补一下源码地址
我们先从简单的开始说起，怎么判断loss？IOU（交并比）

IOU

yolov4用了CIOU_loss 和DIOU_LOSS
简单说一下，有个具体了解，都是从左到右发展来的

看代码就更直观了解他们的运作方式了

if GIoU or DIoU or CIoU:
        if GIoU: #area_c 就是外接矩形
            area_c = torch.prod(con_br - con_tl, 2)  # br tl对应button right和 top left坐标，这个公式就是算最小外接矩形面积
            return iou - (area_c - area_u) / area_c  # GIoU的公式，
        if DIoU or CIoU: 
        	#c2就是欧式距离 加一个小偏置防止除数为0
            c2 = torch.pow(con_br - con_tl, 2).sum(dim=2) + 1e-16
            if DIoU:
            #rho2 就是中心点距离 rho2 = ((bboxes_a[:, None, :2] - bboxes_b[:, :2]) ** 2 / 4).sum(dim=-1)
                return iou - rho2 / c2  # DIoU 的计算公式 加了个中心点距离/欧氏距离
            elif CIoU:  
            #这个V是长宽比
                v = (4 / math.pi ** 2) * torch.pow(torch.atan(w1 / h1).unsqueeze(1) - torch.atan(w2 / h2), 2)
                with torch.no_grad():
                    alpha = v / (1 - iou + v)
                return iou - (rho2 / c2 + v * alpha)  # CIoU 可以看到比Diou多了个长宽比因素
    return iou

如果对上述参数不了解，可以参考一下源代码，这里贴太多反而容易混淆

Loss function

CIOU懂了 那么CIOU loss呢
其实就是CIOU loss = (1-CIOU)
GIOU,CIOU等同理

那么yolo怎么计算loss的呢
偷一下cuijiahua大佬的图

很复杂 看不懂？
没关系 实际上就是 三个loss组成的
如果有物体 就要加上： 坐标框损失，置信度损失，分类类别损失
大概知道什么意思 然后去看代码就可以了：

代码很长 可以只看我注释的地方 方便了解大体作用

class Yolo_loss(nn.Module):
    def __init__(self, n_classes=80, n_anchors=3, device=None, batch=2):
        super(Yolo_loss, self).__init__()
        # 这些老参数了 看我上一章内容都有
        self.device = device
        self.strides = [8, 16, 32]
        image_size = 608
        self.n_classes = n_classes
        self.n_anchors = n_anchors

        self.anchors = [[12, 16], [19, 36], [40, 28], [36, 75], [76, 55], [72, 146], [142, 110], [192, 243], [459, 401]]
        self.anch_masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
        self.ignore_thre = 0.5

        self.masked_anchors, self.ref_anchors, self.grid_x, self.grid_y, self.anchor_w, self.anchor_h = [], [], [], [], [], []
		#遍历三个anchor框 这下面代码在之前都出现过 具体就是初始化那些anchor
        for i in range(3):
            all_anchors_grid = [(w / self.strides[i], h / self.strides[i]) for w, h in self.anchors]
            masked_anchors = np.array([all_anchors_grid[j] for j in self.anch_masks[i]], dtype=np.float32)
            ref_anchors = np.zeros((len(all_anchors_grid), 4), dtype=np.float32)
            ref_anchors[:, 2:] = np.array(all_anchors_grid, dtype=np.float32)
            ref_anchors = torch.from_numpy(ref_anchors)
            # calculate pred - xywh obj cls
            fsize = image_size // self.strides[i]
            grid_x = torch.arange(fsize, dtype=torch.float).repeat(batch, 3, fsize, 1).to(device)
            grid_y = torch.arange(fsize, dtype=torch.float).repeat(batch, 3, fsize, 1).permute(0, 1, 3, 2).to(device)
            anchor_w = torch.from_numpy(masked_anchors[:, 0]).repeat(batch, fsize, fsize, 1).permute(0, 3, 1, 2).to(
                device)
            anchor_h = torch.from_numpy(masked_anchors[:, 1]).repeat(batch, fsize, fsize, 1).permute(0, 3, 1, 2).to(
                device)

            self.masked_anchors.append(masked_anchors)
            self.ref_anchors.append(ref_anchors)
            self.grid_x.append(grid_x)
            self.grid_y.append(grid_y)
            self.anchor_w.append(anchor_w)
            self.anchor_h.append(anchor_h)

    def build_target(self, pred, labels, batchsize, fsize, n_ch, output_id):
        # 目标注册 tgt最后一维是4 对应除p外的标签
        # (B,3,f,f,4)
        tgt_mask = torch.zeros(batchsize, self.n_anchors, fsize, fsize, 4 + self.n_classes).to(device=self.device)
        # (B,3,f,f)
        obj_mask = torch.ones(batchsize, self.n_anchors, fsize, fsize).to(device=self.device)
        tgt_scale = torch.zeros(batchsize, self.n_anchors, fsize, fsize, 2).to(self.device)
        target = torch.zeros(batchsize, self.n_anchors, fsize, fsize, n_ch).to(self.device)

        # labels = labels.cpu().data
        nlabel = (labels.sum(dim=2) > 0).sum(dim=1)  #label数量统计
		# label对应的是x，y，w，h 所以X=x+w,Y=y+h  下面宽高还要除以步长
        truth_x_all = (labels[:, :, 2] + labels[:, :, 0]) / (self.strides[output_id] * 2)
        truth_y_all = (labels[:, :, 3] + labels[:, :, 1]) / (self.strides[output_id] * 2)
        truth_w_all = (labels[:, :, 2] - labels[:, :, 0]) / self.strides[output_id]
        truth_h_all = (labels[:, :, 3] - labels[:, :, 1]) / self.strides[output_id]
        truth_i_all = truth_x_all.to(torch.int16).cpu().numpy() 
        truth_j_all = truth_y_all.to(torch.int16).cpu().numpy()

        for b in range(batchsize):
            n = int(nlabel[b])
            if n == 0:
                continue
            truth_box = torch.zeros(n, 4).to(self.device)
            truth_box[:n, 2] = truth_w_all[b, :n]
            truth_box[:n, 3] = truth_h_all[b, :n]
            truth_i = truth_i_all[b, :n]
            truth_j = truth_j_all[b, :n]

            # calculate iou between truth and reference anchors
            anchor_ious_all = bboxes_iou(truth_box.cpu(), self.ref_anchors[output_id], CIoU=True)

            # temp = bbox_iou(truth_box.cpu(), self.ref_anchors[output_id])

            best_n_all = anchor_ious_all.argmax(dim=1)
            best_n = best_n_all % 3
            best_n_mask = ((best_n_all == self.anch_masks[output_id][0]) |
                           (best_n_all == self.anch_masks[output_id][1]) |
                           (best_n_all == self.anch_masks[output_id][2]))

            if sum(best_n_mask) == 0:
                continue

            truth_box[:n, 0] = truth_x_all[b, :n]
            truth_box[:n, 1] = truth_y_all[b, :n]

            pred_ious = bboxes_iou(pred[b].view(-1, 4), truth_box, xyxy=False)
            pred_best_iou, _ = pred_ious.max(dim=1)
            pred_best_iou = (pred_best_iou > self.ignore_thre)
            pred_best_iou = pred_best_iou.view(pred[b].shape[:3])
            # set mask to zero (ignore) if pred matches truth
            obj_mask[b] = ~ pred_best_iou

            for ti in range(best_n.shape[0]):
                if best_n_mask[ti] == 1:
                    i, j = truth_i[ti], truth_j[ti]
                    a = best_n[ti]
                    obj_mask[b, a, j, i] = 1
                    tgt_mask[b, a, j, i, :] = 1
                    target[b, a, j, i, 0] = truth_x_all[b, ti] - truth_x_all[b, ti].to(torch.int16).to(torch.float)
                    target[b, a, j, i, 1] = truth_y_all[b, ti] - truth_y_all[b, ti].to(torch.int16).to(torch.float)
                    target[b, a, j, i, 2] = torch.log(
                        truth_w_all[b, ti] / torch.Tensor(self.masked_anchors[output_id])[best_n[ti], 0] + 1e-16)
                    target[b, a, j, i, 3] = torch.log(
                        truth_h_all[b, ti] / torch.Tensor(self.masked_anchors[output_id])[best_n[ti], 1] + 1e-16)
                    target[b, a, j, i, 4] = 1
                    target[b, a, j, i, 5 + labels[b, ti, 4].to(torch.int16).cpu().numpy()] = 1
                    tgt_scale[b, a, j, i, :] = torch.sqrt(2 - truth_w_all[b, ti] * truth_h_all[b, ti] / fsize / fsize)
        return obj_mask, tgt_mask, tgt_scale, target

    def forward(self, xin, labels=None):
        loss, loss_xy, loss_wh, loss_obj, loss_cls, loss_l2 = 0, 0, 0, 0, 0, 0
        for output_id, output in enumerate(xin):
            batchsize = output.shape[0]
            fsize = output.shape[2]
            n_ch = 5 + self.n_classes

            output = output.view(batchsize, self.n_anchors, n_ch, fsize, fsize)
            output = output.permute(0, 1, 3, 4, 2)  # .contiguous()

            # logistic activation for xy, obj, cls
            output[..., np.r_[:2, 4:n_ch]] = torch.sigmoid(output[..., np.r_[:2, 4:n_ch]])

            pred = output[..., :4].clone()
            pred[..., 0] += self.grid_x[output_id]
            pred[..., 1] += self.grid_y[output_id]
            pred[..., 2] = torch.exp(pred[..., 2]) * self.anchor_w[output_id]
            pred[..., 3] = torch.exp(pred[..., 3]) * self.anchor_h[output_id]

            obj_mask, tgt_mask, tgt_scale, target = self.build_target(pred, labels, batchsize, fsize, n_ch, output_id)

            # loss calculation
            output[..., 4] *= obj_mask
            output[..., np.r_[0:4, 5:n_ch]] *= tgt_mask
            output[..., 2:4] *= tgt_scale

            target[..., 4] *= obj_mask
            target[..., np.r_[0:4, 5:n_ch]] *= tgt_mask
            target[..., 2:4] *= tgt_scale

            loss_xy += F.binary_cross_entropy(input=output[..., :2], target=target[..., :2],
                                              weight=tgt_scale * tgt_scale, reduction='sum')
            loss_wh += F.mse_loss(input=output[..., 2:4], target=target[..., 2:4], reduction='sum') / 2
            loss_obj += F.binary_cross_entropy(input=output[..., 4], target=target[..., 4], reduction='sum')
            loss_cls += F.binary_cross_entropy(input=output[..., 5:], target=target[..., 5:], reduction='sum')
            loss_l2 += F.mse_loss(input=output, target=target, reduction='sum')

        loss = loss_xy + loss_wh + loss_obj + loss_cls

        return loss, loss_xy, loss_wh, loss_obj, loss_cls, loss_l2

今天累了 代码写到这 后续补完