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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# Copyright (c) Megvii Inc. All rights reserved.
import torch
import torch.nn as nn
class IOUloss(nn.Module):
def __init__(self, reduction="none", loss_type="iou"):
super(IOUloss, self).__init__()
self.reduction = reduction
self.loss_type = loss_type
def forward(self, pred, target):
assert pred.shape[0] == target.shape[0]
pred = pred.view(-1, 4)
target = target.view(-1, 4)
tl = torch.max(
(pred[:, :2] - pred[:, 2:] / 2), (target[:, :2] - target[:, 2:] / 2)
)
br = torch.min(
(pred[:, :2] + pred[:, 2:] / 2), (target[:, :2] + target[:, 2:] / 2)
)
area_p = torch.prod(pred[:, 2:], 1)
area_g = torch.prod(target[:, 2:], 1)
en = (tl < br).type(tl.type()).prod(dim=1)
area_i = torch.prod(br - tl, 1) * en
area_u = area_p + area_g - area_i
iou = (area_i) / (area_u + 1e-16)
if self.loss_type == "iou":
loss = 1 - iou ** 2
elif self.loss_type == "giou":
c_tl = torch.min(
(pred[:, :2] - pred[:, 2:] / 2), (target[:, :2] - target[:, 2:] / 2)
)
c_br = torch.max(
(pred[:, :2] + pred[:, 2:] / 2), (target[:, :2] + target[:, 2:] / 2)
)
area_c = torch.prod(c_br - c_tl, 1)
giou = iou - (area_c - area_u) / area_c.clamp(1e-16)
loss = 1 - giou.clamp(min=-1.0, max=1.0)
if self.reduction == "mean":
loss = loss.mean()
elif self.reduction == "sum":
loss = loss.sum()
return loss
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