306 lines
13 KiB
Python
306 lines
13 KiB
Python
|
# Loss functions
|
|||
|
|
|
|||
|
|
import torch
|
|||
|
|
import torch.nn as nn
|
|||
|
|
import numpy as np
|
|||
|
|
from utils.general import bbox_iou
|
|||
|
|
from utils.torch_utils import is_parallel
|
|||
|
|
|
|||
|
|
|
|||
|
|
def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
|
|||
|
|
# return positive, negative label smoothing BCE targets
|
|||
|
|
return 1.0 - 0.5 * eps, 0.5 * eps
|
|||
|
|
|
|||
|
|
|
|||
|
|
class BCEBlurWithLogitsLoss(nn.Module):
|
|||
|
|
# BCEwithLogitLoss() with reduced missing label effects.
|
|||
|
|
def __init__(self, alpha=0.05):
|
|||
|
|
super(BCEBlurWithLogitsLoss, self).__init__()
|
|||
|
|
self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss()
|
|||
|
|
self.alpha = alpha
|
|||
|
|
|
|||
|
|
def forward(self, pred, true):
|
|||
|
|
loss = self.loss_fcn(pred, true)
|
|||
|
|
pred = torch.sigmoid(pred) # prob from logits
|
|||
|
|
dx = pred - true # reduce only missing label effects
|
|||
|
|
# dx = (pred - true).abs() # reduce missing label and false label effects
|
|||
|
|
alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
|
|||
|
|
loss *= alpha_factor
|
|||
|
|
return loss.mean()
|
|||
|
|
|
|||
|
|
|
|||
|
|
class FocalLoss(nn.Module):
|
|||
|
|
# Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
|
|||
|
|
def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
|
|||
|
|
super(FocalLoss, self).__init__()
|
|||
|
|
self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
|
|||
|
|
self.gamma = gamma
|
|||
|
|
self.alpha = alpha
|
|||
|
|
self.reduction = loss_fcn.reduction
|
|||
|
|
self.loss_fcn.reduction = 'none' # required to apply FL to each element
|
|||
|
|
|
|||
|
|
def forward(self, pred, true):
|
|||
|
|
loss = self.loss_fcn(pred, true)
|
|||
|
|
# p_t = torch.exp(-loss)
|
|||
|
|
# loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
|
|||
|
|
|
|||
|
|
# TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
|
|||
|
|
pred_prob = torch.sigmoid(pred) # prob from logits
|
|||
|
|
p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
|
|||
|
|
alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
|
|||
|
|
modulating_factor = (1.0 - p_t) ** self.gamma
|
|||
|
|
loss *= alpha_factor * modulating_factor
|
|||
|
|
|
|||
|
|
if self.reduction == 'mean':
|
|||
|
|
return loss.mean()
|
|||
|
|
elif self.reduction == 'sum':
|
|||
|
|
return loss.sum()
|
|||
|
|
else: # 'none'
|
|||
|
|
return loss
|
|||
|
|
|
|||
|
|
|
|||
|
|
class QFocalLoss(nn.Module):
|
|||
|
|
# Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
|
|||
|
|
def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
|
|||
|
|
super(QFocalLoss, self).__init__()
|
|||
|
|
self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
|
|||
|
|
self.gamma = gamma
|
|||
|
|
self.alpha = alpha
|
|||
|
|
self.reduction = loss_fcn.reduction
|
|||
|
|
self.loss_fcn.reduction = 'none' # required to apply FL to each element
|
|||
|
|
|
|||
|
|
def forward(self, pred, true):
|
|||
|
|
loss = self.loss_fcn(pred, true)
|
|||
|
|
|
|||
|
|
pred_prob = torch.sigmoid(pred) # prob from logits
|
|||
|
|
alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
|
|||
|
|
modulating_factor = torch.abs(true - pred_prob) ** self.gamma
|
|||
|
|
loss *= alpha_factor * modulating_factor
|
|||
|
|
|
|||
|
|
if self.reduction == 'mean':
|
|||
|
|
return loss.mean()
|
|||
|
|
elif self.reduction == 'sum':
|
|||
|
|
return loss.sum()
|
|||
|
|
else: # 'none'
|
|||
|
|
return loss
|
|||
|
|
|
|||
|
|
class WingLoss(nn.Module):
|
|||
|
|
def __init__(self, w=10, e=2):
|
|||
|
|
super(WingLoss, self).__init__()
|
|||
|
|
# https://arxiv.org/pdf/1711.06753v4.pdf Figure 5
|
|||
|
|
self.w = w
|
|||
|
|
self.e = e
|
|||
|
|
self.C = self.w - self.w * np.log(1 + self.w / self.e)
|
|||
|
|
|
|||
|
|
def forward(self, x, t, sigma=1):
|
|||
|
|
weight = torch.ones_like(t)
|
|||
|
|
weight[torch.where(t==-1)] = 0
|
|||
|
|
diff = weight * (x - t)
|
|||
|
|
abs_diff = diff.abs()
|
|||
|
|
flag = (abs_diff.data < self.w).float()
|
|||
|
|
y = flag * self.w * torch.log(1 + abs_diff / self.e) + (1 - flag) * (abs_diff - self.C)
|
|||
|
|
return y.sum()
|
|||
|
|
|
|||
|
|
class LandmarksLoss(nn.Module):
|
|||
|
|
# BCEwithLogitLoss() with reduced missing label effects.
|
|||
|
|
def __init__(self, alpha=1.0):
|
|||
|
|
super(LandmarksLoss, self).__init__()
|
|||
|
|
self.loss_fcn = WingLoss()#nn.SmoothL1Loss(reduction='sum')
|
|||
|
|
self.alpha = alpha
|
|||
|
|
|
|||
|
|
def forward(self, pred, truel, mask):
|
|||
|
|
loss = self.loss_fcn(pred*mask, truel*mask)
|
|||
|
|
return loss / (torch.sum(mask) + 10e-14)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def compute_loss(p, targets, model): # predictions, targets, model
|
|||
|
|
device = targets.device
|
|||
|
|
lcls, lbox, lobj, lmark = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
|
|||
|
|
tcls, tbox, indices, anchors, tlandmarks, lmks_mask = build_targets(p, targets, model) # targets
|
|||
|
|
h = model.hyp # hyperparameters
|
|||
|
|
|
|||
|
|
# Define criteria
|
|||
|
|
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) # weight=model.class_weights)
|
|||
|
|
BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
|
|||
|
|
|
|||
|
|
landmarks_loss = LandmarksLoss(1.0)
|
|||
|
|
|
|||
|
|
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
|
|||
|
|
cp, cn = smooth_BCE(eps=0.0)
|
|||
|
|
|
|||
|
|
# Focal loss
|
|||
|
|
g = h['fl_gamma'] # focal loss gamma
|
|||
|
|
if g > 0:
|
|||
|
|
BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
|
|||
|
|
|
|||
|
|
# Losses
|
|||
|
|
nt = 0 # number of targets
|
|||
|
|
no = len(p) # number of outputs
|
|||
|
|
balance = [4.0, 1.0, 0.4] if no == 3 else [4.0, 1.0, 0.4, 0.1] # P3-5 or P3-6
|
|||
|
|
for i, pi in enumerate(p): # layer index, layer predictions
|
|||
|
|
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
|
|||
|
|
tobj = torch.zeros_like(pi[..., 0], device=device) # target obj
|
|||
|
|
|
|||
|
|
n = b.shape[0] # number of targets
|
|||
|
|
if n:
|
|||
|
|
nt += n # cumulative targets
|
|||
|
|
ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
|
|||
|
|
|
|||
|
|
# Regression
|
|||
|
|
pxy = ps[:, :2].sigmoid() * 2. - 0.5
|
|||
|
|
pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
|
|||
|
|
pbox = torch.cat((pxy, pwh), 1) # predicted box
|
|||
|
|
iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target)
|
|||
|
|
lbox += (1.0 - iou).mean() # iou loss
|
|||
|
|
|
|||
|
|
# Objectness
|
|||
|
|
tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio
|
|||
|
|
|
|||
|
|
# Classification
|
|||
|
|
if model.nc > 1: # cls loss (only if multiple classes)
|
|||
|
|
t = torch.full_like(ps[:, 13:], cn, device=device) # targets
|
|||
|
|
t[range(n), tcls[i]] = cp
|
|||
|
|
lcls += BCEcls(ps[:, 13:], t) # BCE
|
|||
|
|
|
|||
|
|
# Append targets to text file
|
|||
|
|
# with open('targets.txt', 'a') as file:
|
|||
|
|
# [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
|
|||
|
|
|
|||
|
|
#landmarks loss
|
|||
|
|
#plandmarks = ps[:,5:13].sigmoid() * 8. - 4.
|
|||
|
|
plandmarks = ps[:,5:13]
|
|||
|
|
|
|||
|
|
plandmarks[:, 0:2] = plandmarks[:, 0:2] * anchors[i]
|
|||
|
|
plandmarks[:, 2:4] = plandmarks[:, 2:4] * anchors[i]
|
|||
|
|
plandmarks[:, 4:6] = plandmarks[:, 4:6] * anchors[i]
|
|||
|
|
plandmarks[:, 6:8] = plandmarks[:, 6:8] * anchors[i]
|
|||
|
|
# plandmarks[:, 8:10] = plandmarks[:,8:10] * anchors[i]
|
|||
|
|
|
|||
|
|
lmark += landmarks_loss(plandmarks, tlandmarks[i], lmks_mask[i])
|
|||
|
|
|
|||
|
|
|
|||
|
|
lobj += BCEobj(pi[..., 4], tobj) * balance[i] # obj loss
|
|||
|
|
|
|||
|
|
s = 3 / no # output count scaling
|
|||
|
|
lbox *= h['box'] * s
|
|||
|
|
lobj *= h['obj'] * s * (1.4 if no == 4 else 1.)
|
|||
|
|
lcls *= h['cls'] * s
|
|||
|
|
lmark *= h['landmark'] * s
|
|||
|
|
|
|||
|
|
bs = tobj.shape[0] # batch size
|
|||
|
|
|
|||
|
|
loss = lbox + lobj + lcls + lmark
|
|||
|
|
return loss * bs, torch.cat((lbox, lobj, lcls, lmark, loss)).detach()
|
|||
|
|
|
|||
|
|
|
|||
|
|
def build_targets(p, targets, model):
|
|||
|
|
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
|
|||
|
|
det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
|
|||
|
|
na, nt = det.na, targets.shape[0] # number of anchors, targets
|
|||
|
|
tcls, tbox, indices, anch, landmarks, lmks_mask = [], [], [], [], [], []
|
|||
|
|
#gain = torch.ones(7, device=targets.device) # normalized to gridspace gain
|
|||
|
|
gain = torch.ones(15, device=targets.device)
|
|||
|
|
ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
|
|||
|
|
targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
|
|||
|
|
|
|||
|
|
g = 0.5 # bias
|
|||
|
|
off = torch.tensor([[0, 0],
|
|||
|
|
[1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
|
|||
|
|
# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
|
|||
|
|
], device=targets.device).float() * g # offsets
|
|||
|
|
|
|||
|
|
for i in range(det.nl):
|
|||
|
|
anchors, shape = det.anchors[i], p[i].shape
|
|||
|
|
gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
|
|||
|
|
#landmarks 10
|
|||
|
|
gain[6:14] = torch.tensor(p[i].shape)[[3, 2, 3, 2, 3, 2, 3, 2]] # xyxy gain
|
|||
|
|
|
|||
|
|
# Match targets to anchors
|
|||
|
|
t = targets * gain
|
|||
|
|
if nt:
|
|||
|
|
# Matches
|
|||
|
|
r = t[:, :, 4:6] / anchors[:, None] # wh ratio
|
|||
|
|
j = torch.max(r, 1. / r).max(2)[0] < model.hyp['anchor_t'] # compare
|
|||
|
|
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
|
|||
|
|
t = t[j] # filter
|
|||
|
|
|
|||
|
|
# Offsets
|
|||
|
|
gxy = t[:, 2:4] # grid xy
|
|||
|
|
gxi = gain[[2, 3]] - gxy # inverse
|
|||
|
|
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
|
|||
|
|
l, m = ((gxi % 1. < g) & (gxi > 1.)).T
|
|||
|
|
j = torch.stack((torch.ones_like(j), j, k, l, m))
|
|||
|
|
t = t.repeat((5, 1, 1))[j]
|
|||
|
|
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
|
|||
|
|
else:
|
|||
|
|
t = targets[0]
|
|||
|
|
offsets = 0
|
|||
|
|
|
|||
|
|
# Define
|
|||
|
|
b, c = t[:, :2].long().T # image, class
|
|||
|
|
gxy = t[:, 2:4] # grid xy
|
|||
|
|
gwh = t[:, 4:6] # grid wh
|
|||
|
|
gij = (gxy - offsets).long()
|
|||
|
|
gi, gj = gij.T # grid xy indices
|
|||
|
|
|
|||
|
|
# Append
|
|||
|
|
a = t[:, 14].long() # anchor indices
|
|||
|
|
#indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
|
|||
|
|
indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid
|
|||
|
|
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
|
|||
|
|
anch.append(anchors[a]) # anchors
|
|||
|
|
tcls.append(c) # class
|
|||
|
|
|
|||
|
|
#landmarks
|
|||
|
|
lks = t[:,6:14]
|
|||
|
|
#lks_mask = lks > 0
|
|||
|
|
#lks_mask = lks_mask.float()
|
|||
|
|
lks_mask = torch.where(lks < 0, torch.full_like(lks, 0.), torch.full_like(lks, 1.0))
|
|||
|
|
|
|||
|
|
#应该是关键点的坐标除以anch的宽高才对，便于模型学习。使用gwh会导致不同关键点的编码不同，没有统一的参考标准
|
|||
|
|
|
|||
|
|
lks[:, [0, 1]] = (lks[:, [0, 1]] - gij)
|
|||
|
|
lks[:, [2, 3]] = (lks[:, [2, 3]] - gij)
|
|||
|
|
lks[:, [4, 5]] = (lks[:, [4, 5]] - gij)
|
|||
|
|
lks[:, [6, 7]] = (lks[:, [6, 7]] - gij)
|
|||
|
|
# lks[:, [8, 9]] = (lks[:, [8, 9]] - gij)
|
|||
|
|
|
|||
|
|
'''
|
|||
|
|
#anch_w = torch.ones(5, device=targets.device).fill_(anchors[0][0])
|
|||
|
|
#anch_wh = torch.ones(5, device=targets.device)
|
|||
|
|
anch_f_0 = (a == 0).unsqueeze(1).repeat(1, 5)
|
|||
|
|
anch_f_1 = (a == 1).unsqueeze(1).repeat(1, 5)
|
|||
|
|
anch_f_2 = (a == 2).unsqueeze(1).repeat(1, 5)
|
|||
|
|
lks[:, [0, 2, 4, 6, 8]] = torch.where(anch_f_0, lks[:, [0, 2, 4, 6, 8]] / anchors[0][0], lks[:, [0, 2, 4, 6, 8]])
|
|||
|
|
lks[:, [0, 2, 4, 6, 8]] = torch.where(anch_f_1, lks[:, [0, 2, 4, 6, 8]] / anchors[1][0], lks[:, [0, 2, 4, 6, 8]])
|
|||
|
|
lks[:, [0, 2, 4, 6, 8]] = torch.where(anch_f_2, lks[:, [0, 2, 4, 6, 8]] / anchors[2][0], lks[:, [0, 2, 4, 6, 8]])
|
|||
|
|
|
|||
|
|
lks[:, [1, 3, 5, 7, 9]] = torch.where(anch_f_0, lks[:, [1, 3, 5, 7, 9]] / anchors[0][1], lks[:, [1, 3, 5, 7, 9]])
|
|||
|
|
lks[:, [1, 3, 5, 7, 9]] = torch.where(anch_f_1, lks[:, [1, 3, 5, 7, 9]] / anchors[1][1], lks[:, [1, 3, 5, 7, 9]])
|
|||
|
|
lks[:, [1, 3, 5, 7, 9]] = torch.where(anch_f_2, lks[:, [1, 3, 5, 7, 9]] / anchors[2][1], lks[:, [1, 3, 5, 7, 9]])
|
|||
|
|
|
|||
|
|
#new_lks = lks[lks_mask>0]
|
|||
|
|
#print('new_lks: min --- ', torch.min(new_lks), ' max --- ', torch.max(new_lks))
|
|||
|
|
|
|||
|
|
lks_mask_1 = torch.where(lks < -3, torch.full_like(lks, 0.), torch.full_like(lks, 1.0))
|
|||
|
|
lks_mask_2 = torch.where(lks > 3, torch.full_like(lks, 0.), torch.full_like(lks, 1.0))
|
|||
|
|
|
|||
|
|
lks_mask_new = lks_mask * lks_mask_1 * lks_mask_2
|
|||
|
|
lks_mask_new[:, 0] = lks_mask_new[:, 0] * lks_mask_new[:, 1]
|
|||
|
|
lks_mask_new[:, 1] = lks_mask_new[:, 0] * lks_mask_new[:, 1]
|
|||
|
|
lks_mask_new[:, 2] = lks_mask_new[:, 2] * lks_mask_new[:, 3]
|
|||
|
|
lks_mask_new[:, 3] = lks_mask_new[:, 2] * lks_mask_new[:, 3]
|
|||
|
|
lks_mask_new[:, 4] = lks_mask_new[:, 4] * lks_mask_new[:, 5]
|
|||
|
|
lks_mask_new[:, 5] = lks_mask_new[:, 4] * lks_mask_new[:, 5]
|
|||
|
|
lks_mask_new[:, 6] = lks_mask_new[:, 6] * lks_mask_new[:, 7]
|
|||
|
|
lks_mask_new[:, 7] = lks_mask_new[:, 6] * lks_mask_new[:, 7]
|
|||
|
|
lks_mask_new[:, 8] = lks_mask_new[:, 8] * lks_mask_new[:, 9]
|
|||
|
|
lks_mask_new[:, 9] = lks_mask_new[:, 8] * lks_mask_new[:, 9]
|
|||
|
|
'''
|
|||
|
|
lks_mask_new = lks_mask
|
|||
|
|
lmks_mask.append(lks_mask_new)
|
|||
|
|
landmarks.append(lks)
|
|||
|
|
#print('lks: ', lks.size())
|
|||
|
|
|
|||
|
|
return tcls, tbox, indices, anch, landmarks, lmks_mask
|