# Copyright (c) Alibaba, Inc. and its affiliates.
import math
import warnings
import mmcv
import torch
import torch.nn as nn
from easycv.framework.errors import NotImplementedError
from easycv.models.detection.utils import bbox_overlaps
from easycv.models.loss.utils import weighted_loss
from ..registry import LOSSES
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def iou_loss(pred, target, linear=False, mode='log', eps=1e-6):
"""IoU loss.
Computing the IoU loss between a set of predicted bboxes and target bboxes.
The loss is calculated as negative log of IoU.
Args:
pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
shape (n, 4).
target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
linear (bool, optional): If True, use linear scale of loss instead of
log scale. Default: False.
mode (str): Loss scaling mode, including "linear", "square", and "log".
Default: 'log'
eps (float): Eps to avoid log(0).
Return:
torch.Tensor: Loss tensor.
"""
assert mode in ['linear', 'square', 'log']
if linear:
mode = 'linear'
warnings.warn('DeprecationWarning: Setting "linear=True" in '
'iou_loss is deprecated, please use "mode=`linear`" '
'instead.')
ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps)
if mode == 'linear':
loss = 1 - ious
elif mode == 'square':
loss = 1 - ious**2
elif mode == 'log':
loss = -ious.log()
else:
raise NotImplementedError
return loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def giou_loss(pred, target, eps=1e-7):
r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding
Box Regression <https://arxiv.org/abs/1902.09630>`_.
Args:
pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
shape (n, 4).
target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
eps (float): Eps to avoid log(0).
Return:
Tensor: Loss tensor.
"""
gious = bbox_overlaps(pred, target, mode='giou', is_aligned=True, eps=eps)
loss = 1 - gious
return loss
[docs]@LOSSES.register_module
class YOLOX_IOULoss(nn.Module):
[docs] def __init__(self, reduction='none', loss_type='iou'):
super(YOLOX_IOULoss, self).__init__()
self.reduction = reduction
self.loss_type = loss_type
[docs] def forward(self, pred, target):
assert pred.shape[0] == target.shape[0]
if target.dtype != pred.dtype:
target = target.to(pred.dtype)
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
iou = (area_i) / (area_p + area_g - area_i + 1e-16)
if self.loss_type == 'iou':
loss = 1 - iou**2
elif self.loss_type == 'siou':
# angle cost
c_h = torch.max(pred[:, 1], target[:, 1]) - torch.min(
pred[:, 1], target[:, 1])
c_w = torch.max(pred[:, 0], target[:, 0]) - torch.min(
pred[:, 0], target[:, 0])
sigma = torch.sqrt(((pred[:, :2] - target[:, :2])**2).sum(dim=1))
angle_cost = 2 * (c_h * c_w) / (sigma**2)
# distance cost
gamma = 2 - angle_cost
# gamma = 1
c_dw = torch.max(pred[:, 0], target[:, 0]) - torch.min(
pred[:, 0], target[:, 0]) + (pred[:, 2] + target[:, 2]) / 2
c_dh = torch.max(pred[:, 1], target[:, 1]) - torch.min(
pred[:, 1], target[:, 1]) + (pred[:, 3] + target[:, 3]) / 2
p_x = ((target[:, 0] - pred[:, 0]) / c_dw)**2
p_y = ((target[:, 1] - pred[:, 1]) / c_dh)**2
dist_cost = 2 - torch.exp(-gamma * p_x) - torch.exp(-gamma * p_y)
# shape cost
theta = 4
w_w = torch.abs(pred[:, 2] - target[:, 2]) / torch.max(
pred[:, 2], target[:, 2])
w_h = torch.abs(pred[:, 3] - target[:, 3]) / torch.max(
pred[:, 3], target[:, 3])
shape_cost = torch.pow((1 - torch.exp(-w_w)), theta) + torch.pow(
(1 - torch.exp(-w_h)), theta)
loss = 1 - iou + (dist_cost + shape_cost) / 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_i) / area_c.clamp(1e-16)
loss = 1 - giou.clamp(min=-1.0, max=1.0)
elif self.loss_type == 'diou':
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))
convex_dis = torch.pow(c_br[:, 0] - c_tl[:, 0], 2) + torch.pow(
c_br[:, 1] - c_tl[:, 1], 2) + 1e-7 # convex diagonal squared
center_dis = (torch.pow(pred[:, 0] - target[:, 0], 2) +
torch.pow(pred[:, 1] - target[:, 1], 2)
) # center diagonal squared
diou = iou - (center_dis / convex_dis)
loss = 1 - diou.clamp(min=-1.0, max=1.0)
elif self.loss_type == 'ciou':
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))
convex_dis = torch.pow(c_br[:, 0] - c_tl[:, 0], 2) + torch.pow(
c_br[:, 1] - c_tl[:, 1], 2) + 1e-7 # convex diagonal squared
center_dis = (torch.pow(pred[:, 0] - target[:, 0], 2) +
torch.pow(pred[:, 1] - target[:, 1], 2)
) # center diagonal squared
v = (4 / math.pi**2) * torch.pow(
torch.atan(target[:, 2] / torch.clamp(target[:, 3], min=1e-7))
- torch.atan(pred[:, 2] / torch.clamp(pred[:, 3], min=1e-7)),
2)
with torch.no_grad():
alpha = v / ((1 + 1e-7) - iou + v)
ciou = iou - (center_dis / convex_dis + alpha * v)
loss = 1 - ciou.clamp(min=-1.0, max=1.0)
elif self.loss_type == 'eiou':
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))
convex_dis = torch.pow(c_br[:, 0] - c_tl[:, 0], 2) + torch.pow(
c_br[:, 1] - c_tl[:, 1], 2) + 1e-7 # convex diagonal squared
center_dis = (torch.pow(pred[:, 0] - target[:, 0], 2) +
torch.pow(pred[:, 1] - target[:, 1], 2)
) # center diagonal squared
dis_w = torch.pow(pred[:, 2] - target[:, 2], 2)
dis_h = torch.pow(pred[:, 3] - target[:, 3], 2)
C_w = torch.pow(c_br[:, 0] - c_tl[:, 0], 2) + 1e-7
C_h = torch.pow(c_br[:, 1] - c_tl[:, 1], 2) + 1e-7
eiou = iou - (center_dis / convex_dis) - (dis_w / C_w) - (
dis_h / C_h)
loss = 1 - eiou.clamp(min=-1.0, max=1.0)
if self.reduction == 'mean':
loss = loss.mean()
elif self.reduction == 'sum':
loss = loss.sum()
return loss
[docs]@LOSSES.register_module()
class IoULoss(nn.Module):
"""IoULoss.
Computing the IoU loss between a set of predicted bboxes and target bboxes.
Args:
linear (bool): If True, use linear scale of loss else determined
by mode. Default: False.
eps (float): Eps to avoid log(0).
reduction (str): Options are "none", "mean" and "sum".
loss_weight (float): Weight of loss.
mode (str): Loss scaling mode, including "linear", "square", and "log".
Default: 'log'
"""
[docs] def __init__(self,
linear=False,
eps=1e-6,
reduction='mean',
loss_weight=1.0,
mode='log'):
super(IoULoss, self).__init__()
assert mode in ['linear', 'square', 'log']
if linear:
mode = 'linear'
warnings.warn('DeprecationWarning: Setting "linear=True" in '
'IOULoss is deprecated, please use "mode=`linear`" '
'instead.')
self.mode = mode
self.linear = linear
self.eps = eps
self.reduction = reduction
self.loss_weight = loss_weight
[docs] def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None,
**kwargs):
"""Forward function.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Defaults to None. Options are "none", "mean" and "sum".
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if (weight is not None) and (not torch.any(weight > 0)) and (
reduction != 'none'):
if pred.dim() == weight.dim() + 1:
weight = weight.unsqueeze(1)
return (pred * weight).sum() # 0
if weight is not None and weight.dim() > 1:
# TODO: remove this in the future
# reduce the weight of shape (n, 4) to (n,) to match the
# iou_loss of shape (n,)
assert weight.shape == pred.shape
weight = weight.mean(-1)
loss = self.loss_weight * iou_loss(
pred,
target,
weight,
mode=self.mode,
eps=self.eps,
reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss
[docs]@LOSSES.register_module()
class GIoULoss(nn.Module):
[docs] def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
super(GIoULoss, self).__init__()
self.eps = eps
self.reduction = reduction
self.loss_weight = loss_weight
[docs] def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None,
**kwargs):
if weight is not None and not torch.any(weight > 0):
if pred.dim() == weight.dim() + 1:
weight = weight.unsqueeze(1)
return (pred * weight).sum() # 0
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if weight is not None and weight.dim() > 1:
# TODO: remove this in the future
# reduce the weight of shape (n, 4) to (n,) to match the
# giou_loss of shape (n,)
assert weight.shape == pred.shape
weight = weight.mean(-1)
loss = self.loss_weight * giou_loss(
pred,
target,
weight,
eps=self.eps,
reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss