RetinaNet是Focal Loss for Dense Object Detection这篇论文中提出的一种目标检测网络结构，该网络结构在相同的精度情况下提高了训练速度。RetinaNet基于Focal Loss分类器来加强正样本和负样本之间的区分度，同时引入了Focal Loss检测器来提高检测器的灵敏度。以下是RetinaNet网络结构的详细解析。
一、Anchor-based检测器
RetinaNet的目标检测器是一种Anchor-based检测器，其中Anchor是指在输入图像中的一组预先定义的框（或称锚定框），每个框都是有关尺度和长宽比的离散集合。框与像素之间的映射是通过网络中最后一个卷积层完成的，它将卷积层的特征图与原始输入图像之间生成了一个映射。检测器针对每个Anchor框执行了两个任务：首先，预测所属类别的概率值；其次，预测框向真实边界框的偏移量。在训练期间，对于每个Anchor框，如果预测结果与真实框匹配，则该Anchor框被视为正样本，否则该Anchor框被视为负样本。这种Anchor-based方法使模型可以对不同数量和尺度的物体进行识别和分割。 下面是RetinaNet的Anchor-based检测器的代码实现：
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分类器
Focal Loss是针对目标检测任务的一种修改后的二分类损失函数，它通过加权函数来缓解分类器在面对大量简单负样本（例如背景）时的鲁棒性问题。具体来说，该权重函数主要是在标准交叉熵损失中引入一个可调参数，该参数控制与正确分类相关的样本的权重值。当$\alpha$=0.5时，该权重函数将标准交叉熵损失还原为通用的交叉熵损失。Focal Loss通过对易分类的样本进行降权来使分类器更加关注难分类的样本。 下面是RetinaNet考虑Focal Loss的分类器的代码实现：
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检测器
RetinaNet引入了一个新的检测器，称为Focal Loss检测器，该检测器与Focal Loss分类器共同作用。具体来说，RetinaNet的Focal Loss检测器在分类时考虑了Focal Loss，这意味着该检测器在面对难分类样本时会更加关注，而忽略容易分类的样本。 下面是RetinaNet的Focal Loss检测器的代码实现：
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网络结构整合
最后，我们将RetinaNet网络结构从头到尾地整理一遍。整个网络结构包括了FPN、ClsHead、RegHead、Focal Loss和Smooth L1损失。其中，FPN生成了多个特征层，而ClsHead和RegHead分别预测类别概率和边框偏移。Focal Loss和Smooth L1损失作为网络的训练损失函数。 下面是整合后的RetinaNet网络结构代码实现：
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
