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

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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>
This commit is contained in:
Hua
2026-03-11 13:55:53 +08:00
parent 760b80ee5e
commit f5be7671bc
20 changed files with 1109 additions and 142 deletions
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25
inference/export_onnx.py
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@@ -17,10 +17,12 @@ from config import (
MATH_CHARS,
THREED_CHARS,
NUM_CAPTCHA_TYPES,
REGRESSION_RANGE,
)
from models.classifier import CaptchaClassifier
from models.lite_crnn import LiteCRNN
from models.threed_cnn import ThreeDCNN
from models.regression_cnn import RegressionCNN
def export_model(
@@ -34,7 +36,7 @@ def export_model(
Args:
model: 已加载权重的 PyTorch 模型
model_name: 模型名 (classifier / normal / math / threed)
model_name: 模型名 (classifier / normal / math / threed_text / threed_rotate / threed_slider)
input_shape: 输入形状 (C, H, W)
onnx_dir: 输出目录 (默认使用 config.ONNX_DIR)
"""
@@ -50,7 +52,7 @@ def export_model(
dummy = torch.randn(1, *input_shape)
# 分类器和识别器的 dynamic_axes 不同
if model_name == "classifier":
if model_name == "classifier" or model_name in ("threed_rotate", "threed_slider"):
dynamic_axes = {"input": {0: "batch"}, "output": {0: "batch"}}
else:
# CTC 模型: output shape = (T, B, C)
@@ -78,7 +80,8 @@ def _load_and_export(model_name: str):
return
ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True)
print(f"[加载] {model_name}: epoch={ckpt.get('epoch', '?')} acc={ckpt.get('best_acc', '?')}")
acc_info = ckpt.get('best_acc') or ckpt.get('best_tol_acc', '?')
print(f"[加载] {model_name}: epoch={ckpt.get('epoch', '?')} acc={acc_info}")
if model_name == "classifier":
model = CaptchaClassifier(num_types=NUM_CAPTCHA_TYPES)
@@ -94,11 +97,19 @@ def _load_and_export(model_name: str):
h, w = IMAGE_SIZE["math"]
model = LiteCRNN(chars=chars, img_h=h, img_w=w)
input_shape = (1, h, w)
elif model_name == "threed":
elif model_name == "threed_text":
chars = ckpt.get("chars", THREED_CHARS)
h, w = IMAGE_SIZE["3d"]
h, w = IMAGE_SIZE["3d_text"]
model = ThreeDCNN(chars=chars, img_h=h, img_w=w)
input_shape = (1, h, w)
elif model_name == "threed_rotate":
h, w = IMAGE_SIZE["3d_rotate"]
model = RegressionCNN(img_h=h, img_w=w)
input_shape = (1, h, w)
elif model_name == "threed_slider":
h, w = IMAGE_SIZE["3d_slider"]
model = RegressionCNN(img_h=h, img_w=w)
input_shape = (1, h, w)
else:
print(f"[错误] 未知模型: {model_name}")
return
@@ -108,11 +119,11 @@ def _load_and_export(model_name: str):
def export_all():
"""依次导出 classifier, normal, math, threed 四个模型。"""
"""依次导出 classifier, normal, math, threed_text, threed_rotate, threed_slider 六个模型。"""
print("=" * 50)
print("导出全部 ONNX 模型")
print("=" * 50)
for name in ["classifier", "normal", "math", "threed"]:
for name in ["classifier", "normal", "math", "threed_text", "threed_rotate", "threed_slider"]:
_load_and_export(name)
print("\n全部导出完成。")