RT-ODLab/models/detectors/yolovx/yolovx_pred.py

import torch
import torch.nn as nn


class SingleLevelPredLayer(nn.Module):
    def __init__(self, cls_dim, reg_dim, num_classes, num_coords=4):
        super().__init__()
        # --------- Basic Parameters ----------
        self.cls_dim = cls_dim
        self.reg_dim = reg_dim
        self.num_classes = num_classes
        self.num_coords = num_coords

        # --------- Network Parameters ----------
        self.obj_pred = nn.Conv2d(reg_dim, 1, kernel_size=1)
        self.cls_pred = nn.Conv2d(cls_dim, num_classes, kernel_size=1)
        self.reg_pred = nn.Conv2d(reg_dim, num_coords, kernel_size=1)                

        self.init_bias()
        

    def init_bias(self):
        # Init bias
        init_prob = 0.01
        bias_value = -torch.log(torch.tensor((1. - init_prob) / init_prob))
        # obj pred
        b = self.obj_pred.bias.view(1, -1)
        b.data.fill_(bias_value.item())
        self.obj_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
        # cls pred
        b = self.cls_pred.bias.view(1, -1)
        b.data.fill_(bias_value.item())
        self.cls_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
        # reg pred
        b = self.reg_pred.bias.view(-1, )
        b.data.fill_(1.0)
        self.reg_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
        w = self.reg_pred.weight
        w.data.fill_(0.)
        self.reg_pred.weight = torch.nn.Parameter(w, requires_grad=True)


    def forward(self, cls_feat, reg_feat):
        """
            in_feats: (Tensor) [B, C, H, W]
        """
        obj_pred = self.obj_pred(reg_feat)
        cls_pred = self.cls_pred(cls_feat)
        reg_pred = self.reg_pred(reg_feat)

        return obj_pred, cls_pred, reg_pred
    

class MultiLevelHead(nn.Module):
    def __init__(self, cls_dim, reg_dim, strides, num_classes, num_coords=4, num_levels=3):
        super().__init__()
        # --------- Basic Parameters ----------
        self.cls_dim = cls_dim
        self.reg_dim = reg_dim
        self.strides = strides
        self.num_classes = num_classes
        self.num_coords = num_coords
        self.num_levels = num_levels

        ## ----------- Network Parameters -----------
        self.pred_layers = nn.ModuleList(
            [SingleLevelPredLayer(
                cls_dim,
                reg_dim,
                num_classes,
                num_coords)
                for _ in range(num_levels)
            ])

    def generate_anchors(self, level, fmp_size):
        """
            fmp_size: (List) [H, W]
        """
        # generate grid cells
        fmp_h, fmp_w = fmp_size
        anchor_y, anchor_x = torch.meshgrid([torch.arange(fmp_h), torch.arange(fmp_w)])
        # [H, W, 2] -> [HW, 2]
        anchors = torch.stack([anchor_x, anchor_y], dim=-1).float().view(-1, 2)
        anchors += 0.5  # add center offset
        anchors *= self.strides[level]

        return anchors
        

    def decode_bbox(self, reg_pred, anchors, stride):
        ctr_pred = reg_pred[..., :2] * stride + anchors[..., :2]
        wh_pred = torch.exp(reg_pred[..., 2:]) * stride
        pred_x1y1 = ctr_pred - wh_pred * 0.5
        pred_x2y2 = ctr_pred + wh_pred * 0.5
        box_pred = torch.cat([pred_x1y1, pred_x2y2], dim=-1)

        return box_pred
    

    def forward(self, cls_feats, reg_feats):
        """
            feats: List[(Tensor)] [[B, C, H, W], ...]
        """
        all_anchors = []
        all_obj_preds = []
        all_cls_preds = []
        all_box_preds = []
        for level in range(self.num_levels):
            obj_pred, cls_pred, reg_pred = self.pred_layers[level](
                cls_feats[level], reg_feats[level])

            B, _, H, W = cls_pred.size()
            fmp_size = [H, W]
            # generate anchor boxes: [M, 4]
            anchors = self.generate_anchors(level, fmp_size)
            anchors = anchors.to(cls_pred.device)
            
            # [B, C, H, W] -> [B, H, W, C] -> [B, M, C]
            obj_pred = obj_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, 1)
            cls_pred = cls_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, self.num_classes)
            reg_pred = reg_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, 4)
            box_pred = self.decode_bbox(reg_pred, anchors, self.strides[level])

            all_obj_preds.append(obj_pred)
            all_cls_preds.append(cls_pred)
            all_box_preds.append(box_pred)
            all_anchors.append(anchors)

            # output dict
            outputs = {"pred_obj": all_obj_preds,        # List(Tensor) [B, M, 1]
                       "pred_cls": all_cls_preds,        # List(Tensor) [B, M, C]
                       "pred_box": all_box_preds,        # List(Tensor) [B, M, 4]
                       "anchors": all_anchors,           # List(Tensor) [B, M, 2]
                       "strides": self.strides}           # List(Int) [8, 16, 32]

        return outputs
    

# build detection head
def build_pred_layer(cls_dim, reg_dim, strides, num_classes, num_coords=4, num_levels=3):
    pred_layers = MultiLevelHead(cls_dim, reg_dim, strides, num_classes, num_coords, num_levels) 

    return pred_layers