目标检测dyhead代码核心代码

目标检测中的dyhead是指动态头部网络，是一种用于检测器中的一种模块，主要用于生成多个输出头，每个输出头都负责检测一种不同的目标类型或者是在不同的特征层上进行检测。以下是dyhead核心代码的示例：

class DynaHead(nn.Module):
    def __init__(self, cfg, in_channels):
        super(DynaHead, self).__init__()

        self.in_channels = in_channels
        self.num_classes = cfg.MODEL.NUM_CLASSES
        self.fpn_strides = cfg.MODEL.FPN.FPN_STRIDES
        self.num_heads = cfg.MODEL.DYNAHEAD.NUM_HEADS
        self.num_convs = cfg.MODEL.DYNAHEAD.NUM_CONVS
        self.prior_prob = cfg.MODEL.DYNAHEAD.PRIOR_PROB
        self.loss_weight = cfg.MODEL.DYNAHEAD.LOSS_WEIGHT

        self.cls_tower = nn.ModuleList()
        self.reg_tower = nn.ModuleList()

        for i in range(self.num_heads):
            cls_tower = []
            reg_tower = []
            for j in range(self.num_convs):
                if j == 0:
                    in_ch = self.in_channels
                else:
                    in_ch = self.num_features
                cls_tower.append(nn.Conv2d(
                    in_ch, self.num_features, kernel_size=3,
                    stride=1, padding=1, bias=True))
                cls_tower.append(nn.GroupNorm(32, self.num_features))
                cls_tower.append(nn.ReLU(inplace=True))
                reg_tower.append(nn.Conv2d(
                    in_ch, self.num_features, kernel_size=3,
                    stride=1, padding=1, bias=True))
                reg_tower.append(nn.GroupNorm(32, self.num_features))
                reg_tower.append(nn.ReLU(inplace=True))
            self.cls_tower.append(nn.Sequential(*cls_tower))
            self.reg_tower.append(nn.Sequential(*reg_tower))

        self.cls_logits = nn.ModuleList()
        self.bbox_pred = nn.ModuleList()
        for i in range(self.num_heads):
            self.cls_logits.append(nn.Conv2d(
                self.num_features, self.num_classes, kernel_size=3,
                stride=1, padding=1, bias=True))
            self.bbox_pred.append(nn.Conv2d(
                self.num_features, 4, kernel_size=3,
                stride=1, padding=1, bias=True))

        self._init_weights()

    def _init_weights(self):
        for modules in [self.cls_tower, self.reg_tower, self.cls_logits, self.bbox_pred]:
            for layer in modules.modules():
                if isinstance(layer, nn.Conv2d):
                    nn.init.normal_(layer.weight, std=0.01)
                    nn.init.constant_(layer.bias, 0)

        for modules in [self.cls_tower, self.reg_tower]:
            for layer in modules.modules():
                if isinstance(layer, nn.GroupNorm):
                    nn.init.constant_(layer.weight, 1)
                    nn.init.constant_(layer.bias, 0)

        prior_prob = self.prior_prob
        bias_value = -math.log((1 - prior_prob) / prior_prob)
        for modules in [self.cls_logits, self.bbox_pred]:
            for layer in modules.modules():
                if isinstance(layer, nn.Conv2d):
                    nn.init.normal_(layer.weight, std=0.01)
                    nn.init.constant_(layer.bias, bias_value)

    def forward(self, x):
        cls_logits = []
        bbox_pred = []
        for l, feature in enumerate(x):
            cls_feat = feature
            reg_feat = feature
            for i in range(self.num_convs):
                cls_feat = self.cls_tower[l][i*3](cls_feat)
                reg_feat = self.reg_tower[l][i*3](reg_feat)
            cls_logits.append(self.cls_logits[l](cls_feat))
            bbox_pred.append(torch.exp(self.bbox_pred[l](reg_feat)))
        return cls_logits, bbox_pred

在这个代码中，我们首先定义了 `DynaHead` 类，该类继承自 `nn.Module`。在该类的构造函数中，我们初始化了一些参数，例如输入通道数、类别数、FPN步长、输出头的数量以及每个头的卷积数量等。然后，我们使用 `nn.ModuleList` 将输出头的卷积模块和分类器模块分别存储在 `cls_tower` 和 `reg_tower` 中，并使用 `nn.ModuleList` 将分类器和回归器的输出层分别存储在 `cls_logits` 和 `bbox_pred` 中。

在 `forward` 函数中，我们遍历输入特征图 `x`，并为每个特征图生成类别和边界框预测。在每个特征图上，我们首先将特征图传递给对应的输出头的卷积模块和分类器模块，然后将其传递给分类器的输出层和回归器的输出层，最后将类别和边界框预测存储在 `cls_logits` 和 `bbox_pred` 中并返回。