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import numpy as np
from pysaliency.roc import general_roc
from pysaliency.numba_utils import auc_for_one_positive
import torch
def _general_auc(positives, negatives):
if len(positives) == 1:
return auc_for_one_positive(positives[0], negatives)
else:
return general_roc(positives, negatives)[0]
def log_likelihood(log_density, fixation_mask, weights=None):
#if weights is None:
# weights = torch.ones(log_density.shape[0])
weights = len(weights) * weights.view(-1, 1, 1) / weights.sum()
if isinstance(fixation_mask, torch.sparse.IntTensor):
dense_mask = fixation_mask.to_dense()
else:
dense_mask = fixation_mask
fixation_count = dense_mask.sum(dim=(-1, -2), keepdim=True)
ll = torch.mean(
weights * torch.sum(log_density * dense_mask, dim=(-1, -2), keepdim=True) / fixation_count
)
return (ll + np.log(log_density.shape[-1] * log_density.shape[-2])) / np.log(2)
def nss(log_density, fixation_mask, weights=None):
weights = len(weights) * weights.view(-1, 1, 1) / weights.sum()
if isinstance(fixation_mask, torch.sparse.IntTensor):
dense_mask = fixation_mask.to_dense()
else:
dense_mask = fixation_mask
fixation_count = dense_mask.sum(dim=(-1, -2), keepdim=True)
density = torch.exp(log_density)
mean, std = torch.std_mean(density, dim=(-1, -2), keepdim=True)
saliency_map = (density - mean) / std
nss = torch.mean(
weights * torch.sum(saliency_map * dense_mask, dim=(-1, -2), keepdim=True) / fixation_count
)
return nss
def auc(log_density, fixation_mask, weights=None):
weights = len(weights) * weights / weights.sum()
# TODO: This doesn't account for multiple fixations in the same location!
def image_auc(log_density, fixation_mask):
if isinstance(fixation_mask, torch.sparse.IntTensor):
dense_mask = fixation_mask.to_dense()
else:
dense_mask = fixation_mask
positives = torch.masked_select(log_density, dense_mask.type(torch.bool)).detach().cpu().numpy().astype(np.float64)
negatives = log_density.flatten().detach().cpu().numpy().astype(np.float64)
auc = _general_auc(positives, negatives)
return torch.tensor(auc)
return torch.mean(weights.cpu() * torch.tensor([
image_auc(log_density[i], fixation_mask[i]) for i in range(log_density.shape[0])
]))
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