Добавлены пропсы конвейера и стереодвижки, задействованные в прогоне

assets/conveyors (274 МБ) - ленты и угловая секция NVIDIA, на которые ссылается сцена относительным путём. Раньше исключались как перекачиваемые, но без них сцена не композится из коробки. cv/ - код стереодвижков, которые вызывает control_test, без весов: * defom-stereo - рабочий бейзлайн (DEFOM vitl, вход 480, iters 24) * crestereo - второй движок, точнее по габаритам (MAE 23.5 против 32.8 мм) * fast-foundationstereo - проверялся, в бейзлайн не вошёл * circular_section.py - показатель кругового сечения, перенесён в measure_plane.py: выравнивает облако по СОБСТВЕННЫМ главным осям и режет на пяти высотах вдоль каждой. Три самодельные версии (мировые оси, одно сечение) давали хуже; результаты проверки на эталонной геометрии - в circular_section_results.json Веса по-прежнему не в репозитории - источники в MODELS.md. Наборы кадров прежних прогонов (cv/flow_*, 1.26 ГБ) исключены: это выход, а не исходники. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 13:12:07 +00:00
parent 0d32f32db0
commit 6e1a22ba8b
184 changed files with 17666 additions and 3 deletions
@@ -0,0 +1,150 @@
+#!/usr/bin/env python3
+"""Build a TensorRT engine from the FFSGWCVolume plugin ONNX.
+
+This mirrors cpp/app/build_single_engine.cpp in Python. The ONNX parser still
+needs the custom FFSGWCVolume plugin creator registered before parsing, so the
+shared plugin library is auto-detected from cpp/build or can be passed with
+--plugin_lib.
+"""
+
+import argparse
+import ctypes
+import os
+from pathlib import Path
+
+
+PLUGIN_NAME = "FFSGWCVolume"
+PLUGIN_VERSION = "1"
+_LOADED_PLUGIN_LIBS = []
+
+
+def find_default_plugin_library() -> Path | None:
+    repo_dir = Path(__file__).resolve().parents[1]
+    candidates = [
+        repo_dir / "cpp" / "build" / "libffs_gwc_plugin.so",
+        repo_dir / "cpp" / "build" / "lib" / "libffs_gwc_plugin.so",
+        repo_dir / "cpp" / "build" / "Release" / "libffs_gwc_plugin.so",
+    ]
+    for path in candidates:
+        if path.exists():
+            return path
+    return None
+
+
+def load_plugin_library(path: str) -> None:
+    """Load an optional shared library that registers FFSGWCVolume."""
+    lib = ctypes.CDLL(path, mode=ctypes.RTLD_GLOBAL)
+    _LOADED_PLUGIN_LIBS.append(lib)
+
+    # The current C++ code registers via ffs_depth::registerFFSGWCPlugin().
+    # A loadable Python plugin library should expose an extern "C" wrapper with
+    # one of these names so ctypes can call it without C++ name mangling.
+    for symbol in ("ffs_register_gwc_plugin", "registerFFSGWCPlugin"):
+        try:
+            fn = getattr(lib, symbol)
+        except AttributeError:
+            continue
+        fn.restype = ctypes.c_bool
+        if not fn():
+            raise RuntimeError(f"{symbol}() returned false for {path}")
+        return
+
+    # Some TensorRT plugin libraries register creators during library load. That
+    # is not true for this repo's current static C++ helper, but allow it here.
+
+
+def find_plugin_creator(trt) -> bool:
+    registry = trt.get_plugin_registry()
+    creator = registry.get_plugin_creator(PLUGIN_NAME, PLUGIN_VERSION, "")
+    return creator is not None
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Build a TensorRT engine from an ONNX graph containing FFSGWCVolume."
+    )
+    parser.add_argument("plugin_onnx", type=Path, help="Path to plugin ONNX file")
+    parser.add_argument("output_engine", type=Path, help="Path to write TensorRT engine")
+    parser.add_argument(
+        "--plugin_lib",
+        type=Path,
+        default=None,
+        help=(
+            "Shared library that registers FFSGWCVolume. Defaults to "
+            "cpp/build/libffs_gwc_plugin.so when present. Pure Python cannot "
+            "provide this repo's CUDA plugin implementation."
+        ),
+    )
+    parser.add_argument(
+        "--fp32",
+        action="store_true",
+        help="Disable FP16 builder flag. Default allows FP16 when supported.",
+    )
+    parser.add_argument(
+        "--workspace-mb",
+        type=int,
+        default=4096,
+        help="TensorRT workspace memory limit in MiB.",
+    )
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    if not args.plugin_onnx.exists():
+        raise FileNotFoundError(f"ONNX file does not exist: {args.plugin_onnx}")
+    args.output_engine.parent.mkdir(parents=True, exist_ok=True)
+
+    import tensorrt as trt
+
+    logger = trt.Logger(trt.Logger.INFO)
+    trt.init_libnvinfer_plugins(logger, "")
+
+    plugin_lib = args.plugin_lib or find_default_plugin_library()
+    if plugin_lib is not None:
+        if not plugin_lib.exists():
+            raise FileNotFoundError(f"Plugin library does not exist: {plugin_lib}")
+        load_plugin_library(str(plugin_lib))
+
+    if not find_plugin_creator(trt):
+        raise RuntimeError(
+            f"{PLUGIN_NAME} plugin creator is not registered. "
+            "Build/load a shared library for cpp/src/gwc_volume_plugin.cpp and pass "
+            "--plugin_lib, or use cpp/build/ffs_build_single_engine."
+        )
+
+    explicit_batch = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
+    builder = trt.Builder(logger)
+    network = builder.create_network(explicit_batch)
+    parser = trt.OnnxParser(network, logger)
+
+    parsed = False
+    if hasattr(parser, "parse_from_file"):
+        parsed = parser.parse_from_file(str(args.plugin_onnx))
+    else:
+        parsed = parser.parse(args.plugin_onnx.read_bytes())
+    if not parsed:
+        for i in range(parser.num_errors):
+            print(parser.get_error(i))
+        raise RuntimeError(f"failed to parse ONNX: {args.plugin_onnx}")
+
+    config = builder.create_builder_config()
+    config.set_memory_pool_limit(
+        trt.MemoryPoolType.WORKSPACE, int(args.workspace_mb) * 1024 * 1024
+    )
+    if not args.fp32 and builder.platform_has_fast_fp16:
+        config.set_flag(trt.BuilderFlag.FP16)
+
+    serialized = builder.build_serialized_network(network, config)
+    if serialized is None:
+        raise RuntimeError("build_serialized_network failed")
+
+    args.output_engine.write_bytes(bytes(serialized))
+    precision = "FP32" if args.fp32 else "FP16 allowed"
+    print(f"Built engine: {args.output_engine}")
+    print(f"Precision: {precision}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())