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_utils.py

@@ -1,7 +1,7 @@
 """
 CTC 训练通用逻辑
 
-提供 train_ctc_model() 函数，被 train_normal / train_math / train_3d 共用。
+提供 train_ctc_model() 函数，被 train_normal / train_math / train_3d_text 共用。
 职责：
 1. 检查合成数据，不存在则自动调用生成器
 2. 构建 Dataset / DataLoader（含真实数据混合）
@@ -12,8 +12,10 @@ CTC 训练通用逻辑
 """
 
 import os
+import random
 from pathlib import Path
 
+import numpy as np
 import torch
 import torch.nn as nn
 from torch.utils.data import DataLoader, random_split
@@ -25,11 +27,21 @@ from config import (
     ONNX_CONFIG,
     TRAIN_CONFIG,
     IMAGE_SIZE,
+    RANDOM_SEED,
     get_device,
 )
 from training.dataset import CRNNDataset, build_train_transform, build_val_transform
 
 
+def _set_seed(seed: int = RANDOM_SEED):
+    """设置全局随机种子，保证训练可复现。"""
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+
+
 # ============================================================
 # 准确率计算
 # ============================================================
@@ -104,9 +116,12 @@ def train_ctc_model(
         config_key:    TRAIN_CONFIG 中的键名
     """
     cfg = TRAIN_CONFIG[config_key]
-    img_h, img_w = IMAGE_SIZE[config_key if config_key != "threed" else "3d"]
+    img_h, img_w = IMAGE_SIZE[config_key]
     device = get_device()
 
+    # 设置随机种子
+    _set_seed()
+
     # ---- 1. 检查 / 生成合成数据 ----
     syn_path = Path(synthetic_dir)
     existing = list(syn_path.glob("*.png"))
@@ -139,11 +154,11 @@ def train_ctc_model(
 
     train_loader = DataLoader(
         train_ds, batch_size=cfg["batch_size"], shuffle=True,
-        num_workers=2, collate_fn=CRNNDataset.collate_fn, pin_memory=True,
+        num_workers=0, collate_fn=CRNNDataset.collate_fn, pin_memory=True,
     )
     val_loader = DataLoader(
         val_ds_clean, batch_size=cfg["batch_size"], shuffle=False,
-        num_workers=2, collate_fn=CRNNDataset.collate_fn, pin_memory=True,
+        num_workers=0, collate_fn=CRNNDataset.collate_fn, pin_memory=True,
     )
 
     print(f"[数据] 训练: {train_size}  验证: {val_size}")
@@ -166,12 +181,11 @@ def train_ctc_model(
         pbar = tqdm(train_loader, desc=f"Epoch {epoch}/{cfg['epochs']}", leave=False)
         for images, targets, target_lengths, _ in pbar:
             images = images.to(device)
-            targets = targets.to(device)
-            target_lengths = target_lengths.to(device)
 
             logits = model(images)  # (T, B, C)
             T, B, C = logits.shape
-            input_lengths = torch.full((B,), T, dtype=torch.int32, device=device)
+            # cuDNN CTC requires targets/lengths on CPU
+            input_lengths = torch.full((B,), T, dtype=torch.int32)
 
             log_probs = logits.log_softmax(2)
             loss = ctc_loss(log_probs, targets, input_lengths, target_lengths)