# TensorRT Engine 生成脚本
# 使用方法: python scripts/build_engine.py

import os
import sys
import argparse

project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, project_root)

import torch
from ultralytics import YOLO
import tensorrt as trt
import onnx


def build_engine(onnx_path, engine_path, fp16=True, dynamic_batch=True, imgsz=640):
    """构建TensorRT引擎"""
    logger = trt.Logger(trt.Logger.INFO)
    builder = trt.Builder(logger)

    network_flags = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
    network = builder.create_network(network_flags)

    parser = trt.OnnxParser(network, logger)
    with open(onnx_path, 'rb') as f:
        if not parser.parse(f.read()):
            for error in range(parser.num_errors):
                print(parser.get_error(error))
            raise RuntimeError("ONNX 解析失败")

    config = builder.create_builder_config()

    if dynamic_batch:
        profile = builder.create_optimization_profile()
        min_shape = (1, 3, imgsz, imgsz)
        opt_shape = (4, 3, imgsz, imgsz)
        max_shape = (8, 3, imgsz, imgsz)

        profile.set_shape("images", min_shape, opt_shape, max_shape)
        config.add_optimization_profile(profile)

    if fp16:
        config.set_flag(trt.BuilderFlag.FP16)

    serialized_engine = builder.build_serialized_network(network, config)

    with open(engine_path, "wb") as f:
        f.write(serialized_engine)

    print(f"TensorRT引擎已保存: {engine_path}")


def export_onnx(model_path, onnx_path, imgsz=640):
    """导出ONNX模型"""
    model = YOLO(model_path)
    model.export(
        format="onnx",
        imgsz=[imgsz, imgsz],
        simplify=True,
        opset=12,
        dynamic=True,
    )
    import shutil
    import glob
    onnx_files = glob.glob("yolo11n*.onnx")
    if onnx_files:
        shutil.move(onnx_files[0], onnx_path)
    print(f"ONNX模型已导出: {onnx_path}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="TensorRT Engine Builder")
    parser.add_argument("--model", type=str, default="models/yolo11n.pt",
                        help="YOLO模型路径")
    parser.add_argument("--engine", type=str, default="models/yolo11n.engine",
                        help="输出引擎路径")
    parser.add_argument("--onnx", type=str, default="models/yolo11n.onnx",
                        help="ONNX模型路径")
    parser.add_argument("--imgsz", type=int, default=640,
                        help="输入图像尺寸")
    parser.add_argument("--fp16", action="store_true", default=True,
                        help="启用FP16")
    parser.add_argument("--no-dynamic", action="store_true",
                        help="禁用动态Batch")

    args = parser.parse_args()

    os.makedirs(os.path.dirname(args.engine), exist_ok=True)
    onnx_dir = os.path.dirname(args.onnx) if os.path.dirname(args.onnx) else '.'
    os.makedirs(onnx_dir, exist_ok=True)

    if not os.path.exists(args.onnx):
        export_onnx(args.model, args.onnx, args.imgsz)

    build_engine(args.onnx, args.engine, args.fp16, not args.no_dynamic, args.imgsz)