detections/scripts/jetson/generate_int8_cache.py

#!/usr/bin/env python3
"""
Generate an INT8 calibration cache for TensorRT on Jetson.

Run this INSIDE the Jetson Docker container:

  docker compose -f docker-compose.demo-jetson.yml run --rm \
    -v /path/to/images:/calibration \
    detections \
    python3 scripts/generate_int8_cache.py \
      --images-dir /calibration \
      --onnx /models/azaion.onnx \
      --output /models/azaion.int8_calib.cache

The cache file must be in the loader's models volume so the detections
service can download it on startup via the Loader API.
"""
import argparse
import sys
from pathlib import Path

import cv2
import numpy as np


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--images-dir", required=True, help="Directory with calibration images (JPG/PNG)")
    parser.add_argument("--onnx", required=True, help="Path to azaion.onnx")
    parser.add_argument("--output", default="azaion.int8_calib.cache")
    parser.add_argument("--input-size", type=int, default=1280, help="Model input H=W (default 1280)")
    parser.add_argument("--num-samples", type=int, default=500)
    return parser.parse_args()


def collect_images(images_dir: str, num_samples: int) -> list[Path]:
    root = Path(images_dir)
    images: list[Path] = []
    for pattern in ("**/*.jpg", "**/*.jpeg", "**/*.png"):
        images += sorted(root.glob(pattern))
    return images[:num_samples]


def preprocess(path: Path, h: int, w: int) -> np.ndarray | None:
    img = cv2.imread(str(path))
    if img is None:
        return None
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, (w, h))
    img = img.astype(np.float32) / 255.0
    return np.ascontiguousarray(img.transpose(2, 0, 1)[np.newaxis])  # NCHW


def main():
    args = parse_args()

    try:
        import pycuda.autoinit  # noqa: F401
        import pycuda.driver as cuda
        import tensorrt as trt
    except ImportError as e:
        print(f"ERROR: {e}\nRun this script inside the Jetson Docker container.", file=sys.stderr)
        sys.exit(1)

    images = collect_images(args.images_dir, args.num_samples)
    if not images:
        print(f"No images found in {args.images_dir}", file=sys.stderr)
        sys.exit(1)
    print(f"Using {len(images)} calibration images")

    H = W = args.input_size

    class _ImageCalibrator(trt.IInt8EntropyCalibrator2):
        def __init__(self):
            super().__init__()
            self._idx = 0
            self._buf = cuda.mem_alloc(3 * H * W * 4)

        def get_batch_size(self):
            return 1

        def get_batch(self, names):
            while self._idx < len(images):
                arr = preprocess(images[self._idx], H, W)
                self._idx += 1
                if arr is None:
                    continue
                cuda.memcpy_htod(self._buf, arr)
                return [int(self._buf)]
            return None

        def read_calibration_cache(self):
            return None

        def write_calibration_cache(self, cache):
            with open(args.output, "wb") as f:
                f.write(cache)
            print(f"Cache written → {args.output}")

    onnx_data = Path(args.onnx).read_bytes()
    logger = trt.Logger(trt.Logger.INFO)
    explicit_batch = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)

    with (
        trt.Builder(logger) as builder,
        builder.create_network(explicit_batch) as network,
        trt.OnnxParser(network, logger) as parser,
        builder.create_builder_config() as config,
    ):
        config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 4 * 1024 ** 3)
        config.set_flag(trt.BuilderFlag.INT8)
        if builder.platform_has_fast_fp16:
            config.set_flag(trt.BuilderFlag.FP16)

        calibrator = _ImageCalibrator()
        config.int8_calibrator = calibrator

        if not parser.parse(onnx_data):
            for i in range(parser.num_errors):
                print(parser.get_error(i), file=sys.stderr)
            sys.exit(1)

        inp = network.get_input(0)
        shape = inp.shape
        C = shape[1]
        if shape[0] == -1:
            profile = builder.create_optimization_profile()
            profile.set_shape(inp.name, (1, C, H, W), (1, C, H, W), (1, C, H, W))
            config.add_optimization_profile(profile)

        print("Building TensorRT engine with INT8 calibration (several minutes on Jetson)…")
        plan = builder.build_serialized_network(network, config)
        if plan is None:
            print("Engine build failed", file=sys.stderr)
            sys.exit(1)

    print("Done. Upload the cache to the Loader before (re)starting the detections service.")


if __name__ == "__main__":
    main()