def noise(outlier_classes: List[int],
generated_noise: torch.Tensor,
norm: torch.Tensor,
nom_class: int,
train_set: Dataset,
gt: bool = False) -> Dataset:
"""
Creates a dataset based on the nominal classes of a given dataset and generated noise anomalies.
:param outlier_classes: a list of all outlier class indices.
:param generated_noise: torch tensor of noise images (might also be Outlier Exposure based noise) (n x c x h x w).
:param norm: torch tensor of nominal images (n x c x h x w).
:param nom_class: the index of the class that is considered nominal.
:param train_set: some training dataset.
:param gt: whether to provide ground-truth maps as well, atm not available!
:return: a modified dataset, with training data consisting of nominal samples and artificial anomalies.
"""
if gt:
raise ValueError('No GT mode for pure noise available!')
anom = generated_noise.clamp(0, 255).byte()
data = torch.cat((norm, anom))
targets = torch.cat((torch.ones(norm.size(0)) * nom_class,
torch.ones(anom.size(0)) * outlier_classes[0]))
train_set.data = data
train_set.targets = targets
return train_set
def malformed_normal(outlier_classes: List[int],
generated_noise: torch.Tensor,
norm: torch.Tensor,
nom_class: int,
train_set: Dataset,
gt: bool = False,
brightness_threshold: float = 0.11 * 255) -> Dataset:
"""
Creates a dataset based on the nominal classes of a given dataset and generated noise anomalies.
Unlike above, the noise images are not directly utilized as anomalies, but added to nominal samples to
create malformed normal anomalies.
:param outlier_classes: a list of all outlier class indices.
:param generated_noise: torch tensor of noise images (might also be Outlier Exposure based noise) (n x c x h x w).
:param norm: torch tensor of nominal images (n x c x h x w).
:param nom_class: the index of the class that is considered nominal.
:param train_set: some training dataset.
:param gt: whether to provide ground-truth maps as well.
:param brightness_threshold: if the average brightness (averaged over color channels) of a pixel exceeds this
threshold, the noise image's pixel value is subtracted instead of added.
This avoids adding brightness values to bright pixels, where approximately no effect is achieved at all.
:return: a modified dataset, with training data consisting of nominal samples and artificial anomalies.
"""
assert (norm.dim() == 4
or norm.dim() == 3) and generated_noise.shape == norm.shape
norm_dim = norm.dim()
if norm_dim == 3:
norm, generated_noise = norm.unsqueeze(1), generated_noise.unsqueeze(
1) # assuming ch dim is skipped
anom = norm.clone()
# invert noise for bright regions (bright regions are considered being on average > brightness_threshold)
generated_noise = generated_noise.int()
bright_regions = norm.sum(1) > brightness_threshold * norm.shape[1]
for ch in range(norm.shape[1]):
gnch = generated_noise[:, ch]
gnch[bright_regions] = gnch[bright_regions] * -1
generated_noise[:, ch] = gnch
anom = (anom.int() + generated_noise).clamp(0, 255).byte()
data = torch.cat((norm, anom))
targets = torch.cat((torch.ones(norm.size(0)) * nom_class,
torch.ones(anom.size(0)) * outlier_classes[0]))
if norm_dim == 3:
data = data.squeeze(1)
train_set.data = data
train_set.targets = targets
if gt:
gtmaps = torch.cat((
torch.zeros_like(norm)[:, 0].float(), # 0 for nominal
(norm != anom).max(1)[0].clone().float()) # 1 for anomalous
)
if norm_dim == 4:
gtmaps = gtmaps.unsqueeze(1)
return train_set, gtmaps
else:
return train_set