Focal 损失函数代码如下：def focal_loss(input_values, gamma): """Computes the focal loss""" p = torch.exp(-input_values) loss = (1 - p) ** gamma * input_values return loss.mean() class FocalLoss(nn.Module): def __init__(self, weight=None, gamma=0.): super(FocalLoss, self).__init__() assert gamma >= 0 self.gamma = gamma self.weight = weight def forward(self, input, target): return focal_loss(F.cross_entropy(input, target, reduction='none', weight=self.weight), self.gamma) LDAM损失函数代码如下：class LDAMLoss(nn.Module): def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).__init__() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s self.weight = weight def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index.scatter_(1, target.data.view(-1, 1), 1) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) x_m = x - batch_m output = torch.where(index, x_m, x) return F.cross_entropy(self.s*output, target, weight=self.weight) Large Margin aware Focal (LMF) 损失函数是 Focal 损失函数和 LDAM损失函数加权的线性组合，帮我用pytorch代码实现LMF损失函数

Focal loss代码如下：class FocalLoss(nn.Module): def init(self, alpha=1, gamma=2, weight=None): super(FocalLoss, self).init() self.alpha = alpha self.gamma = gamma def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: log_probs = F.log_softmax(x, dim=-1) probs = torch.exp(log_probs) focal_loss = -self.alpha * (torch.pow((1 - probs), self.gamma)) * log_probs loss = torch.sum(target * focal_loss, dim=-1) return loss.mean() LDAM loss 代码如下：class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s self.weight = weight def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index.scatter_(1, target.data.view(-1, 1), 1) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) x_m = x - batch_m output = torch.where(index, x_m, x) return F.cross_entropy(self.s*output, target, weight=self.weight) LMF loss 是Focal loss 和LDAM loss两个损失函数的加权求和，请用pytorch代码实现LMF损失函数

下面是LMF损失函数的PyTorch代码实现，其中alpha和beta是Focal loss和LDAM loss的权重，需要根据具体情况进行调整： class LMF(nn.Module): def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30, ...

Focal loss代码如下：class FocalLoss(nn.Module): def init(self, alpha=1, gamma=2, weight=None): super(FocalLoss, self).init() self.alpha = alpha self.gamma = gamma def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: log_probs = F.log_softmax(x, dim=-1) probs = torch.exp(log_probs) focal_loss = -self.alpha * (torch.pow((1 - probs), self.gamma)) * log_probs loss = torch.sum(target * focal_loss, dim=-1) return loss.mean() LDAM loss 代码如下：class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s self.weight = weight def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index.scatter_(1, target.data.view(-1, 1), 1) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) x_m = x - batch_m output = torch.where(index, x_m, x) return F.cross_entropy(self.s*output, target, weight=self.weight) LMF loss 是Focal loss 和LDAM loss两个损失函数的加权求和，请用pytorch代码实现LMF损失函数并在模型训练中使用

下面是LMF损失函数的PyTorch代码实现，并在模型训练中使用： import torch.nn.functional as F class LMF(nn.Module): def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30, alpha=0.25, gamma=2...

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(FocalLoss, self).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

这个代码实现了一个 Focal Loss 损失函数，它是对二分类问题中的交叉熵损失函数的一种改进。它主要通过增加一个可调的超参数 $\gamma$，来调整难易样本的权重，从而解决类别不平衡问题。在代码中，它被实现为一个 ...

class SoftmaxFocalLoss(nn.Module): def init(self, gamma, ignore_lb=255, *args, **kwargs): super(FocalLoss, self).init() self.gamma = gamma self.nll = nn.NLLLoss(ignore_index=ignore_lb) def forward(self, logits, labels): scores = F.softmax(logits, dim=1) factor = torch.pow(1.-scores, self.gamma) log_score = F.log_softmax(logits, dim=1) log_score = factor * log_score loss = self.nll(log_score, labels) return loss

gamma是Focal Loss中的一个超参数，ignore_lb是一个指定忽略标签的索引值，默认为255。该损失函数的前向传播方法forward接受logits和labels作为输入，并且计算出损失值。首先，通过softmax函数计算出logits的概率...

def focal_loss_with_logits(y_hat_log, y, gamma=2): log0 = F.logsigmoid(-y_hat_log) log1 = F.logsigmoid(y_hat_log) gamma0 = torch.pow(torch.abs(1 - y - torch.exp(log0)), gamma) gamma1 = torch.pow(torch.abs(y - torch.exp(log1)), gamma) return torch.mean(-(1 - y) * gamma0 * log0 - y * gamma1 * log1)是什么意思

这段代码定义了一个名为 focal_loss_with_logits 的函数，用于计算使用 Focal Loss 的二分类损失函数。具体的实现如下： 1. 接收两个参数 y_hat_log 和 y，分别表示模型的预测 logits 和真实标签。 2. ...

class FocalLoss(nn.Module): """Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5).""" def init(self): """Initializer for FocalLoss class with no parameters.""" super().init() @staticmethod def forward(pred, label, gamma=1.5, alpha=0.25): """Calculates and updates confusion matrix for object detection/classification tasks.""" loss = F.binary_cross_entropy_with_logits(pred, label, reduction="none") # 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 = pred.sigmoid() # prob from logits p_t = label * pred_prob + (1 - label) * (1 - pred_prob) modulating_factor = (1.0 - p_t) ** gamma loss = modulating_factor if alpha > 0: alpha_factor = label alpha + (1 - label) * (1 - alpha) loss = alpha_factor return loss.mean(1).sum()class BboxLoss(nn.Module): """Criterion class for computing training losses during training.""" def init(self, reg_max=16): """Initialize the BboxLoss module with regularization maximum and DFL settings.""" super().init() self.dfl_loss = DFLoss(reg_max) if reg_max > 1 else None def forward(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask): """IoU loss.""" weight = target_scores.sum(-1)[fg_mask].unsqueeze(-1) # iou = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask], xywh=False, CIoU=True) # loss_iou = ((1.0 - iou) weight).sum() / target_scores_sum iou = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask],xywh=False, alpha_iou=True, alpha=3.0) loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum if type(iou) is tuple: if len(iou) == 2: loss_iou = ((1.0 - iou[0]) * iou[1].detach() * weight).sum() / target_scores_sum else: loss_iou = (iou[0] * iou[1] * weight).sum() / target_scores_sum else: loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum # DFL loss if self.dfl_loss: target_ltrb = bbox2dist(anchor_points, target_bboxes, self.dfl_loss.reg_max - 1) loss_dfl = self.dfl_loss(pred_dist[fg_mask].view(-1, self.dfl_loss.reg_max), target_ltrb[fg_mask]) * weight loss_dfl = loss_dfl.sum() / target_scores_sum else: loss_dfl = torch.tensor(0.0).to(pred_dist.device) return loss_iou, loss_dfl 我想在BboxLoss函数中添加Focal Loss缓解类别不平衡问题。请帮我修改以下代码，并形成新的完整代码

Focal Loss的公式是FL(p_t) = -alpha * (1 - p_t)^gamma * log(p_t)，其中p_t是预测概率。在回归问题中，可能需要调整。比如，这里的iou可以视为类似p_t的指标，因为高iou的样本更容易，低iou则困难。所以可以将(1 -...

import torch import torch.nn as nn import torch.nn.functional as F class FocalDiceLoss(nn.Module): """ 结合 FocalLoss 和 DiceLoss 用于多分类场景: - 假设 predict:[B, tag_size, R, S, S] - 假设 target: [B, R, S, S] (整型标签) - 最终返回: [B, R, S, S] 形状的逐元素损失, 以便与 mask 相乘后再求平均算法流程: 1) flatten => [BRSS, tag_size], 计算 focal_loss & dice_loss 2) reshape => [B, R, S, S] 3) focal + dice = total """ def init(self, alpha=1.0, gamma=2.0, smooth=1.0, focal_weight=0.5): """ alpha: FocalLoss 中正例损失缩放系数 gamma: FocalLoss 中聚焦因子 smooth: DiceLoss 中平滑项, 防止分母为0 focal_weight: focal_loss 占总损失的权重 w_f dice_loss 的权重则是 (1 - w_f) """ super(FocalDiceLoss, self).init() self.alpha = alpha self.gamma = gamma self.smooth = smooth self.focal_weight = focal_weight def forward(self, predict: torch.Tensor, target: torch.Tensor) -> torch.Tensor: """ predict: [B, tag_size, R, S, S] target: [B, R, S, S] (long型, 取值0~tag_size-1) return: [B, R, S, S] => 逐元素损失 """ B, tag_size, R, S1, S2 = predict.size() # =========== Step1: Flatten for easy CE / Dice computations =========== # [B, tag_size, R, S1, S2] -> [B, R, S1, S2, tag_size] pred_perm = predict.permute(0, 2, 3, 4, 1).contiguous() # => [B, R, S1, S2, tag_size] # flatten => [BRS1S2, tag_size] pred_2d = pred_perm.view(-1, tag_size) # [B, R, S1, S2] => [BRS1S2] target_1d = target.view(-1) # =========== Step2: FocalLoss = alpha (1-pt)^gamma * CE ============ ce_loss = F.cross_entropy(pred_2d, target_1d, reduction='none') # [BRS1S2] pt = torch.exp(-ce_loss) # [BR*S1S2] focal_loss = self.alpha (1 - pt) ** self.gamma * ce_loss # [BRS1*S2] # =========== Step3: DiceLoss (softmax + one_hot) ===================== # 1) 先把 pred_2

好的，我现在需要帮用户解决如何在PyTorch中实现结合Focal Loss和Dice Loss的自定义损失函数，特别是在多分类场景下。首先，我需要回顾这两种损失的基本原理以及它们在多分类中的应用方式。用户提供的参考资料里有...

def init(self, reg_max=16): """Initialize the BboxLoss module with regularization maximum and DFL settings.""" super().init() self.dfl_loss = DFLoss(reg_max) if reg_max > 1 else None # 添加Focal Loss初始化 self.focal_loss = FocalLoss(alpha=0.25, gamma=2.0) # 参数可调 def forward(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask): """IoU loss.""" weight = target_scores.sum(-1)[fg_mask].unsqueeze(-1) # iou = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask], xywh=False, CIoU=True) # loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum iou = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask],xywh=False, alpha_iou=True, alpha=3.0) if isinstance(iou, tuple): # 处理多返回值情况 loss_iou = ((1.0 - iou[0]) * iou[1].detach() * weight).sum() / target_scores_sum else: loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum # DFL loss if self.dfl_loss: target_ltrb = bbox2dist(anchor_points, target_bboxes, self.dfl_loss.reg_max - 1) loss_dfl = self.dfl_loss(pred_dist[fg_mask].view(-1, self.dfl_loss.reg_max), target_ltrb[fg_mask]) * weight loss_dfl = loss_dfl.sum() / target_scores_sum else: loss_dfl = torch.tensor(0.0).to(pred_dist.device) return loss_iou, loss_dfl 我想添加使用Focal Loss缓解类别不平衡问题。请帮我修改以下代码，并形成新的完整代码

考虑到用户可能希望在这个模块中添加Focal Loss作为分类损失，并且希望保持代码的完整性，那么可能需要修改forward函数的参数，加入pred_scores，并计算对应的Focal Loss。因此，在用户提供的代码中，他们在初始化...

def forward(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask): """IoU loss.""" weight = target_scores.sum(-1)[fg_mask].unsqueeze(-1) iou = probiou(pred_bboxes[fg_mask], target_bboxes[fg_mask]) loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum # DFL loss if self.dfl_loss: target_ltrb = bbox2dist(anchor_points, xywh2xyxy(target_bboxes[..., :4]), self.dfl_loss.reg_max - 1) loss_dfl = self.dfl_loss(pred_dist[fg_mask].view(-1, self.dfl_loss.reg_max), target_ltrb[fg_mask]) * weight loss_dfl = loss_dfl.sum() / target_scores_sum else: loss_dfl = torch.tensor(0.0).to(pred_dist.device) return loss_iou, loss_想在BboxLoss函数中添加Focal Loss缓解类别不平衡问题。请帮我修改以下代码，并形成新的完整代码

假设在现有的代码中，分类损失可能被计算为BCEWithLogitsLoss或其他类似损失函数，现在需要将其改为Focal Loss。例如，原来的代码可能有类似loss_cls = BCEWithLogitsLoss(pred_scores, target_scores)，现在要替换...

def Focal_Loss(inputs, target, cls_weights, num_classes=21, alpha=0.5, gamma=2): n, c, h, w = inputs.size() nt, ht, wt = target.size() if h != ht and w != wt: inputs = F.interpolate(inputs, size=(ht, wt), mode="bilinear", align_corners=True) temp_inputs = inputs.transpose(1, 2).transpose(2, 3).contiguous().view(-1, c) temp_target = target.view(-1) logpt = -nn.CrossEntropyLoss(weight=cls_weights, ignore_index=num_classes, reduction='none')(temp_inputs, temp_target) pt = torch.exp(logpt) if alpha is not None: logpt *= alpha loss = -((1 - pt) ** gamma) * logpt loss = loss.mean() return loss 是什么意思

这段代码实现了 Focal Loss，是一种用于解决分类问题中，样本不平衡导致的分类精度下降的方法。其中，输入的参数包括： - inputs：模型的输出，是一个大小为 (n, c, h, w) 的张量，其中 n 表示 batch size，c 表示...

def loss_labels(self, outputs, targets, indices, num_boxes, log=True): assert 'pred_logits' in outputs src_logits = outputs['pred_logits'] idx = self._get_src_permutation_idx(indices) target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) target_classes = torch.full(src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device) target_classes[idx] = target_classes_o loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight) losses = {'loss_ce': loss_ce} if log: # TODO this should probably be a separate loss, not hacked in this one here losses['class_error'] = 100 - accuracy(src_logits[idx], target_classes_o)[0] return losses怎么替换为focal损失函数

要将上述代码替换为Focal Loss，可以修改loss_labels函数的第8行，将F.cross_entropy替换为我们刚刚实现的FocalLoss。修改后的代码如下： python def loss_labels(self, outputs, targets, indices, num_boxes, ...

class ComputeLoss: sort_obj_iou = False # Compute losses def init(self, model, autobalance=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) 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.anchors = m.anchors self.device = device这个代码什么意思

在初始化时，会根据模型的超参数定义不同的损失函数，包括分类损失 BCEcls、目标检测损失 BCEobj 和 Focal Loss。同时还对正负样本进行了平滑处理，以及为不同的特征层定义了不同的权重平衡。在计算损失时，会用到...

Traceback (most recent call last): File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 275, in <module> train_loss, train_acc = train(model_ft, DEVICE, train_loader, optimizer, epoch,model_ema) File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 48, in train loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss File "/home/bder73002/anaconda3/envs/python3.9.2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/bder73002/hpy/ConvNextV2_Demo/models/losses.py", line 56, in forward focal_loss = self.focal_loss(x, target) File "/home/bder73002/anaconda3/envs/python3.9.2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/bder73002/hpy/ConvNextV2_Demo/models/losses.py", line 21, in forward return focal_loss(F.cross_entropy(input, target, reduction='none', weight=self.weight), self.gamma) File "/home/bder73002/anaconda3/envs/python3.9.2/lib/python3.9/site-packages/torch/nn/functional.py", line 2693, in cross_entropy return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction) File "/home/bder73002/anaconda3/envs/python3.9.2/lib/python3.9/site-packages/torch/nn/functional.py", line 2388, in nll_loss ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index) RuntimeError: Expected object of scalar type Long but got scalar type Float for argument #2 'target' in call to _thnn_nll_loss_forward

cross_entropy函数要求输入的target必须是Long类型的张量，但是你传入的target是Float类型的标量。你需要将标签转换为Long类型的张量。可以使用以下代码将标签转换为Long类型： python targets = targets.long()...

自适应权重焦点损失：meta_focal_loss的Python实现

在这个文件中，我们将详细解析有关meta_focal_loss的知识点，这是一种使用焦点损失（Focal Loss）的自适应权重机制。为了确保能够理解和应用这些内容，我们将从以下几个方面入手： 1. 什么是焦点损失（Focal Loss）...

我想要减小分类损失，添加使用Focal Loss缓解类别不平衡问题。我将提供loss.py和metrics.py中的相关代码，请帮我进行补充，保证box_loss的减小，同时实现cls_loss的减小 loss.py中代码： def forward(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask): """IoU loss.""" weight = target_scores.sum(-1)[fg_mask].unsqueeze(-1) # iou = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask], xywh=False, CIoU=True) # loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum iou = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask],xywh=False, alpha_iou=True, alpha=3.0) loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum if type(iou) is tuple: if len(iou) == 2: loss_iou = ((1.0 - iou[0]) * iou[1].detach() * weight).sum() / target_scores_sum else: loss_iou = (iou[0] * iou[1] * weight).sum() / target_scores_sum else: loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum metrics.py中代码： def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False,alpha_iou=False,alpha=3.0, eps=1e-7): #中间不变 # IoU iou = inter / union if CIoU or DIoU or GIoU: cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 c2 = cw.pow(2) + ch.pow(2) + eps # convex diagonal squared rho2 = ( (b2_x1 + b2_x2 - b1_x1 - b1_x2).pow(2) + (b2_y1 + b2_y2 - b1_y1 - b1_y2).pow(2) ) / 4 # center dist2 if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 v = (4 / math.pi2) * ((w2 / h2).atan() - (w1 / h1).atan()).pow(2) with torch.no_grad(): alpha = v / (v - iou + (1 + eps)) return iou - (rho2 / c2 + v * alpha) # CIoU return iou - rho2 / c2 # DIoU c_area = cw * ch + eps # convex area return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf # 添加Alpha-IoU计算 if alpha_iou: alpha = alpha if alpha > 0 else 3.0 # 默认α=3 alpha_iou = 1 - ((1 - iou) ** alpha) # Alpha-IoU公式 return alpha_iou return iou # IoU

好的，我现在需要帮用户解决如何通过添加Focal Loss来减小分类损失，同时保证box_loss也减小的问题。用户提供了loss.py和metrics.py的相关代码，需要我进行补充。首先，我需要理解用户的需求和现有代码的结构。 ...

#算分类损失（FocalLoss） pred_cls = pred_dist[fg_mask] # 仅计算前景样本 target_cls = target_scores[fg_mask] if pred_cls.numel() > 0: loss_cls = self.focal_loss(pred_cls, target_cls).sum() / target_scores_sum else: # 避免无前景样本时除零 loss_cls = torch.tensor(0.0, device=pred_dist.device) return loss_iou, loss_dfl,loss_cls我在1在loss.py中的BboxLoss添加了代码，请帮我在bbox_iou进行相应的修改 # IoU iou = inter / union if CIoU or DIoU or GIoU: cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 c2 = cw.pow(2) + ch.pow(2) + eps # convex diagonal squared rho2 = ( (b2_x1 + b2_x2 - b1_x1 - b1_x2).pow(2) + (b2_y1 + b2_y2 - b1_y1 - b1_y2).pow(2) ) / 4 # center dist2 if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 v = (4 / math.pi2) * ((w2 / h2).atan() - (w1 / h1).atan()).pow(2) with torch.no_grad(): alpha = v / (v - iou + (1 + eps)) return iou - (rho2 / c2 + v * alpha) # CIoU return iou - rho2 / c2 # DIoU c_area = cw * ch + eps # convex area return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf # 添加Alpha-IoU计算 if alpha_iou: alpha = alpha if alpha > 0 else 3.0 # 默认α=3 alpha_iou = 1 - ((1 - iou) ** alpha) # Alpha-IoU公式 return alpha_iou # 添加Focal参数处理 return iou # IoU

好的，用户现在想在他们的BboxLoss类中添加FocalLoss相关的代码，并且在bbox_iou函数中进行相应的修改。用户提供的代码片段中有一些关于Alpha-IoU和Focal参数的处理，但看起来可能没有正确集成。我需要仔细分析他们...

Traceback (most recent call last): File "D:/YOLOV8/ultralytics-main/datasets/demo_2.py", line 10, in <module> results = model.train(data='data.yaml', epochs=100,workers=0,batch=16,imgsz=640,device=0, lr0= 0.001,weight_decay=0.05,mixup=0.2, hsv_h=0.015) File "D:\YOLOV8\ultralytics-main\ultralytics\engine\model.py", line 803, in train self.trainer.train() File "D:\YOLOV8\ultralytics-main\ultralytics\engine\trainer.py", line 207, in train self._do_train(world_size) File "D:\YOLOV8\ultralytics-main\ultralytics\engine\trainer.py", line 385, in _do_train self.loss, self.loss_items = self.model(batch) File "D:\anaconda\envs\yolo8\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "D:\YOLOV8\ultralytics-main\ultralytics\nn\tasks.py", line 111, in forward return self.loss(x, *args, **kwargs) File "D:\YOLOV8\ultralytics-main\ultralytics\nn\tasks.py", line 290, in loss self.criterion = self.init_criterion() File "D:\YOLOV8\ultralytics-main\ultralytics\nn\tasks.py", line 389, in init_criterion return E2EDetectLoss(self) if getattr(self, "end2end", False) else v8DetectionLoss(self) File "D:\YOLOV8\ultralytics-main\ultralytics\utils\loss.py", line 195, in init self.bbox_loss = BboxLoss(m.reg_max).to(device) File "D:\YOLOV8\ultralytics-main\ultralytics\utils\loss.py", line 99, in init self.focal_loss = FocalLoss(alpha=3.0, gamma=2.0) # 参数可调 TypeError: init() got an unexpected keyword argument 'alpha'

根本原因是当前代码库中的FocalLoss类构造函数不支持alpha参数。 --- ### **解决方案步骤** #### **1. 检查FocalLoss类定义** 打开ultralytics/utils/loss.py，找到FocalLoss类定义。典型错误情况如下： ...

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