# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

"""

Loss functions

"""

import torch

import torch.nn as nn

from utils.metrics import bbox_iou

from utils.torch_utils import is_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:

    # Compute losses

    def __init__(self, model, autobalance=False):

        self.sort_obj_iou = 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)

        det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module

        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, 0.02])  # P3-P7

        self.ssi = list(det.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

        for k in 'na', 'nc', 'nl', 'anchors':

            setattr(self, k, getattr(det, k))

    def __call__(self, p, targets):  # predictions, targets, model

        device = targets.device

        lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)

        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_like(pi[..., 0], device=device)  # target obj

            n = b.shape[0]  # number of targets

            if n:

                ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets

                # Regression

                pxy = ps[:, :2].sigmoid() * 2 - 0.5

                pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]

                pbox = torch.cat((pxy, pwh), 1)  # predicted box

                iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # iou(prediction, target)

                lbox += (1.0 - iou).mean()  # iou loss

                # Objectness

                score_iou = iou.detach().clamp(0).type(tobj.dtype)

                if self.sort_obj_iou:

                    sort_id = torch.argsort(score_iou)

                    b, a, gj, gi, score_iou = b[sort_id], a[sort_id], gj[sort_id], gi[sort_id], score_iou[sort_id]

                tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * score_iou  # iou ratio

                # Classification

                if self.nc > 1:  # cls loss (only if multiple classes)

                    t = torch.full_like(ps[:, 5:], self.cn, device=device)  # targets

                    t[range(n), tcls[i]] = self.cp

                    lcls += self.BCEcls(ps[:, 5:], 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=targets.device)  # normalized to gridspace gain

        ai = torch.arange(na, device=targets.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=targets.device).float() * g  # offsets

        for i in range(self.nl):

            anchors = self.anchors[i]

            gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain

            # Match targets to anchors

            t = targets * gain

            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

            b, c = t[:, :2].long().T  # image, class

            gxy = t[:, 2:4]  # grid xy

            gwh = t[:, 4:6]  # grid wh

            gij = (gxy - offsets).long()

            gi, gj = gij.T  # grid xy indices

            # Append

            a = t[:, 6].long()  # anchor indices

            indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)))  # image, anchor, grid indices

            tbox.append(torch.cat((gxy - gij, gwh), 1))  # box

            anch.append(anchors[a])  # anchors

            tcls.append(c)  # class

        return tcls, tbox, indices, anch