[batch_index_1:batch_index_2].astype(
ART_NUMPY_DTYPE)
y_batch = y_robust[batch_index_1:batch_index_2]
p_0 = 0
p_1 = 0.22
W = [p_0, p_1]
while True:
p_j_p_1 = W[-1] + max(W[-1] - W[-2] - 0.03, 0.06)
if p_j_p_1 > 1:
break
W.append(p_j_p_1)
W = [math.ceil(p * self.max_iter) for p in W]
eta = self.eps_step
self.count_condition_1 = 0
for k_iter in trange(self.max_iter,
desc="AutoPGD - iteration",
leave=False,
disable=not self.verbose):
# Get perturbation, use small scalar to avoid division by 0
tol = 10e-8
# Get gradient wrt loss; invert it if attack is targeted
grad = self.estimator.loss_gradient(
x_k, y_batch) * (1 - 2 * int(self.targeted))
# Apply norm bound
if self.norm in [np.inf, "inf"]:
grad = np.sign(grad)
elif self.norm == 1:
ind = tuple(range(1, len(x_k.shape)))
grad = grad / (np.sum(
np.abs(grad), axis=ind, keepdims=True) + tol)
elif self.norm == 2:
ind = tuple(range(1, len(x_k.shape)))
grad = grad / (np.sqrt(
np.sum(np.square(grad), axis=ind, keepdims=True)) +
tol)
assert x_k.shape == grad.shape
perturbation = grad
# Apply perturbation and clip
z_k_p_1 = x_k + eta * perturbation
if self.estimator.clip_values is not None:
clip_min, clip_max = self.estimator.clip_values
z_k_p_1 = np.clip(z_k_p_1, clip_min, clip_max)
if k_iter == 0:
x_1 = z_k_p_1
perturbation = projection(x_1 - x_init_batch, self.eps,
self.norm)
x_1 = x_init_batch + perturbation
f_0 = self.estimator.loss(x=x_k,
y=y_batch,
reduction="mean")
f_1 = self.estimator.loss(x=x_1,
y=y_batch,
reduction="mean")
self.eta_w_j_m_1 = eta
self.f_max_w_j_m_1 = f_0
if f_1 >= f_0:
self.f_max = f_1
self.x_max = x_1
self.x_max_m_1 = x_init_batch
self.count_condition_1 += 1
else:
self.f_max = f_0
self.x_max = x_k.copy()
self.x_max_m_1 = x_init_batch
# Settings for next iteration k
x_k_m_1 = x_k.copy()
x_k = x_1
else:
perturbation = projection(z_k_p_1 - x_init_batch,
self.eps, self.norm)
z_k_p_1 = x_init_batch + perturbation
alpha = 0.75
x_k_p_1 = x_k + alpha * (z_k_p_1 - x_k) + (
1 - alpha) * (x_k - x_k_m_1)
if self.estimator.clip_values is not None:
clip_min, clip_max = self.estimator.clip_values
x_k_p_1 = np.clip(x_k_p_1, clip_min, clip_max)
perturbation = projection(x_k_p_1 - x_init_batch,
self.eps, self.norm)
x_k_p_1 = x_init_batch + perturbation
f_k_p_1 = self.estimator.loss(x=x_k_p_1,
y=y_batch,
reduction="mean")
if f_k_p_1 > self.f_max:
self.count_condition_1 += 1
self.x_max = x_k_p_1
self.x_max_m_1 = x_k
self.f_max = f_k_p_1
if k_iter in W:
rho = 0.75
condition_1 = self.count_condition_1 < rho * (
k_iter - W[W.index(k_iter) - 1])
condition_2 = self.eta_w_j_m_1 == eta and self.f_max_w_j_m_1 == self.f_max
if condition_1 or condition_2:
eta = eta / 2
x_k_m_1 = self.x_max_m_1
x_k = self.x_max
else:
x_k_m_1 = x_k
x_k = x_k_p_1.copy()
self.count_condition_1 = 0
self.eta_w_j_m_1 = eta
self.f_max_w_j_m_1 = self.f_max
else:
x_k_m_1 = x_k
x_k = x_k_p_1.copy()
y_pred_adv_k = self.estimator.predict(x_k)
if self.targeted:
sample_is_not_robust_k = np.invert(
np.argmax(y_pred_adv_k, axis=1) != np.argmax(y_batch,
axis=1))
elif not self.targeted:
sample_is_not_robust_k = np.invert(
np.argmax(y_pred_adv_k, axis=1) == np.argmax(y_batch,
axis=1))
x_robust[batch_index_1:batch_index_2][
sample_is_not_robust_k] = x_k[sample_is_not_robust_k]
x_adv[sample_is_robust] = x_robust
return x_adv
def normalise_confusion_matrix(cm: np.ndarray):
return cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
def __call__(
self,
x: np.ndarray,
y: Optional[np.ndarray] = None
) -> Tuple[np.ndarray, Optional[np.ndarray]]:
"""
Apply JPEG compression to sample `x`.
:param x: Sample to compress with shape of `NCHW`, `NHWC`, `NCFHW` or `NFHWC`. `x` values are expected to be in
the data range [0, 1] or [0, 255].
:param y: Labels of the sample `x`. This function does not affect them in any way.
:return: compressed sample.
"""
x_ndim = x.ndim
if x_ndim not in [4, 5]:
raise ValueError(
"Unrecognized input dimension. JPEG compression can only be applied to image and video data."
)
if x.min() < 0.0:
raise ValueError(
"Negative values in input `x` detected. The JPEG compression defence requires unnormalized input."
)
# Swap channel index
if self.channels_first and x_ndim == 4:
# image shape NCHW to NHWC
x = np.transpose(x, (0, 2, 3, 1))
elif self.channels_first and x_ndim == 5:
# video shape NCFHW to NFHWC
x = np.transpose(x, (0, 2, 3, 4, 1))
# insert temporal dimension to image data
if x_ndim == 4:
x = np.expand_dims(x, axis=1)
# Convert into uint8
if self.clip_values[1] == 1.0:
x = x * 255
x = x.astype("uint8")
# Set image mode
if x.shape[-1] == 1:
image_mode = "L"
elif x.shape[-1] == 3:
image_mode = "RGB"
else:
raise NotImplementedError(
"Currently only support `RGB` and `L` images.")
# Prepare grayscale images for "L" mode
if image_mode == "L":
x = np.squeeze(x, axis=-1)
# Compress one image at a time
x_jpeg = x.copy()
for idx in tqdm(np.ndindex(x.shape[:2]), desc="JPEG compression"):
x_jpeg[idx] = self._compress(x[idx], image_mode)
# Undo preparation grayscale images for "L" mode
if image_mode == "L":
x_jpeg = np.expand_dims(x_jpeg, axis=-1)
# Convert to ART dtype
if self.clip_values[1] == 1.0:
x_jpeg = x_jpeg / 255.0
x_jpeg = x_jpeg.astype(ART_NUMPY_DTYPE)
# remove temporal dimension for image data
if x_ndim == 4:
x_jpeg = np.squeeze(x_jpeg, axis=1)
# Swap channel index
if self.channels_first and x_jpeg.ndim == 4:
# image shape NHWC to NCHW
x_jpeg = np.transpose(x_jpeg, (0, 3, 1, 2))
elif self.channels_first and x_ndim == 5:
# video shape NFHWC to NCFHW
x_jpeg = np.transpose(x_jpeg, (0, 4, 1, 2, 3))
return x_jpeg, y
def translate(self, translation: ndarray) -> None:
self.position += translation.astype(float).reshape(3, 1)
def to_unique_bytes(a: np.ndarray, input_range: Tuple[float, float]) -> Any:
"""Returns an array of unique ubytes after applying LinearTransform on the input array."""
ubyte_range = (np.iinfo(np.ubyte).min, np.iinfo(np.ubyte).max)
a = LinearTransform.transform(data=a, input_range=input_range, output_range=ubyte_range)
return np.unique(a.astype(np.ubyte))
def thresholded_relu(values: np.ndarray, alpha: float):
values = values.astype(float)
for index, x in np.ndenumerate(values):
values[index] = values[index] * (x > alpha)
return values
def bfloat16_to_float32(data: np.ndarray,
dims: Union[int, Sequence[int]]) -> np.ndarray:
"""Converts ndarray of bf16 (as uint32) to f32 (as uint32)."""
shift = lambda x: x << 16 # noqa: E731
return shift(data.astype(np.int32)).reshape(dims).view(np.float32)
def __call__(self, img: np.ndarray, dtype: Optional[np.dtype] = None):
"""
Apply the transform to `img`, assuming `img` is a numpy array.
"""
assert isinstance(img, np.ndarray), "image must be numpy array."
return img.astype(self.dtype if dtype is None else dtype)
def plot_volumetric_roi(data: np.ndarray,
out_file: str,
view: str = 'orig',
skew: tuple = None,
facecolor: str = 'bright',
edgecolor: str = 'dark',
lrflip: bool = False) -> None:
""" Plot a volumetric ROI, color coding the cluster labels.
Parameters
----------
data : np.ndarray
3D data array of a NIfTI image
out_file : str
Output filename for the .svg figure
view : str, optional
The viewing angle of the ROI. Allowed values are {'orig',
'left', 'right', 'superior', 'inferior', 'anterior',
'posterior'}
skew : tuple, optional
The elevation and azimuth to rotate the ROI. If set to
(0, 0) it is difficult to see the 3D effect. If left empty,
default skewing elevation and azimuth values are used.
facecolor : str, optional
The colors the voxels will take, ordered by cluster-id. The
color palette from sns.color_palette() is used. Allowed values
are are {'deep', 'muted', 'pastel', 'bright', 'dark',
'colorblind'}
edgecolor : str, optional
The colors the borders around the voxels will take, ordered
by cluster-id. Same as facecolor.
lrflip : bool, optional
Left-right flip of the ROI for viewing purposes
"""
# Modify data matrix for optimal viewing
data = data[find_objects(
data.astype(bool).astype(int))[0]] # remove whitespace
if lrflip:
data = np.fliplr(data)
data = make_hollow(data) # Remove voxels that aren't visible
# Viewing angle
views = {
'orig': (30, 320),
'left': (0, 0),
'right': (0, 180),
'superior': (90, 90),
'inferior': (270, 90),
'anterior': (0, -90),
'posterior': (0, -270)
}
if not skew:
skew = (10, -10) if {'inferior', 'posterior'}.intersection(
{view}) else (10, 10)
elev, azim = tuple(map(sum, zip(views[view], skew)))
# Colors
face_palette = sns.color_palette(facecolor).as_hex()
edge_palette = sns.color_palette(edgecolor).as_hex()
facecolors = np.empty(data.shape, dtype=object)
edgecolors = np.empty(data.shape, dtype=object)
for i, k in enumerate(np.unique(data)[1:]):
facecolors[data == k] = face_palette[i]
edgecolors[data == k] = edge_palette[i]
# Plotting
plt.ioff()
fig = plt.figure(figsize=(30 / 2.54, 30 / 2.54))
fig.tight_layout()
ax = fig.gca(projection='3d')
ax.view_init(elev, azim)
ax.voxels(data, facecolors=facecolors, edgecolors=edgecolors)
dim = np.max(data.shape) / 2
ax.set_xlim(right=dim * 2)
ax.set_ylim(top=dim * 2)
ax.set_zlim(top=dim * 2)
plt.axis('off')
fig.savefig(out_file, transparent=True, bbox_inches='tight', pad_inches=0)
plt.close(fig)
def write_nifti(
data: np.ndarray,
file_name: str,
affine: Optional[np.ndarray] = None,
target_affine: Optional[np.ndarray] = None,
resample: bool = True,
output_spatial_shape: Optional[Sequence[int]] = None,
mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
align_corners: bool = False,
dtype: Optional[np.dtype] = np.float64,
output_dtype: Optional[np.dtype] = np.float32,
) -> None:
"""
Write numpy data into NIfTI files to disk. This function converts data
into the coordinate system defined by `target_affine` when `target_affine`
is specified.
If the coordinate transform between `affine` and `target_affine` could be
achieved by simply transposing and flipping `data`, no resampling will
happen. otherwise this function will resample `data` using the coordinate
transform computed from `affine` and `target_affine`. Note that the shape
of the resampled `data` may subject to some rounding errors. For example,
resampling a 20x20 pixel image from pixel size (1.5, 1.5)-mm to (3.0,
3.0)-mm space will return a 10x10-pixel image. However, resampling a
20x20-pixel image from pixel size (2.0, 2.0)-mm to (3.0, 3.0)-mma space
will output a 14x14-pixel image, where the image shape is rounded from
13.333x13.333 pixels. In this case `output_spatial_shape` could be specified so
that this function writes image data to a designated shape.
When `affine` and `target_affine` are None, the data will be saved with an
identity matrix as the image affine.
This function assumes the NIfTI dimension notations.
Spatially it supports up to three dimensions, that is, H, HW, HWD for
1D, 2D, 3D respectively.
When saving multiple time steps or multiple channels `data`, time and/or
modality axes should be appended after the first three dimensions. For
example, shape of 2D eight-class segmentation probabilities to be saved
could be `(64, 64, 1, 8)`. Also, data in shape (64, 64, 8), (64, 64, 8, 1)
will be considered as a single-channel 3D image.
Args:
data: input data to write to file.
file_name: expected file name that saved on disk.
affine: the current affine of `data`. Defaults to `np.eye(4)`
target_affine: before saving
the (`data`, `affine`) as a Nifti1Image,
transform the data into the coordinates defined by `target_affine`.
resample: whether to run resampling when the target affine
could not be achieved by swapping/flipping data axes.
output_spatial_shape: spatial shape of the output image.
This option is used when resample = True.
mode: {``"bilinear"``, ``"nearest"``}
This option is used when ``resample = True``.
Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
This option is used when ``resample = True``.
Padding mode for outside grid values. Defaults to ``"border"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
align_corners: Geometrically, we consider the pixels of the input as squares rather than points.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.
If None, use the data type of input data. To be compatible with other modules,
the output data type is always ``np.float32``.
output_dtype: data type for saving data. Defaults to ``np.float32``.
"""
if not isinstance(data, np.ndarray):
raise AssertionError("input data must be numpy array.")
dtype = dtype or data.dtype
sr = min(data.ndim, 3)
if affine is None:
affine = np.eye(4, dtype=np.float64)
affine = to_affine_nd(sr, affine)
if target_affine is None:
target_affine = affine
target_affine = to_affine_nd(sr, target_affine)
if np.allclose(affine, target_affine, atol=1e-3):
# no affine changes, save (data, affine)
results_img = nib.Nifti1Image(data.astype(output_dtype),
to_affine_nd(3, target_affine))
nib.save(results_img, file_name)
return
# resolve orientation
start_ornt = nib.orientations.io_orientation(affine)
target_ornt = nib.orientations.io_orientation(target_affine)
ornt_transform = nib.orientations.ornt_transform(start_ornt, target_ornt)
data_shape = data.shape
data = nib.orientations.apply_orientation(data, ornt_transform)
_affine = affine @ nib.orientations.inv_ornt_aff(ornt_transform,
data_shape)
if np.allclose(_affine, target_affine, atol=1e-3) or not resample:
results_img = nib.Nifti1Image(data.astype(output_dtype),
to_affine_nd(3, target_affine))
nib.save(results_img, file_name)
return
# need resampling
affine_xform = AffineTransform(normalized=False,
mode=mode,
padding_mode=padding_mode,
align_corners=align_corners,
reverse_indexing=True)
transform = np.linalg.inv(_affine) @ target_affine
if output_spatial_shape is None:
output_spatial_shape, _ = compute_shape_offset(data.shape, _affine,
target_affine)
output_spatial_shape_ = list(output_spatial_shape)
if data.ndim > 3: # multi channel, resampling each channel
while len(output_spatial_shape_) < 3:
output_spatial_shape_ = output_spatial_shape_ + [1]
spatial_shape, channel_shape = data.shape[:3], data.shape[3:]
data_np = data.reshape(list(spatial_shape) + [-1])
data_np = np.moveaxis(data_np, -1, 0) # channel first for pytorch
data_torch = affine_xform(
torch.as_tensor(
np.ascontiguousarray(data_np).astype(dtype)).unsqueeze(0),
torch.as_tensor(np.ascontiguousarray(transform).astype(dtype)),
spatial_size=output_spatial_shape_[:3],
)
data_np = data_torch.squeeze(0).detach().cpu().numpy()
data_np = np.moveaxis(data_np, 0, -1) # channel last for nifti
data_np = data_np.reshape(
list(data_np.shape[:3]) + list(channel_shape))
else: # single channel image, need to expand to have batch and channel
while len(output_spatial_shape_) < len(data.shape):
output_spatial_shape_ = output_spatial_shape_ + [1]
data_torch = affine_xform(
torch.as_tensor(
np.ascontiguousarray(data).astype(dtype)[None, None]),
torch.as_tensor(np.ascontiguousarray(transform).astype(dtype)),
spatial_size=output_spatial_shape_[:len(data.shape)],
)
data_np = data_torch.squeeze(0).squeeze(0).detach().cpu().numpy()
results_img = nib.Nifti1Image(data_np.astype(output_dtype),
to_affine_nd(3, target_affine))
nib.save(results_img, file_name)
return
def _generate_bss(
self, x_batch: np.ndarray, y_batch: np.ndarray,
c_batch: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
Generate adversarial examples for a batch of inputs with a specific batch of constants.
:param x_batch: A batch of original examples.
:param y_batch: A batch of targets (0-1 hot).
:param c_batch: A batch of constants.
:return: A tuple of best elastic distances, best labels, best attacks.
"""
def compare(object1, object2):
return object1 == object2 if self.targeted else object1 != object2
x_orig = x_batch.astype(ART_NUMPY_DTYPE)
fine_tuning = np.full(x_batch.shape[0], False, dtype=bool)
prev_loss = 1e6 * np.ones(x_batch.shape[0])
prev_l2dist = np.zeros(x_batch.shape[0])
# Resize and initialize Adam
if self.use_resize:
x_orig = self._resize_image(x_orig, self._init_size,
self._init_size, True)
assert (x_orig != 0).any()
x_adv = x_orig.copy()
else:
x_orig = x_batch
self._reset_adam(np.prod(self.estimator.input_shape).item())
if x_batch.shape == self._current_noise.shape:
self._current_noise.fill(0)
else:
self._current_noise = np.zeros(x_batch.shape,
dtype=ART_NUMPY_DTYPE)
x_adv = x_orig.copy()
# Initialize best distortions, best changed labels and best attacks
best_dist = np.inf * np.ones(x_adv.shape[0])
best_label = -np.inf * np.ones(x_adv.shape[0])
best_attack = np.array([x_adv[i] for i in range(x_adv.shape[0])])
for iter_ in range(self.max_iter):
logger.debug("Iteration step %i out of %i", iter_, self.max_iter)
# Upscaling for very large number of iterations
if self.use_resize:
if iter_ == 2000:
x_adv = self._resize_image(x_adv, 64, 64)
x_orig = zoom(
x_orig,
[
1,
x_adv.shape[1] / x_orig.shape[1],
x_adv.shape[2] / x_orig.shape[2],
x_adv.shape[3] / x_orig.shape[3],
],
)
elif iter_ == 10000:
x_adv = self._resize_image(x_adv, 128, 128)
x_orig = zoom(
x_orig,
[
1,
x_adv.shape[1] / x_orig.shape[1],
x_adv.shape[2] / x_orig.shape[2],
x_adv.shape[3] / x_orig.shape[3],
],
)
# Compute adversarial examples and loss
x_adv = self._optimizer(x_adv, y_batch, c_batch)
preds, l2dist, loss = self._loss(x_orig, x_adv, y_batch, c_batch)
# Reset Adam if a valid example has been found to avoid overshoot
mask_fine_tune = (~fine_tuning) & (loss == l2dist) & (prev_loss !=
prev_l2dist)
fine_tuning[mask_fine_tune] = True
self._reset_adam(self.adam_mean.size,
np.repeat(mask_fine_tune,
x_adv[0].size)) # type: ignore
prev_l2dist = l2dist
# Abort early if no improvement is obtained
if self.abort_early and iter_ % self._early_stop_iters == 0:
if (loss > 0.9999 * prev_loss).all():
break
prev_loss = loss
# Adjust the best result
labels_batch = np.argmax(y_batch, axis=1)
for i, (dist,
pred) in enumerate(zip(l2dist, np.argmax(preds, axis=1))):
if dist < best_dist[i] and compare(pred, labels_batch[i]):
best_dist[i] = dist
best_attack[i] = x_adv[i]
best_label[i] = pred
# Resize images to original size before returning
best_attack = np.array(best_attack)
if self.use_resize:
if not self.estimator.channels_first:
best_attack = zoom(
best_attack,
[
1,
int(x_batch.shape[1]) / best_attack.shape[1],
int(x_batch.shape[2]) / best_attack.shape[2],
1,
],
)
else:
best_attack = zoom(
best_attack,
[
1,
1,
int(x_batch.shape[2]) / best_attack.shape[2],
int(x_batch.shape[2]) / best_attack.shape[3],
],
)
return best_dist, best_label, best_attack
def fit_transform_params(column: np.ndarray, backend: "Backend") -> dict: # noqa
compute = backend.df_engine.compute
return {
"mean": compute(column.astype(np.float32).mean()),
"std": compute(column.astype(np.float32).std()),
}
def peak_detect(values: np.ndarray, threshold: Union[float, int]=None, min_distance: Union[float, int]=10,
max_number: int=None, search_region: Tuple[float, float]=(0.0, 1.0),
find_min_instead: bool=False) -> Tuple[np.ndarray, np.ndarray]:
"""Find the peaks or valleys of a 1D signal.
Uses the difference (np.diff) in signal to find peaks. Current limitations include:
1) Only for use in 1-D data; 2D may be possible with the gradient function.
2) Will not detect peaks at the very edge of array (i.e. 0 or -1 index)
Parameters
----------
values : array-like
Signal values to search for peaks within.
threshold : int, float
The value the peak must be above to be considered a peak. This removes "peaks"
that are in a low-value region.
If passed an int, the actual value is the threshold.
E.g. when passed 15, any peak less with a value <15 is removed.
If passed a float, it will threshold as a percent. Must be between 0 and 1.
E.g. when passed 0.4, any peak <40% of the maximum value will be removed.
min_distance : int, float
If passed an int, parameter is the number of elements apart a peak must be from neighboring peaks.
If passed a float, must be between 0 and 1 and represents the ratio of the profile to exclude.
E.g. if passed 0.05 with a 1000-element profile, the minimum peak width will be 0.05*1000 = 50 elements.
max_number : int
Specify up to how many peaks will be returned. E.g. if 3 is passed in and 5 peaks are found, only the 3 largest
peaks will be returned.
find_min_instead : bool
If False (default), peaks will be returned.
If True, valleys will be returned.
Returns
-------
max_vals : numpy.array
The values of the peaks found.
max_idxs : numpy.array
The x-indices (locations) of the peaks.
Raises
------
ValueError
If float not between 0 and 1 passed to threshold.
"""
peak_vals = [] # a list to hold the y-values of the peaks. Will be converted to a numpy array
peak_idxs = [] # ditto for x-values (index) of y data.
if find_min_instead:
values = -values
"""Limit search to search region"""
left_end = search_region[0]
if is_float_like(left_end):
left_index = int(left_end*len(values))
elif is_int_like(left_end):
left_index = left_end
else:
raise ValueError(f"{left_end} must be a float or int")
right_end = search_region[1]
if is_float_like(right_end):
right_index = int(right_end * len(values))
elif is_int_like(right_end):
right_index = right_end
else:
raise ValueError(f"{right_end} must be a float or int")
# minimum peak spacing calc
if isinstance(min_distance, float):
if 0 > min_distance >= 1:
raise ValueError("When min_peak_width is passed a float, value must be between 0 and 1")
else:
min_distance = int(min_distance * len(values))
values = values[left_index:right_index]
"""Determine threshold value"""
if isinstance(threshold, float) and threshold < 1:
data_range = values.max() - values.min()
threshold = threshold * data_range + values.min()
elif isinstance(threshold, float) and threshold >= 1:
raise ValueError("When threshold is passed a float, value must be less than 1")
elif threshold is None:
threshold = values.min()
"""Take difference"""
values_diff = np.diff(values.astype(float)) # y and y_diff must be converted to signed type.
"""Find all potential peaks"""
for idx in range(len(values_diff) - 1):
# For each item of the diff array, check if:
# 1) The y-value is above the threshold.
# 2) The value of y_diff is positive (negative for valley search), it means the y-value changed upward.
# 3) The next y_diff value is zero or negative (or positive for valley search); a positive-then-negative diff value means the value
# is a peak of some kind. If the diff is zero it could be a flat peak, which still counts.
# 1)
if values[idx + 1] < threshold:
continue
y1_gradient = values_diff[idx] > 0
y2_gradient = values_diff[idx + 1] <= 0
# 2) & 3)
if y1_gradient and y2_gradient:
# If the next value isn't zero it's a single-pixel peak. Easy enough.
if values_diff[idx + 1] != 0:
peak_vals.append(values[idx + 1])
peak_idxs.append(idx + 1 + left_index)
# elif idx >= len(y_diff) - 1:
# pass
# Else if the diff value is zero, it could be a flat peak, or it could keep going up; we don't know yet.
else:
# Continue on until we find the next nonzero diff value.
try:
shift = 0
while values_diff[(idx + 1) + shift] == 0:
shift += 1
if (idx + 1 + shift) >= (len(values_diff) - 1):
break
# If the next diff is negative (or positive for min), we've found a peak. Also put the peak at the center of the flat
# region.
is_a_peak = values_diff[(idx + 1) + shift] < 0
if is_a_peak:
peak_vals.append(values[int((idx + 1) + np.round(shift / 2))])
peak_idxs.append((idx + 1 + left_index) + np.round(shift / 2))
except IndexError:
pass
# convert to numpy arrays
peak_vals = np.array(peak_vals)
peak_idxs = np.array(peak_idxs)
"""Enforce the min_peak_distance by removing smaller peaks."""
# For each peak, determine if the next peak is within the min peak width range.
index = 0
while index < len(peak_idxs) - 1:
# If the second peak is closer than min_peak_distance to the first peak, find the larger peak and remove the other one.
if peak_idxs[index] > peak_idxs[index + 1] - min_distance:
if peak_vals[index] > peak_vals[index + 1]:
idx2del = index + 1
else:
idx2del = index
peak_vals = np.delete(peak_vals, idx2del)
peak_idxs = np.delete(peak_idxs, idx2del)
else:
index += 1
"""If Maximum Number passed, return only up to number given based on a sort of peak values."""
if max_number is not None and len(peak_idxs) > max_number:
sorted_peak_vals = peak_vals.argsort() # sorts low to high
peak_vals = peak_vals[sorted_peak_vals[-max_number:]]
peak_idxs = peak_idxs[sorted_peak_vals[-max_number:]]
# If we were looking for minimums, convert the values back to the original sign
if find_min_instead:
peak_vals = -peak_vals
return peak_vals, peak_idxs
def elu(values: np.ndarray, alpha: float):
values = values.astype(float)
for index, x in np.ndenumerate(values):
if x < 0:
values[index] = alpha * (np.exp(x) - 1)
return values
def __call__(self, img: np.ndarray):
assert isinstance(img, np.ndarray), "image must be numpy array."
return img.astype(self.dtype)
def diff(img1: np.ndarray, img2: np.ndarray) -> np.ndarray:
img1_ = img1.astype(int)
img2_ = img2.astype(int)
diff = img1_ - img2_
return (np.abs(diff)).astype('uint8')
def structural_similarity(array1: np.ndarray, array2: np.ndarray, filter_size: int = 40, filter_sigma: float = 1., \
k1: float = 0.01, k2: float = 0.03, max_val: int = 255) -> (np.float64, np.ndarray):
"""
Compares two given array's with the Structural Similarity (SSIM) index method.
References
----------
Zhou Wang et al: https://github.com/obartra/ssim/blob/master/assets/ssim.pdf
https://en.wikipedia.org/wiki/Structural_similarity
https://scikit-image.org/docs/dev/auto_examples/transform/plot_ssim.html
https://blog.csdn.net/weixin_42096901/article/details/90172534
https://github.com/tensorflow/models/blob/master/research/compression/image_encoder/msssim.py
Parameters
----------
array1 numpy.ndarray array to compare against the other given array
array2 numpy.ndarray array to compare against the other given array
filter_size int gaussian kernel size
filter_sigma float gaussian kernel intensity
k1 float default value
k2 float default value
max_val int dynamic range of the image 255 for 8-bit 65535 for 16-bit
Raises
------
ValueError if given array's doesn't match each others shape (height, width, channels)
Returns
-------
mssim numpy.ndarray array (map) of the contrast sensitivity
ssim numpy.float64 mean of the contrast sensitivity number between -1 and 1
"""
if array1.shape != array2.shape:
msg = 'Input arrays must have the same shape'
raise ValueError(msg)
array1 = array1.astype(np.float64)
array2 = array2.astype(np.float64)
height, width = array1.shape[:2]
if filter_size: # is 1 or more
# filter size can't be larger than height or width of arrays.
size = min(filter_size, height, width)
# scale down sigma if a smaller filter size is used.
sigma = size * filter_sigma / filter_size if filter_size else 0
window = gaussian_kernel(shape=(size, ), sigma=(sigma, ))
# convolve = convolve_array
# compute weighted means
mu1 = convolve_array(array1, window)
mu2 = convolve_array(array2, window)
# compute weighted covariances
sigma_11 = convolve_array(np.multiply(array1, array1), window)
sigma_22 = convolve_array(np.multiply(array2, array2), window)
sigma_12 = convolve_array(np.multiply(array1, array2), window)
else: # Empty blur kernel so no need to convolve.
mu1, mu2 = array1, array2
sigma_11 = np.multiply(array1, array1)
sigma_22 = np.multiply(array2, array2)
sigma_12 = np.multiply(array1, array2)
# compute weighted variances
mu_11 = np.multiply(mu1, mu1)
mu_22 = np.multiply(mu2, mu2)
mu_12 = np.multiply(mu1, mu2)
sigma_11 = np.subtract(sigma_11, mu_11)
sigma_22 = np.subtract(sigma_22, mu_22)
sigma_12 = np.subtract(sigma_12, mu_12)
# constants to avoid numerical instabilities close to zero
c1 = (k1 * max_val)**2.
c2 = (k2 * max_val)**2.
v1 = 2.0 * sigma_12 + c2
v2 = sigma_11 + sigma_22 + c2
# Numerator of SSIM
num_ssim = (2. * mu_12 + c1) * v1 # -> np.ndarray
# Denominator of SSIM
den_ssim = (mu_11 + mu_22 + c1) * v2 # -> np.ndarray
# SSIM (contrast sensitivity)
ssim = num_ssim / den_ssim # -> np.ndarray
# MeanSSIM
mssim = np.mean(ssim) # -> np.float64
return mssim, ssim # -> (np.float64, np.ndarray)
def linear_sum_assignment_iter(cost_matrix: np.ndarray):
"""Iterate over the solutions to the linear sum assignment problem
in increasing order of cost
The method used for the first solution is the Hungarian algorithm,
also known as the Munkres or Kuhn-Munkres algorithm.
The method used to find the second best solution and iterate over
the solutions is described in [1]_, but is implemented in a slightly
different, Dijkstra-like way. The states are represented as
.. math: (cost(N_k), r, M_k, N_k, I_k, O_k)
with :math:``r`` a random number used to avoid comparing the assignments.
This function can also solve a generalization of the classic assignment
problem where the cost matrix is rectangular. If it has more rows than
columns, then not every row needs to be assigned to a column, and vice
versa.
It supports infinite weights to represent edges that must never be
used.
Parameters
----------
cost_matrix : array
The cost matrix of the bipartite graph.
Yields
-------
row_ind, col_ind : array
An array of row indices and one of corresponding column indices giving
the optimal assignment. The cost of the assignment can be computed
as ``cost_matrix[row_ind, col_ind].sum()``. The row indices will be
sorted; in the case of a square cost matrix they will be equal to
``numpy.arange(cost_matrix.shape[0])``.
Examples
--------
>>> cost = np.array([[4, 1, 3], [2, 0, float("inf")], [3, 2, 2]])
>>> from matchmaker import linear_sum_assignment_iter
>>> it = linear_sum_assignment_iter(cost)
>>> row_ind, col_ind = next(it)
>>> col_ind
array([1, 0, 2])
>>> cost[row_ind, col_ind].sum()
5.0
>>> row_ind, col_ind = next(it)
>>> col_ind
array([0, 1, 2])
>>> cost[row_ind, col_ind].sum()
6.0
References
----------
.. [1] Chegireddy, Chandra R., and Horst W. Hamacher. "Algorithms for finding
k-best perfect matchings." Discrete applied mathematics 18, no. 2
(1987): 155-165.
"""
cost_matrix = np.asarray(cost_matrix)
# make the cost_matrix square as the algorithm only works
# for perfect matchings
# any value other than 0 would work
# see <https://cstheory.stackexchange.com/a/42168/43172>
n, m = cost_matrix.shape
if n < m:
cost_matrix = np.concatenate(
(cost_matrix, np.zeros((m - n, m), dtype=cost_matrix.dtype)), axis=0
)
elif n > m:
cost_matrix = np.concatenate(
(cost_matrix, np.zeros((n, n - m), dtype=cost_matrix.dtype)), axis=1
)
def transform(a, b):
"""transforms a solution assignment (a, b)
back to the original matrix
"""
mask = (a < n) & (b < m)
return a[mask], b[mask]
cost = lambda assignment: cost_matrix[assignment].sum()
# linear_sum_assignment doesn't require the matrix to be square,
# but second_best_assignment needs the best solution for a square matrix
M1 = linear_sum_assignment(cost_matrix_without_inf(cost_matrix))
if not np.isposinf(cost(M1)):
yield transform(*M1)
else:
return
# from now, use a copy of cost_matrix
# with dtype float
cost_matrix = cost_matrix.astype(float)
I1 = []
O1 = []
N1 = _second_best_assignment(cost_matrix, M1, I1, O1)
if N1 is None:
return
Q = [(cost(N1), np.random.rand(), M1, N1, I1, O1)]
while Q:
_, _, M, N, I, O = heappop(Q)
yield transform(*N)
e = _choose_in_difference(M, N)
Ip, Op = I + [e], O
Np = _second_best_assignment(cost_matrix, M, Ip, Op)
if Np is not None:
heappush(Q, (cost(Np), np.random.rand(), M, Np, Ip, Op))
Ik, Ok = I, O + [e]
Nk = _second_best_assignment(cost_matrix, N, Ik, Ok)
if Nk is not None:
heappush(Q, (cost(Nk), np.random.rand(), N, Nk, Ik, Ok))
