239 lines
9.9 KiB
Python
239 lines
9.9 KiB
Python
|
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
|||
|
|
"""
|
|||
|
|
Loss functions
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
import torch
|
|||
|
|
import torch.nn as nn
|
|||
|
|
|
|||
|
|
from app.yolov5.utils.metrics import bbox_iou
|
|||
|
|
from app.yolov5.utils.torch_utils import de_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().__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().__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().__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 ComputeLoss:
|
|||
|
|
sort_obj_iou = False
|
|||
|
|
|
|||
|
|
# Compute losses
|
|||
|
|
def __init__(self, model, autobalance=False):
|
|||
|
|
device = next(model.parameters()).device # get model device
|
|||
|
|
h = model.hyp # hyperparameters
|
|||
|
|
|
|||
|
|
# Define criteria
|
|||
|
|
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
|
|||
|
|
BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
|
|||
|
|
|
|||
|
|
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
|
|||
|
|
self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
|
|||
|
|
|
|||
|
|
# Focal loss
|
|||
|
|
g = h['fl_gamma'] # focal loss gamma
|
|||
|
|
if g > 0:
|
|||
|
|
BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
|
|||
|
|
|
|||
|
|
m = de_parallel(model).model[-1] # Detect() module
|
|||
|
|
self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
|
|||
|
|
self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index
|
|||
|
|
self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
|
|||
|
|
self.na = m.na # number of anchors
|
|||
|
|
self.nc = m.nc # number of classes
|
|||
|
|
self.nl = m.nl # number of layers
|
|||
|
|
self.anchors = m.anchors
|
|||
|
|
self.device = device
|
|||
|
|
|
|||
|
|
def __call__(self, p, targets): # predictions, targets
|
|||
|
|
lcls = torch.zeros(1, device=self.device) # class loss
|
|||
|
|
lbox = torch.zeros(1, device=self.device) # box loss
|
|||
|
|
lobj = torch.zeros(1, device=self.device) # object loss
|
|||
|
|
tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
|
|||
|
|
|
|||
|
|
# Losses
|
|||
|
|
for i, pi in enumerate(p): # layer index, layer predictions
|
|||
|
|
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
|
|||
|
|
tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
|
|||
|
|
|
|||
|
|
n = b.shape[0] # number of targets
|
|||
|
|
# 增加部分：判断tcls里面的标签是否超界，超界赋0
|
|||
|
|
for url in range(len(tcls[i])):
|
|||
|
|
if tcls[i][url] > (self.nc - 1):
|
|||
|
|
tcls[i][url] = 0
|
|||
|
|
if n:
|
|||
|
|
# pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0
|
|||
|
|
pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions
|
|||
|
|
|
|||
|
|
# Regression
|
|||
|
|
pxy = pxy.sigmoid() * 2 - 0.5
|
|||
|
|
pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
|
|||
|
|
pbox = torch.cat((pxy, pwh), 1) # predicted box
|
|||
|
|
iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)
|
|||
|
|
lbox += (1.0 - iou).mean() # iou loss
|
|||
|
|
|
|||
|
|
# Objectness
|
|||
|
|
iou = iou.detach().clamp(0).type(tobj.dtype)
|
|||
|
|
if self.sort_obj_iou:
|
|||
|
|
j = iou.argsort()
|
|||
|
|
b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
|
|||
|
|
if self.gr < 1:
|
|||
|
|
iou = (1.0 - self.gr) + self.gr * iou
|
|||
|
|
tobj[b, a, gj, gi] = iou # iou ratio
|
|||
|
|
|
|||
|
|
# Classification
|
|||
|
|
if self.nc > 1: # cls loss (only if multiple classes)
|
|||
|
|
t = torch.full_like(pcls, self.cn, device=self.device) # targets
|
|||
|
|
t[range(n), tcls[i]] = self.cp
|
|||
|
|
lcls += self.BCEcls(pcls, 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)]
|
|||
|
|
|
|||
|
|
obji = self.BCEobj(pi[..., 4], tobj)
|
|||
|
|
lobj += obji * self.balance[i] # obj loss
|
|||
|
|
if self.autobalance:
|
|||
|
|
self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
|
|||
|
|
|
|||
|
|
if self.autobalance:
|
|||
|
|
self.balance = [x / self.balance[self.ssi] for x in self.balance]
|
|||
|
|
lbox *= self.hyp['box']
|
|||
|
|
lobj *= self.hyp['obj']
|
|||
|
|
lcls *= self.hyp['cls']
|
|||
|
|
bs = tobj.shape[0] # batch size
|
|||
|
|
|
|||
|
|
return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
|
|||
|
|
|
|||
|
|
def build_targets(self, p, targets):
|
|||
|
|
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
|
|||
|
|
na, nt = self.na, targets.shape[0] # number of anchors, targets
|
|||
|
|
tcls, tbox, indices, anch = [], [], [], []
|
|||
|
|
gain = torch.ones(7, device=self.device) # normalized to gridspace gain
|
|||
|
|
ai = torch.arange(na, device=self.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=self.device).float() * g # offsets
|
|||
|
|
|
|||
|
|
for i in range(self.nl):
|
|||
|
|
anchors, shape = self.anchors[i], p[i].shape
|
|||
|
|
gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
|
|||
|
|
|
|||
|
|
# Match targets to anchors
|
|||
|
|
t = targets * gain # shape(3,n,7)
|
|||
|
|
if nt:
|
|||
|
|
# Matches
|
|||
|
|
r = t[..., 4:6] / anchors[:, None] # wh ratio
|
|||
|
|
j = torch.max(r, 1 / r).max(2)[0] < self.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
|
|||
|
|
bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
|
|||
|
|
a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class
|
|||
|
|
gij = (gxy - offsets).long()
|
|||
|
|
gi, gj = gij.T # grid indices
|
|||
|
|
|
|||
|
|
# Append
|
|||
|
|
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
|
|||
|
|
|
|||
|
|
return tcls, tbox, indices, anch
|