yolov5如何修改损失函数

Yolov5的损失函数是在PyTorch中实现的，如果想要修改损失函数，需要对其源代码进行修改。

下面是一个简单的例子，演示如何修改Yolov5的损失函数：

1. 打开`models/yolo.py`文件，找到`compute_loss()`函数。

2. 在该函数中，可以看到损失函数的计算过程。例如，YOLOv5s的损失函数计算代码如下：

loss, loss_items = 0, []
for i, pi in enumerate(pred):
    # Prepare targets
    if i in [3, 4, 5]:
        # 3: large obj, 4: medium obj, 5: small obj
        obj, ignore = t[1], t[2]
        m = torch.sigmoid
    else:
        # 0: x, 1: y, 2: w, 3: h, 4: obj, 5: cls
        obj, ignore = t[4], t[3] & torch.lt(t[4], 1)
        m = torch.nn.functional.one_hot if i == 0 else torch.sigmoid

    # Define criteria
    BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([self.nclass], device=obj.device))
    BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([self.obj], device=obj.device))
    BCEpos = nn.BCEWithLogitsLoss()
    lcls, lbox, lobj = 0, 0, 0

    # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
    cp, cn = smooth_bce(pi[ignore == 0], t[5][ignore == 0], self.smooth_eps, self.nclass)

    # Losses
    if len(cp):
        lcls = BCEcls(pi[ignore == 0], cp)
    if len(cn):
        lcls += BCEcls(pi[ignore == 0], cn)
    if obj.sum() > 0:
        lobj = BCEobj(pi[obj > 0], obj[obj > 0])
        lbox = BCEpos(pi[obj > 0], t[0][obj > 0]) + \
               BCEpos(pi[obj > 0], t[1][obj > 0]) + \
               BCEpos(pi[obj > 0], t[2][obj > 0]) + \
               BCEpos(pi[obj > 0], t[3][obj > 0])

    loss_items.append([lbox, lobj, lcls])
    loss += lbox + lobj + lcls

3. 如果要修改损失函数，可以改变BCEWithLogitsLoss的参数，或者在计算损失时增加其他的项。例如，如果要增加一个正则化项，可以在计算损失时增加该项的计算。修改后的代码如下：

loss, loss_items = 0, []
for i, pi in enumerate(pred):
    # Prepare targets
    if i in [3, 4, 5]:
        # 3: large obj, 4: medium obj, 5: small obj
        obj, ignore = t[1], t[2]
        m = torch.sigmoid
    else:
        # 0: x, 1: y, 2: w, 3: h, 4: obj, 5: cls
        obj, ignore = t[4], t[3] & torch.lt(t[4], 1)
        m = torch.nn.functional.one_hot if i == 0 else torch.sigmoid

    # Define criteria
    BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([self.nclass], device=obj.device))
    BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([self.obj], device=obj.device))
    BCEpos = nn.BCEWithLogitsLoss()
    lcls, lbox, lobj, lreg = 0, 0, 0, 0

    # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
    cp, cn = smooth_bce(pi[ignore == 0], t[5][ignore == 0], self.smooth_eps, self.nclass)

    # Losses
    if len(cp):
        lcls = BCEcls(pi[ignore == 0], cp)
    if len(cn):
        lcls += BCEcls(pi[ignore == 0], cn)
    if obj.sum() > 0:
        lobj = BCEobj(pi[obj > 0], obj[obj > 0])
        lbox = BCEpos(pi[obj > 0], t[0][obj > 0]) + \
               BCEpos(pi[obj > 0], t[1][obj > 0]) + \
               BCEpos(pi[obj > 0], t[2][obj > 0]) + \
               BCEpos(pi[obj > 0], t[3][obj > 0])
        lreg = torch.sum(torch.square(pi[obj > 0] - t[0][obj > 0])) # regularization term

    loss_items.append([lbox, lobj, lcls, lreg])
    loss += lbox + lobj + lcls + lreg

4. 保存修改后的代码，并重新训练模型即可。