bce_loss.py

# encoding: utf-8
"""
@author:  xingyu liao
@contact: sherlockliao01@gmail.com
"""

import torch
import torch.nn.functional as F


def ratio2weight(targets, ratio):
    pos_weights = targets * (1 - ratio)
    neg_weights = (1 - targets) * ratio
    weights = torch.exp(neg_weights + pos_weights)

    weights[targets > 1] = 0.0
    return weights


def cross_entropy_sigmoid_loss(pred_class_logits, gt_classes, sample_weight=None):
    loss = F.binary_cross_entropy_with_logits(pred_class_logits, gt_classes, reduction='none')

    if sample_weight is not None:
        targets_mask = torch.where(gt_classes.detach() > 0.5,
                                   torch.ones(1, device="cuda"), torch.zeros(1, device="cuda"))  # dtype float32
        weight = ratio2weight(targets_mask, sample_weight)
        loss = loss * weight

    with torch.no_grad():
        non_zero_cnt = max(loss.nonzero(as_tuple=False).size(0), 1)

    loss = loss.sum() / non_zero_cnt
    return loss