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