Expand 3D captcha into three subtypes: 3d_text, 3d_rotate, 3d_slider

Split the single "3d" captcha type into three independent expert models:
- 3d_text: 3D perspective text OCR (renamed from old "3d", CTC-based ThreeDCNN)
- 3d_rotate: rotation angle regression (new RegressionCNN, circular loss)
- 3d_slider: slider offset regression (new RegressionCNN, SmoothL1 loss)

CAPTCHA_TYPES expanded from 3 to 5 classes. Classifier samples updated
to 50000 (10000 per class). New generators, model, dataset, training
utilities, and full pipeline/export/CLI support for all subtypes.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

training/train_classifier.py

@@ -1,16 +1,18 @@
 """
 训练调度分类器 (CaptchaClassifier)
 
-从各类型验证码数据中混合采样，训练分类器区分 normal / math / 3d。
+从各类型验证码数据中混合采样，训练分类器区分 normal / math / 3d_text / 3d_rotate / 3d_slider。
 数据来源: data/classifier/ 目录 (按类型子目录组织)
 
 用法: python -m training.train_classifier
 """
 
 import os
+import random
 import shutil
 from pathlib import Path
 
+import numpy as np
 import torch
 import torch.nn as nn
 from torch.utils.data import DataLoader, random_split
@@ -24,15 +26,20 @@ from config import (
     CLASSIFIER_DIR,
     SYNTHETIC_NORMAL_DIR,
     SYNTHETIC_MATH_DIR,
-    SYNTHETIC_3D_DIR,
+    SYNTHETIC_3D_TEXT_DIR,
+    SYNTHETIC_3D_ROTATE_DIR,
+    SYNTHETIC_3D_SLIDER_DIR,
     CHECKPOINTS_DIR,
     ONNX_DIR,
     ONNX_CONFIG,
+    RANDOM_SEED,
     get_device,
 )
 from generators.normal_gen import NormalCaptchaGenerator
 from generators.math_gen import MathCaptchaGenerator
 from generators.threed_gen import ThreeDCaptchaGenerator
+from generators.threed_rotate_gen import ThreeDRotateGenerator
+from generators.threed_slider_gen import ThreeDSliderGenerator
 from models.classifier import CaptchaClassifier
 from training.dataset import CaptchaDataset, build_train_transform, build_val_transform
 
@@ -52,7 +59,9 @@ def _prepare_classifier_data():
     type_info = [
         ("normal", SYNTHETIC_NORMAL_DIR, NormalCaptchaGenerator),
         ("math", SYNTHETIC_MATH_DIR, MathCaptchaGenerator),
-        ("3d", SYNTHETIC_3D_DIR, ThreeDCaptchaGenerator),
+        ("3d_text", SYNTHETIC_3D_TEXT_DIR, ThreeDCaptchaGenerator),
+        ("3d_rotate", SYNTHETIC_3D_ROTATE_DIR, ThreeDRotateGenerator),
+        ("3d_slider", SYNTHETIC_3D_SLIDER_DIR, ThreeDSliderGenerator),
     ]
 
     for cls_name, syn_dir, gen_cls in type_info:
@@ -95,6 +104,13 @@ def main():
     img_h, img_w = IMAGE_SIZE["classifier"]
     device = get_device()
 
+    # 设置随机种子
+    random.seed(RANDOM_SEED)
+    np.random.seed(RANDOM_SEED)
+    torch.manual_seed(RANDOM_SEED)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(RANDOM_SEED)
+
     print("=" * 60)
     print("训练调度分类器 (CaptchaClassifier)")
     print(f"  类别: {CAPTCHA_TYPES}")
@@ -128,11 +144,11 @@ def main():
 
     train_loader = DataLoader(
         train_ds, batch_size=cfg["batch_size"], shuffle=True,
-        num_workers=2, pin_memory=True,
+        num_workers=0, pin_memory=True,
     )
     val_loader = DataLoader(
         val_ds_clean, batch_size=cfg["batch_size"], shuffle=False,
-        num_workers=2, pin_memory=True,
+        num_workers=0, pin_memory=True,
     )
 
     print(f"[数据] 训练: {train_size}  验证: {val_size}")