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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from ..general import xywh2xyxy | |
| from ..loss import FocalLoss, smooth_BCE | |
| from ..metrics import bbox_iou | |
| from ..torch_utils import de_parallel | |
| from .general import crop_mask | |
| class ComputeLoss: | |
| # Compute losses | |
| def __init__(self, model, autobalance=False, overlap=False): | |
| self.sort_obj_iou = False | |
| self.overlap = overlap | |
| 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.nm = m.nm # number of masks | |
| self.anchors = m.anchors | |
| self.device = device | |
| def __call__(self, preds, targets, masks): # predictions, targets, model | |
| p, proto = preds | |
| bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width | |
| lcls = torch.zeros(1, device=self.device) | |
| lbox = torch.zeros(1, device=self.device) | |
| lobj = torch.zeros(1, device=self.device) | |
| lseg = torch.zeros(1, device=self.device) | |
| tcls, tbox, indices, anchors, tidxs, xywhn = 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 | |
| if n: | |
| pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1) # subset of predictions | |
| # Box 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 | |
| # Mask regression | |
| if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample | |
| masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0] | |
| marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized | |
| mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)) | |
| for bi in b.unique(): | |
| j = b == bi # matching index | |
| if self.overlap: | |
| mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0) | |
| else: | |
| mask_gti = masks[tidxs[i]][j] | |
| lseg += self.single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j]) | |
| 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'] | |
| lseg *= self.hyp['box'] / bs | |
| loss = lbox + lobj + lcls + lseg | |
| return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach() | |
| def single_mask_loss(self, gt_mask, pred, proto, xyxy, area): | |
| # Mask loss for one image | |
| pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80) | |
| loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none') | |
| return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean() | |
| 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, tidxs, xywhn = [], [], [], [], [], [] | |
| gain = torch.ones(8, 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) | |
| if self.overlap: | |
| batch = p[0].shape[0] | |
| ti = [] | |
| for i in range(batch): | |
| num = (targets[:, 0] == i).sum() # find number of targets of each image | |
| ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num) | |
| ti = torch.cat(ti, 1) # (na, nt) | |
| else: | |
| ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1) | |
| targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., 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, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors | |
| (a, tidx), (b, c) = at.long().T, 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 | |
| tidxs.append(tidx) | |
| xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized | |
| return tcls, tbox, indices, anch, tidxs, xywhn | |