import torch
import torch.nn as nn

# -------------------- Detection Pred Layer --------------------
## Single-level pred layer
class AFDetPredLayer(nn.Module):
    def __init__(self,
                 cls_dim      :int,
                 reg_dim      :int,
                 stride       :int,
                 num_classes  :int,
                 ):
        super().__init__()
        # --------- Basic Parameters ----------
        self.stride  = stride
        self.cls_dim = cls_dim
        self.reg_dim = reg_dim
        self.num_classes = num_classes

        # --------- Network Parameters ----------
        self.cls_pred = nn.Conv2d(self.cls_dim, num_classes, kernel_size=1)
        self.reg_pred = nn.Conv2d(self.reg_dim, 4, 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))
        # 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]
        """
        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 = anchors + 0.5
        anchors = anchors * self.stride

        return anchors
        
    def forward(self, cls_feat, reg_feat):
        # 预测层
        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, H, W] -> [B, H, W, C] -> [B, H*W, C]
        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 = reg_pred[..., :2] * self.stride + anchors
        bwbh_pred = torch.exp(reg_pred[..., 2:]) * self.stride
        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_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

## Multi-level pred layer
class Yolov5AFDetPredLayer(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        # --------- Basic Parameters ----------
        self.cfg = cfg

        # ----------- Network Parameters -----------
        ## pred layers
        self.multi_level_preds = nn.ModuleList(
            [AFDetPredLayer(cls_dim      = round(cfg.head_dim * cfg.width),
                            reg_dim      = round(cfg.head_dim * cfg.width),
                            stride       = cfg.out_stride[level],
                            num_classes  = cfg.num_classes,)
                            for level in range(cfg.num_levels)
                            ])

    def forward(self, cls_feats, reg_feats):
        all_anchors = []
        all_fmp_sizes = []
        all_cls_preds = []
        all_reg_preds = []
        all_box_preds = []
        for level in range(self.cfg.num_levels):
            # -------------- Single-level prediction --------------
            outputs = self.multi_level_preds[level](cls_feats[level], reg_feats[level])

            # collect results
            all_cls_preds.append(outputs["pred_cls"])
            all_reg_preds.append(outputs["pred_reg"])
            all_box_preds.append(outputs["pred_box"])
            all_fmp_sizes.append(outputs["fmp_size"])
            all_anchors.append(outputs["anchors"])
        
        # output dict
        outputs = {"pred_cls":  all_cls_preds,         # List(Tensor) [B, M, C]
                   "pred_reg":  all_reg_preds,         # List(Tensor) [B, M, 4*(reg_max)]
                   "pred_box":  all_box_preds,         # List(Tensor) [B, M, 4]
                   "fmp_sizes": all_fmp_sizes,         # List(Tensor) [M, 1]
                   "anchors":   all_anchors,           # List(Tensor) [M, 2]
                   "strides":   self.cfg.out_stride,   # List(Int) = [8, 16, 32]
                   }

        return outputs