如果我要改成focal loss要怎么改

如果您要将损失函数改为 Focal Loss，您需要在模型训练时使用 Focal Loss 代替传统的交叉熵损失函数。Focal Loss 是一种针对类别不平衡问题的损失函数，它可以有效地缓解类别不平衡问题，提高模型的分类准确率。具体实现方法可以参考论文《Focal Loss for Dense Object Detection》中的公式和代码实现。

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将SSD的损失函数改成focal loss的代码

Focal Loss是一种针对类别不平衡的损失函数，可以在训练过程中减少易分类样本的权重，从而提高模型对难分类样本的关注度。以下是将SSD的损失函数改成focal loss的代码：

import torch
import torch.nn as nn


class FocalLoss(nn.Module):
    def __init__(self, alpha=0.25, gamma=2, logits=True, reduction='mean'):
        super(FocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.logits = logits
        self.reduction = reduction

    def forward(self, inputs, targets):
        if self.logits:
            BCE_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction='none')
        else:
            BCE_loss = nn.functional.binary_cross_entropy(inputs, targets, reduction='none')

        pt = torch.exp(-BCE_loss)
        F_loss = self.alpha * (1 - pt) ** self.gamma * BCE_loss

        if self.reduction == 'mean':
            return torch.mean(F_loss)
        elif self.reduction == 'sum':
            return torch.sum(F_loss)
        else:
            return F_loss

class MultiBoxLoss(nn.Module):
    def __init__(self, num_classes, overlap_thresh, prior_for_matching,
                 bkg_label, neg_mining, neg_pos, neg_overlap, encode_target,
                 use_gpu=True):
        super(MultiBoxLoss, self).__init__()
        self.use_gpu = use_gpu
        self.num_classes = num_classes
        self.threshold = overlap_thresh
        self.background_label = bkg_label
        self.encode_target = encode_target
        self.use_prior_for_matching = prior_for_matching
        self.do_neg_mining = neg_mining
        self.negpos_ratio = neg_pos
        self.neg_overlap = neg_overlap
        self.variance = [0.1, 0.2]

        self.focal_loss = FocalLoss()

    def forward(self, predictions, targets):
        loc_data, conf_data, prior_data = predictions
        num = loc_data.size(0)
        num_priors = prior_data.size(0)

        loc_t = torch.Tensor(num, num_priors, 4)
        conf_t = torch.LongTensor(num, num_priors)
        for idx in range(num):
            truths = targets[idx][:, :-1].data
            labels = targets[idx][:, -1].data
            defaults = prior_data.data
            match(self.threshold, truths, defaults, self.variance, labels,
                  loc_t, conf_t, idx)

        if self.use_gpu:
            loc_t = loc_t.cuda()
            conf_t = conf_t.cuda()

        pos = conf_t > 0
        num_pos = pos.sum(dim=1, keepdim=True)

        # Localization Loss (Smooth L1)
        # Shape: [batch,num_priors,4]
        pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data)
        loc_p = loc_data[pos_idx].view(-1, 4)
        loc_t = loc_t[pos_idx].view(-1, 4)
        loss_l = nn.functional.smooth_l1_loss(loc_p, loc_t, reduction='sum')

        # Compute max conf across batch for hard negative mining
        batch_conf = conf_data.view(-1, self.num_classes)
        loss_c = self.focal_loss(batch_conf, conf_t.view(-1, 1))

        # Hard Negative Mining
        loss_c[pos] = 0  # filter out pos boxes for now
        loss_c = loss_c.view(num, -1)
        _, loss_idx = loss_c.sort(1, descending=True)
        _, idx_rank = loss_idx.sort(1)
        num_pos = pos.long().sum(1, keepdim=True)
        num_neg = torch.clamp(self.negpos_ratio * num_pos, max=pos.size(1) - 1)
        neg = idx_rank < num_neg.expand_as(idx_rank)

        # Confidence Loss Including Positive and Negative Examples
        pos_idx = pos.unsqueeze(2).expand_as(conf_data)
        neg_idx = neg.unsqueeze(2).expand_as(conf_data)
        conf_p = conf_data[(pos_idx + neg_idx).gt(0)].view(-1, self.num_classes)
        targets_weighted = conf_t[(pos + neg).gt(0)]
        loss_c = self.focal_loss(conf_p, targets_weighted)

        # Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
        N = num_pos.sum().float()
        loss_l /= N
        loss_c /= N

        return loss_l, loss_c

在MultiBoxLoss中，我们用focal_loss替换了原来的交叉熵损失函数。在FocalLoss中，我们计算每个样本的二元交叉熵损失，然后再乘以一个类别权重系数(1 - pt)^gamma，其中pt是预测概率的指数形式，gamma是一个可调参数，用于控制易分类样本的权重。最后，我们返回一个平均的损失值。在MultiBoxLoss中，我们计算了定位损失和分类损失，并将它们相加，再除以正样本的数量求取平均值。同时，我们采用了硬负样本挖掘策略，过滤掉难以分类的样本，提高模型的准确率。

把criterion = nn.CrossEntropyLoss().to(device)改成focal loss函数

可以通过以下代码将 criterion 改为 Focal Loss 函数：

import torch.nn.functional as F

class FocalLoss(nn.Module):
    def __init__(self, gamma=2.0, alpha=None, reduction='mean'):
        super(FocalLoss, self).__init__()
        self.gamma = gamma
        self.alpha = alpha
        self.reduction = reduction

    def forward(self, input, target):
        ce_loss = F.cross_entropy(input, target, reduction='none')
        pt = torch.exp(-ce_loss)
        focal_loss = ((1 - pt) ** self.gamma * ce_loss).mean()
        if self.alpha is not None:
            alpha = torch.tensor(self.alpha, device=input.device)
            focal_loss *= F.multi_label_soft_margin_loss(alpha * input.sigmoid(), target, reduction='mean')
        if self.reduction == 'mean':
            return focal_loss
        elif self.reduction == 'sum':
            return focal_loss.sum()
        else:
            return focal_loss

criterion = FocalLoss().to(device)

请注意，这里我们定义了一个新的 FocalLoss 类，并在此类中实现了 Focal Loss 函数。在这个函数中，我们首先计算交叉熵（CE）损失，然后计算 PT，最后使用 Focal Loss 公式计算损失。此外，我们还支持使用 alpha 权重以及不同的 reduction 选项。

最后，我们将 criterion 设置为 Focal Loss 并传入我们定义的设备（device）。