tracker/model/aggregate.py

import torch
import torch.nn.functional as F


# Soft aggregation from STM
def aggregate(prob, dim, return_logits=False):
    new_prob = torch.cat(
        [torch.prod(1 - prob, dim=dim, keepdim=True), prob], dim
    ).clamp(1e-7, 1 - 1e-7)
    logits = torch.log((new_prob / (1 - new_prob)))
    prob = F.softmax(logits, dim=dim)

    if return_logits:
        return logits, prob
    else:
        return prob