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import torch |
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import torch.nn as nn |
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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): |
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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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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') |
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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) |
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dx = pred - true |
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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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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 |
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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' |
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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) |
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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: |
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return loss |
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class QFocalLoss(nn.Module): |
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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 |
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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' |
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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) |
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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: |
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return loss |
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def compute_loss(p, targets, model): |
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device = targets.device |
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lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) |
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tcls, tbox, indices, anchors = build_targets(p, targets, model) |
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h = model.hyp |
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BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) |
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BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) |
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cp, cn = smooth_BCE(eps=0.0) |
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g = h['fl_gamma'] |
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if g > 0: |
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BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) |
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nt = 0 |
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balance = [4.0, 1.0, 0.3, 0.1, 0.03] |
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for i, pi in enumerate(p): |
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b, a, gj, gi = indices[i] |
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tobj = torch.zeros_like(pi[..., 0], device=device) |
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n = b.shape[0] |
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if n: |
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nt += n |
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ps = pi[b, a, gj, gi] |
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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) |
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iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) |
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lbox += (1.0 - iou).mean() |
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tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * iou.detach().clamp(0).type(tobj.dtype) |
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if model.nc > 1: |
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t = torch.full_like(ps[:, 5:], cn, device=device) |
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t[range(n), tcls[i]] = cp |
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lcls += BCEcls(ps[:, 5:], t) |
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lobj += BCEobj(pi[..., 4], tobj) * balance[i] |
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lbox *= h['box'] |
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lobj *= h['obj'] |
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lcls *= h['cls'] |
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bs = tobj.shape[0] |
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loss = lbox + lobj + lcls |
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return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() |
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def build_targets(p, targets, model): |
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det = model.module.model[-1] if is_parallel(model) else model.model[-1] |
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na, nt = det.na, targets.shape[0] |
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tcls, tbox, indices, anch = [], [], [], [] |
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gain = torch.ones(7, device=targets.device) |
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ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) |
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targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) |
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g = 0.5 |
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off = torch.tensor([[0, 0], |
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[1, 0], [0, 1], [-1, 0], [0, -1], |
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], device=targets.device).float() * g |
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for i in range(det.nl): |
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anchors = det.anchors[i] |
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gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] |
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t = targets * gain |
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if nt: |
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r = t[:, :, 4:6] / anchors[:, None] |
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j = torch.max(r, 1. / r).max(2)[0] < model.hyp['anchor_t'] |
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t = t[j] |
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gxy = t[:, 2:4] |
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gxi = gain[[2, 3]] - gxy |
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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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b, c = t[:, :2].long().T |
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gxy = t[:, 2:4] |
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gwh = t[:, 4:6] |
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gij = (gxy - offsets).long() |
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gi, gj = gij.T |
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a = t[:, 6].long() |
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indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) |
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tbox.append(torch.cat((gxy - gij, gwh), 1)) |
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anch.append(anchors[a]) |
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tcls.append(c) |
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return tcls, tbox, indices, anch |
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