Hua
9b5f29083e
New solver subsystem with independent models: - GapDetectorCNN (1x128x256 grayscale → sigmoid) for slide gap detection - RotationRegressor (3x128x128 RGB → sin/cos via tanh) for rotation angle prediction - SlideSolver with 3-tier strategy: template match → edge detect → CNN fallback - RotateSolver with ONNX sin/cos → atan2 inference - Generators, training scripts, CLI commands, and slide track utility Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
83 lines
2.3 KiB
Python
83 lines
2.3 KiB
Python
"""
|
||
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
|