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import torch
from torch import nn
import torch.nn.functional as F
def l1(_crossattn_similarity, mask, token_idx = [1,2]):
similarity = torch.cat(_crossattn_similarity,1)[1]
similarity = similarity.mean(0).permute(2,0,1)
# similarity = similarity.softmax(dim = 0)
return (similarity[token_idx] * mask.cuda()).sum()
def bce(_crossattn_similarity, mask, token_idx = [1,2]):
similarity = torch.cat(_crossattn_similarity,1)[1]
similarity = similarity.mean(0).permute(2,0,1)
# similarity = similarity.softmax(dim = 0)
return -sum([
F.binary_cross_entropy_with_logits(x - 1.0, mask.cuda())
for x in similarity[token_idx]
])
def softmax(_crossattn_similarity, mask, token_idx = [1,2]):
similarity = torch.cat(_crossattn_similarity,1)[1]
similarity = similarity.mean(0).permute(2,0,1)
similarity = similarity[1:].softmax(dim = 0) # Comute the softmax to obtain probability values
token_idx = [x - 1 for x in token_idx]
score = similarity[token_idx].sum(dim = 0) # Sum up all relevant tokens to get pixel-wise probability of belonging to the correct class
score = torch.log(score) # Obtain log-probabilities per-pixel
return (score * mask.cuda()).sum() # Sum up log-probabilities (equivalent to multiplying P-values) for all pixels inside of the mask |