"""
旋转角度回归模型 (RotationRegressor)

用于预测旋转验证码的正确旋转角度。
使用 sin/cos 编码避免 0°/360° 边界问题。
RGB 输入，输出 (sin θ, cos θ) ∈ [-1,1]。

架构:
    Conv(3→32) + BN + ReLU + Pool
    Conv(32→64) + BN + ReLU + Pool
    Conv(64→128) + BN + ReLU + Pool
    Conv(128→256) + BN + ReLU + Pool
    AdaptiveAvgPool2d(1) → FC(256→128) → ReLU → FC(128→2) → Tanh

约 400K 参数，~2MB。
"""

import torch
import torch.nn as nn


class RotationRegressor(nn.Module):
    """
    旋转角度回归模型。

    RGB 输入 3x128x128，输出 (sin θ, cos θ)。
    推理时用 atan2(sin, cos) 转换为角度。
    """

    def __init__(self, img_h: int = 128, img_w: int = 128):
        super().__init__()
        self.img_h = img_h
        self.img_w = img_w

        self.features = nn.Sequential(
            # block 1: 3 → 32, H/2, W/2
            nn.Conv2d(3, 32, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, 2),

            # block 2: 32 → 64, H/4, W/4
            nn.Conv2d(32, 64, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, 2),

            # block 3: 64 → 128, H/8, W/8
            nn.Conv2d(64, 128, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, 2),

            # block 4: 128 → 256, H/16, W/16
            nn.Conv2d(128, 256, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, 2),
        )

        self.pool = nn.AdaptiveAvgPool2d(1)

        self.regressor = nn.Sequential(
            nn.Linear(256, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, 2),
            nn.Tanh(),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x: (batch, 3, H, W) RGB 图

        Returns:
            output: (batch, 2) → (sin θ, cos θ) ∈ [-1, 1]
        """
        feat = self.features(x)
        feat = self.pool(feat)          # (B, 256, 1, 1)
        feat = feat.flatten(1)          # (B, 256)
        out = self.regressor(feat)      # (B, 2)
        return out