RetinaNet網路結構詳解

RetinaNet是Focal Loss for Dense Object Detection這篇論文中提出的一種目標檢測網路結構，該網路結構在相同的精度情況下提高了訓練速度。RetinaNet基於Focal Loss分類器來加強正樣本和負樣本之間的區分度，同時引入了Focal Loss檢測器來提高檢測器的靈敏度。以下是RetinaNet網路結構的詳細解析。

一、Anchor-based檢測器

RetinaNet的目標檢測器是一種Anchor-based檢測器，其中Anchor是指在輸入圖像中的一組預先定義的框（或稱錨定框），每個框都是有關尺度和長寬比的離散集合。框與像素之間的映射是通過網路中最後一個卷積層完成的，它將卷積層的特徵圖與原始輸入圖像之間生成了一個映射。檢測器針對每個Anchor框執行了兩個任務：首先，預測所屬類別的概率值；其次，預測框向真實邊界框的偏移量。在訓練期間，對於每個Anchor框，如果預測結果與真實框匹配，則該Anchor框被視為正樣本，否則該Anchor框被視為負樣本。這種Anchor-based方法使模型可以對不同數量和尺度的物體進行識別和分割。

下面是RetinaNet的Anchor-based檢測器的代碼實現：

class RetinaNet(nn.Module):
    def __init__(self):
        super(RetinaNet, self).__init__()

        self.fpn = FPN()
        self.cls_head = ClsHead()
        self.reg_head = RegHead()

    def forward(self, x):
        out = self.fpn(x)
        cls_out = []
        reg_out = []

        for feature in out:
            cls_out.append(self.cls_head(feature))
            reg_out.append(self.reg_head(feature))

        return tuple(cls_out), tuple(reg_out)

二、Focal Loss分類器

Focal Loss是針對目標檢測任務的一種修改後的二分類損失函數，它通過加權函數來緩解分類器在面對大量簡單負樣本（例如背景）時的魯棒性問題。具體來說，該權重函數主要是在標準交叉熵損失中引入一個可調參數，該參數控制與正確分類相關的樣本的權重值。當$\alpha$=0.5時，該權重函數將標準交叉熵損失還原為通用的交叉熵損失。Focal Loss通過對易分類的樣本進行降權來使分類器更加關注難分類的樣本。

下面是RetinaNet考慮Focal Loss的分類器的代碼實現：

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

        self.alpha = alpha
        self.gamma = gamma
        self.reduction = reduction

    def forward(self, cls_pred, cls_targets):
        pos_inds = cls_targets > 0
        neg_inds = cls_targets == 0

        pos_pred = cls_pred[pos_inds]
        neg_pred = cls_pred[neg_inds]

        pos_loss = -pos_pred.log() * (1 - pos_pred) ** self.gamma * self.alpha
        neg_loss = -neg_pred.log() * (neg_pred) ** self.gamma * (1 - self.alpha)

        if self.reduction == 'mean':
            num_pos = pos_inds.float().sum()
            pos_loss = pos_loss.sum()
            neg_loss = neg_loss.sum()
            loss = (pos_loss + neg_loss) / num_pos.clamp(min=1)
        else:
            loss = pos_loss.sum() + neg_loss.sum()

        return loss

三、Focal Loss檢測器

RetinaNet引入了一個新的檢測器，稱為Focal Loss檢測器，該檢測器與Focal Loss分類器共同作用。具體來說，RetinaNet的Focal Loss檢測器在分類時考慮了Focal Loss，這意味著該檢測器在面對難分類樣本時會更加關注，而忽略容易分類的樣本。

下面是RetinaNet的Focal Loss檢測器的代碼實現：

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

        self.cls_loss = FocalLoss(alpha, gamma, reduction=reduction)
        self.reg_loss = nn.SmoothL1Loss(reduction=reduction)

    def forward(self, cls_out, reg_out, cls_targets, reg_targets):
        cls_losses = []
        reg_losses = []

        for cls_pred, reg_pred, cls_target, reg_target, in zip(cls_out, reg_out, cls_targets, reg_targets):
            pos_inds = cls_target > 0
            num_pos = pos_inds.float().sum()

            cls_loss = self.cls_loss(cls_pred, cls_target)
            reg_loss = self.reg_loss(pos_pred, pos_target, )

            cls_losses.append(cls_loss)
            reg_losses.append(reg_loss)

        cls_loss = sum(cls_losses) / len(cls_losses)
        reg_loss = sum(reg_losses) / len(reg_losses)

        loss = cls_loss + reg_loss

        return loss

四、RetinaNet網路結構整合

最後，我們將RetinaNet網路結構從頭到尾地整理一遍。整個網路結構包括了FPN、ClsHead、RegHead、Focal Loss和Smooth L1損失。其中，FPN生成了多個特徵層，而ClsHead和RegHead分別預測類別概率和邊框偏移。Focal Loss和Smooth L1損失作為網路的訓練損失函數。

下面是整合後的RetinaNet網路結構代碼實現：

class FPN(nn.Module):
    def __init__(self):
        super(FPN, self).__init__()

        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1)
        self.conv7 = nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1)
        self.conv8 = nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1)
        self.conv9 = nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1)

        self.latent3 = nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)
        self.latent4 = nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)
        self.latent5 = nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)

        self.pred3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.pred4 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.pred5 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        conv3, conv4, conv5, conv6, conv7, conv8, conv9 = x

        lat3 = self.latent3(conv3)
        lat4 = self.latent4(conv4)
        lat5 = self.latent5(conv5)

        p5 = self.pred5(lat5)
        p4 = self.pred4(lat4 + F.interpolate(p5, size=lat4.size()[-2:], mode='nearest'))
        p3 = self.pred3(lat3 + F.interpolate(p4, size=lat3.size()[-2:], mode='nearest'))

        p6 = self.conv6(conv6)
        p7 = self.conv7(F.relu(p6))
        p8 = self.conv8(F.relu(p7))
        p9 = self.conv9(F.relu(p8))

        return p3, p4, p5, p6, p7, p8, p9


class ClsHead(nn.Module):
    def __init__(self):
        super(ClsHead, self).__init__()

        self.output = nn.Conv2d(256, 9, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        x = self.output(x)
        x = x.permute(0, 2, 3, 1)
        x = x.reshape(x.shape[0], -1, 1)

        return x


class RegHead(nn.Module):
    def __init__(self):
        super(RegHead, self).__init__()

        self.output = nn.Conv2d(256, 36, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        x = self.output(x)
        x = x.permute(0, 2, 3, 1)
        x = x.reshape(x.shape[0], -1, 4)

        return x


class RetinaNet(nn.Module):
    def __init__(self):
        super(RetinaNet, self).__init__()

        self.fpn = FPN()
        self.cls_head = ClsHead()
        self.reg_head = RegHead()

        self.focal_loss_detection = FocalLossDetection()

    def forward(self, x, cls_targets, reg_targets):
        out = self.fpn(x)
        cls_out = []
        reg_out = []

        for feature in out:
            cls_out.append(self.cls_head(feature))
            reg_out.append(self.reg_head(feature))

        loss = self.focal_loss_detection(cls_out, reg_out, cls_targets, reg_targets)

        return loss, cls_out, reg_out