utils/segment/loss.py

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
        self.device = device

        # 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