YOLO-Tutorial-v2/models/yolov2/yolov2_pred.py

import torch
import torch.nn as nn


# -------------------- Detection Pred Layer --------------------
## Single-level pred layer
class Yolov2DetPredLayer(nn.Module):
    def __init__(self, cfg, num_classes):
        super().__init__()
        # --------- Basic Parameters ----------
        self.stride  = cfg.out_stride
        self.cls_dim = cfg.head_dim
        self.reg_dim = cfg.head_dim
        self.num_classes = cfg.num_classes
        # ------------------- Anchor box -------------------
        self.anchor_size = torch.as_tensor(cfg.anchor_sizes).float().view(-1, 2) # [A, 2]
        self.num_anchors = self.anchor_size.shape[0]

        # --------- Network Parameters ----------
        self.obj_pred = nn.Conv2d(self.cls_dim, 1 * self.num_anchors, kernel_size=1)
        self.cls_pred = nn.Conv2d(self.cls_dim, num_classes * self.num_anchors, kernel_size=1)
        self.reg_pred = nn.Conv2d(self.reg_dim, 4 * self.num_anchors, 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)

    def generate_anchors(self, fmp_size):
        """
            fmp_size: (List) [H, W]
        """
        # 特征图的宽和高
        fmp_h, fmp_w = fmp_size

        # 生成网格的x坐标和y坐标
        anchor_y, anchor_x = torch.meshgrid([torch.arange(fmp_h), torch.arange(fmp_w)])

        # 将xy两部分的坐标拼起来：[H, W, 2] -> [HW, 2]
        anchor_xy = torch.stack([anchor_x, anchor_y], dim=-1).float().view(-1, 2)
        # [HW, 2] -> [HW, A, 2] -> [M, 2], M=HWA
        anchor_xy = anchor_xy.unsqueeze(1).repeat(1, self.num_anchors, 1)
        anchor_xy = anchor_xy.view(-1, 2)

        # [A, 2] -> [1, A, 2] -> [HW, A, 2] -> [M, 2], M=HWA
        anchor_wh = self.anchor_size.unsqueeze(0).repeat(fmp_h*fmp_w, 1, 1)
        anchor_wh = anchor_wh.view(-1, 2)

        anchors = torch.cat([anchor_xy, anchor_wh], dim=-1)

        return anchors
        
    def forward(self, cls_feat, reg_feat):
        # 预测层
        obj_pred = self.obj_pred(cls_feat)
        cls_pred = self.cls_pred(cls_feat)
        reg_pred = self.reg_pred(reg_feat)

        # 生成网格坐标
        B, _, H, W = cls_pred.size()
        fmp_size = [H, W]
        anchors = self.generate_anchors(fmp_size)
        anchors = anchors.to(cls_pred.device)

        # 对 pred 的size做一些view调整，便于后续的处理
        # [B, C*A, H, W] -> [B, H, W, C*A] -> [B, H*W*A, 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)
        
        # 解算边界框坐标
        cxcy_pred = (torch.sigmoid(reg_pred[..., :2]) + anchors[..., :2]) * self.stride
        bwbh_pred = torch.exp(reg_pred[..., 2:]) * anchors[..., 2:]
        pred_x1y1 = cxcy_pred - bwbh_pred * 0.5
        pred_x2y2 = cxcy_pred + bwbh_pred * 0.5
        box_pred = torch.cat([pred_x1y1, pred_x2y2], dim=-1)

        # output dict
        outputs = {"pred_obj": obj_pred,       # (torch.Tensor) [B, M, 1]
                   "pred_cls": cls_pred,       # (torch.Tensor) [B, M, C]
                   "pred_reg": reg_pred,       # (torch.Tensor) [B, M, 4]
                   "pred_box": box_pred,       # (torch.Tensor) [B, M, 4]
                   "anchors" : anchors,        # (torch.Tensor) [M, 2]
                   "fmp_size": fmp_size,
                   "stride"  : self.stride,    # (Int)
                   }

        return outputs