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Add YOLOv26n RDK X5 model and quantized artifacts

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# RDK X5 YOLOv26n 量化
输入模型为 `../best.onnx`，固定输入 `images [1,3,640,640]`，opset 11。流程参考
`D-Robotics/rdk_model_zoo` 的 `rdk_x5` 分支中 `ultralytics_yolo26` 示例：
- `extract_yolo26_bpu.py` 移除 TopK、Gather 和最终检测结果组装，只输出三尺度
`(cls, box)` 原始特征，输出布局为 NHWC。
- 校准数据从 `../../dataset` 均匀抽取 50 张图片，直接 resize 到 640x640，保存
RGB/CHW/float32；`data_scale=1/255` 由量化配置完成。
- 板端输入为 NV12，量化默认 int8，并使用官方 Softmax int8 优化项。
执行 `./quantize_x5.sh` 会在 Docker 镜像
`openexplorer/ai_toolchain_ubuntu_20_x5_cpu:v1.2.8` 中依次运行 `hb_mapper checker`
和 `hb_mapper makertbin`，产物为
`mapper_output_bpu/best_bpu_bayese_640x640_nv12.bin`。部署端需要按三尺度输出完成
sigmoid、LTRB 解码、阈值筛选和 NMS；可直接复用 `postprocess_yolo26.py`。
板端输入是 NV12，模型输出顺序为 `cls_s8, box_s8, cls_s16, box_s16,
cls_s32, box_s32`，输出张量布局是 NHWC。`.bin` 的 `model_info` 应以板端
`hrt_model_exec` 实际显示的名称和量化参数为准。

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model_parameters:
onnx_model: "./best_bpu.onnx"
march: "bayes-e"
layer_out_dump: false
working_dir: "mapper_output_bpu"
output_model_file_prefix: "best_bpu_bayese_640x640_nv12"
input_parameters:
input_name: "images"
input_type_rt: "nv12"
input_type_train: "rgb"
input_layout_train: "NCHW"
norm_type: "data_scale"
scale_value: 0.003921568627451
calibration_parameters:
cal_data_dir: "./calibration_data"
cal_data_type: "float32"
calibration_type: "default"
optimization: "set_Softmax_input_int8,set_Softmax_output_int8"
compiler_parameters:
jobs: 8
compile_mode: "latency"
debug: true
optimize_level: "O3"

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images/train/00246cf2-46a7-4a4e-8bc8-8e7f2317ad09.png
images/train/084113da-5f61-45b5-ad3f-ba8554be0c0f_aug1.jpg
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images/train/1876b5d2-8c8f-42f7-adf6-b1947f3dd50c.jpg
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images/train/44926b9a-1aa6-43b1-ac5b-a4930ab630a0.png
images/train/49795500-4f4b-437f-ade7-0a17841bf309_aug2.jpg
images/train/5584b78d-87bd-4326-aa33-0cf9299bb99e_aug1.jpg
images/train/5e5854dc-9716-4579-ab99-5f78383de59d_aug1.jpg
images/train/64330e99-c7f1-4437-a5c0-8d46c8bdb80e.png
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images/train/6dffe65d-b770-4163-a242-8191a87c2f3e_aug2.jpg
images/train/7358fcd2-1eb5-4b14-aa0d-95bb3a70f456_aug1.jpg
images/train/77d8e632-c826-4608-ae0d-40c3dc35dae2.png
images/train/7dd1d683-bbbb-4783-8700-94634f64a1fa_aug1.jpg
images/train/82c41b72-ee4d-4d78-985f-1eb82f50b2f4_aug3.jpg
images/train/87fb24a0-849b-47b9-8ae6-ca4e89abb52c.png
images/train/8cf0b48c-52f0-4b02-88fe-fd26c0063565.png
images/train/921171d2-9791-481a-a3b8-9c93fcb0f479_aug1.jpg
images/train/964a3741-0a41-4a04-82f3-f74cc558a223_aug2.jpg
images/train/9bd59151-88c0-4f4f-bcd8-a6c823248424_aug2.jpg
images/train/a0bc336e-84ed-41ee-8516-4dce806dcd64.png
images/train/a56563df-3e99-45fb-9f29-5f3ec2062da4_aug2.jpg
images/train/aaefdc2e-28cd-4769-96bd-4f7d43d58032.png
images/train/b0473ba5-63d0-4ca1-a1b3-cd3a0d85cbce.png
images/train/b62abb00-d665-4053-8639-97cf3243cf0d_aug1.jpg
images/train/bb6936bc-cc3a-4bb1-9a2c-61ef054a8866_aug4.jpg
images/train/c0c8f3c3-fa59-430a-819f-8da63d470d83.jpg
images/train/c6a9a9fd-242d-420f-91bc-79419f83604a_aug2.jpg
images/train/cc7d55cb-6fb7-4ed8-a524-b022c671454e_aug1.jpg
images/train/d1b89258-c545-4d4a-bd32-60e6f5d7131a_aug1.jpg
images/train/d6601c84-b38a-46df-94c9-3e67ae4c0b7b_aug2.jpg
images/train/dbf7ba4e-24fc-45dd-b655-144bd4ccbe71_aug1.jpg
images/train/e0f3883b-2301-4b95-b975-9fd4a1cfa0be.png
images/train/e605a9e1-5ae1-4da4-8326-9050c21966c6_aug2.jpg
images/train/eb08a24f-7ead-4fae-9f4c-717cd54e4b8f_aug3.jpg
images/train/f040251a-9a78-4536-9439-3803759952e5.png
images/train/f5c6161c-9ea7-432a-9b44-82678c19ee0e_aug2.jpg
images/train/fb30aaaf-3009-4089-98b4-088e020ae1bb_aug2.jpg
images/vel/02f61ef1-3c40-446e-ac2d-fbc7e4ccf605_aug1.jpg
images/vel/0dc056fb-8373-4834-8630-bc0057a1d24c_aug1.jpg
images/vel/1cb927cc-6af4-4c87-a3fa-f5a6f1fc1ebd_aug2.jpg
images/vel/2bdc0ba0-bac7-41d2-80ce-f0301e3e2d5c.png
images/vel/3b347f91-60b4-4d1a-b187-50a9f106f072_aug2.jpg
images/vel/4a674364-26ba-43c8-9448-bcd2bdd68275_aug2.jpg
images/vel/4fc2dc2f-85db-4df0-a4a6-e18f15a856dd.png
images/vel/fb5adb2c-e598-4ae4-bbf1-8e46a10158c3_aug4.jpg

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2026-08-07 10:39:43,366 file: tool_utils.py func: tool_utils line No: 77 log will be stored in /workspace/smart_car_2026/model/hb_mapper_checker.log
2026-08-07 10:39:43,367 file: hb_mapper.py func: hb_mapper line No: 72 Start hb_mapper....
2026-08-07 10:39:43,367 file: hb_mapper.py func: hb_mapper line No: 73 hbdk version 3.49.15
2026-08-07 10:39:43,368 file: hb_mapper.py func: hb_mapper line No: 74 horizon_nn version 1.1.0
2026-08-07 10:39:43,368 file: hb_mapper.py func: hb_mapper line No: 75 hb_mapper version 1.24.3
2026-08-07 10:39:43,386 file: onnx_parser.py func: onnx_parser line No: 39 Model input names: ['images']
2026-08-07 10:39:43,388 file: hb_mapper_checker.py func: hb_mapper_checker line No: 106 Model type: onnx
2026-08-07 10:39:43,388 file: hb_mapper_checker.py func: hb_mapper_checker line No: 107 march: bayes-e
2026-08-07 10:39:43,388 file: hb_mapper_checker.py func: hb_mapper_checker line No: 112 input names ['images']
2026-08-07 10:39:43,389 file: hb_mapper_checker.py func: hb_mapper_checker line No: 113 input shapes {'images': [1, 3, 640, 640]}
2026-08-07 10:39:43,396 file: onnx_parser.py func: onnx_parser line No: 39 Model input names: ['images']
2026-08-07 10:39:43,397 file: hb_mapper_checker.py func: hb_mapper_checker line No: 119 Begin model checking....
2026-08-07 10:39:43,409 file: model_builder.py func: model_builder line No: 35 Start to Horizon NN Model Convert.
2026-08-07 10:39:43,430 file: model_debugger.py func: model_debugger line No: 67 Loading horizon_nn debug methods:set()
2026-08-07 10:39:43,431 file: quantization_config.py func: quantization_config line No: 305 The activation calibration parameters:
calibration_type: fixed
2026-08-07 10:39:43,431 file: model_builder.py func: model_builder line No: 197 The specified model compilation architecture: bayes-e.
2026-08-07 10:39:43,431 file: model_builder.py func: model_builder line No: 207 The specified model compilation optimization parameters: [].
2026-08-07 10:39:43,432 file: model_builder.py func: model_builder line No: 35 Start to prepare the onnx model.
2026-08-07 10:39:43,468 file: prepare.py func: prepare line No: 106 Input ONNX Model Information:
ONNX IR version: 6
Opset version: ['ai.onnx v11', 'horizon v1']
Producer: onnx.utils.extract_model
Domain: None
Version: None
Graph input:
images: shape=[1, 3, 640, 640], dtype=FLOAT32
Graph output:
cls_s8: shape=[1, 80, 80, 4], dtype=FLOAT32
box_s8: shape=[1, 80, 80, 4], dtype=FLOAT32
cls_s16: shape=[1, 40, 40, 4], dtype=FLOAT32
box_s16: shape=[1, 40, 40, 4], dtype=FLOAT32
cls_s32: shape=[1, 20, 20, 4], dtype=FLOAT32
box_s32: shape=[1, 20, 20, 4], dtype=FLOAT32
2026-08-07 10:39:43,655 file: model_builder.py func: model_builder line No: 38 End to prepare the onnx model.
2026-08-07 10:39:43,689 file: model_builder.py func: model_builder line No: 265 Saving model to: ./.hb_check/original_float_model.onnx.
2026-08-07 10:39:43,689 file: model_builder.py func: model_builder line No: 35 Start to optimize the onnx model.
2026-08-07 10:39:43,863 file: constant_folding.py func: constant_folding line No: 66 Summary info for constant_folding:
2026-08-07 10:39:43,863 file: constant_folding.py func: constant_folding line No: 67 After constant_folding, the number of nodes has changed from 355 to 355.
2026-08-07 10:39:43,864 file: constant_folding.py func: constant_folding line No: 71 After constant_folding, the number of parameters has changed from 2375629 to 2375629.
2026-08-07 10:39:43,864 file: constant_folding.py func: constant_folding line No: 76 Detailed info for constant_folding:
2026-08-07 10:39:43,864 file: constant_folding.py func: constant_folding line No: 88
2026-08-07 10:39:44,358 file: model_builder.py func: model_builder line No: 38 End to optimize the onnx model.
2026-08-07 10:39:44,389 file: model_builder.py func: model_builder line No: 265 Saving model to: ./.hb_check/optimized_float_model.onnx.
2026-08-07 10:39:44,389 file: model_builder.py func: model_builder line No: 35 Start to calibrate the model.
2026-08-07 10:39:44,681 file: calibration_data_set.py func: calibration_data_set line No: 111 input name: images, number_of_samples: 1
2026-08-07 10:39:44,681 file: calibration_data_set.py func: calibration_data_set line No: 123 There are 1 samples in the data set.
2026-08-07 10:39:44,682 file: fixed_calibrater.py func: fixed_calibrater line No: 31 Run calibration model with fixed thresholds method.
2026-08-07 10:39:45,210 file: model_builder.py func: model_builder line No: 38 End to calibrate the model.
2026-08-07 10:39:45,332 file: model_builder.py func: model_builder line No: 265 Saving model to: ./.hb_check/calibrated_model.onnx.
2026-08-07 10:39:45,333 file: model_builder.py func: model_builder line No: 35 Start to quantize the model.
2026-08-07 10:39:47,542 file: constant_folding.py func: constant_folding line No: 66 Summary info for constant_folding:
2026-08-07 10:39:47,542 file: constant_folding.py func: constant_folding line No: 67 After constant_folding, the number of nodes has changed from 280 to 280.
2026-08-07 10:39:47,543 file: constant_folding.py func: constant_folding line No: 71 After constant_folding, the number of parameters has changed from 2406337 to 2406337.
2026-08-07 10:39:47,543 file: constant_folding.py func: constant_folding line No: 76 Detailed info for constant_folding:
2026-08-07 10:39:47,543 file: constant_folding.py func: constant_folding line No: 88
2026-08-07 10:39:47,634 file: model_builder.py func: model_builder line No: 38 End to quantize the model.
2026-08-07 10:39:47,729 file: model_builder.py func: model_builder line No: 265 Saving model to: ./.hb_check/quantized_model.onnx.
2026-08-07 10:39:47,729 file: model_builder.py func: model_builder line No: 35 Start to compile the model with march bayes-e.
2026-08-07 10:39:48,338 file: hybrid_build.py func: hybrid_build line No: 111 Compile submodel: Extracted from {main_graph}_subgraph_0
2026-08-07 10:39:48,363 file: hbdk_cc.py func: hbdk_cc line No: 126 hbdk-cc parameters:['--input-layout', 'NHWC', '--output-layout', 'NHWC']
2026-08-07 10:39:48,363 file: hbdk_cc.py func: hbdk_cc line No: 127 hbdk-cc command used:hbdk-cc -f hbir -m /tmp/tmpo3cnuwll/Extracted from {main_graph}_subgraph_0.hbir -o /tmp/tmpo3cnuwll/Extracted from {main_graph}_subgraph_0.hbm --march bayes-e --progressbar --input-layout NHWC --output-layout NHWC
2026-08-07 10:39:49,198 file: tool_utils.py func: tool_utils line No: 326 consumed time 0.65736
2026-08-07 10:39:49,411 file: tool_utils.py func: tool_utils line No: 326 FPS=52.45, latency = 19064.1 us, DDR = 122035200 bytes (see ./.hb_check/Extracted from {main_graph}_subgraph_0.html)
2026-08-07 10:39:49,414 file: hybrid_build.py func: hybrid_build line No: 111 Compile submodel: Extracted from {main_graph}_subgraph_1
2026-08-07 10:39:49,431 file: hbdk_cc.py func: hbdk_cc line No: 126 hbdk-cc parameters:['--input-layout', 'NHWC', '--output-layout', 'NHWC']
2026-08-07 10:39:49,431 file: hbdk_cc.py func: hbdk_cc line No: 127 hbdk-cc command used:hbdk-cc -f hbir -m /tmp/tmpo3cnuwll/Extracted from {main_graph}_subgraph_1.hbir -o /tmp/tmpo3cnuwll/Extracted from {main_graph}_subgraph_1.hbm --march bayes-e --progressbar --input-layout NHWC --output-layout NHWC
2026-08-07 10:39:50,353 file: tool_utils.py func: tool_utils line No: 326 consumed time 0.755009
2026-08-07 10:39:50,544 file: tool_utils.py func: tool_utils line No: 326 FPS=45.81, latency = 21827.8 us, DDR = 138583616 bytes (see ./.hb_check/Extracted from {main_graph}_subgraph_1.html)
2026-08-07 10:39:50,549 file: hybrid_build.py func: hybrid_build line No: 111 Compile submodel: Extracted from {main_graph}_subgraph_2
2026-08-07 10:39:50,556 file: hbdk_cc.py func: hbdk_cc line No: 126 hbdk-cc parameters:['--input-layout', 'NHWC', '--output-layout', 'NHWC']
2026-08-07 10:39:50,557 file: hbdk_cc.py func: hbdk_cc line No: 127 hbdk-cc command used:hbdk-cc -f hbir -m /tmp/tmpo3cnuwll/Extracted from {main_graph}_subgraph_2.hbir -o /tmp/tmpo3cnuwll/Extracted from {main_graph}_subgraph_2.hbm --march bayes-e --progressbar --input-layout NHWC --output-layout NHWC
2026-08-07 10:39:51,107 file: tool_utils.py func: tool_utils line No: 326 consumed time 0.387866
2026-08-07 10:39:51,284 file: tool_utils.py func: tool_utils line No: 326 FPS=142.7, latency = 7007.7 us, DDR = 44277504 bytes (see ./.hb_check/Extracted from {main_graph}_subgraph_2.html)
2026-08-07 10:39:51,550 file: model_builder.py func: model_builder line No: 38 End to compile the model with march bayes-e.
2026-08-07 10:39:51,564 file: print_info_dict.py func: print_info_dict line No: 72 The main quantized node information:
======================================================================================================================================
Node ON Subgraph Type Cosine Similarity Threshold DataType
--------------------------------------------------------------------------------------------------------------------------------------
/model.0/conv/Conv BPU id(0) HzSQuantizedConv -- 1.0 int8
/model.0/act/Mul BPU id(0) HzLut -- 1.0 int8
/model.1/conv/Conv BPU id(0) HzSQuantizedConv -- 1.0 int8
/model.1/act/Mul BPU id(0) HzLut -- 1.0 int8
/model.2/cv1/conv/Conv BPU id(0) HzSQuantizedConv -- 1.0 int8
/model.2/cv1/act/Mul BPU id(0) HzLut -- 1.0 int8
/model.2/Split BPU id(0) Split -- 1.0 int8
/model.2/m.0/cv1/conv/Conv BPU id(0) HzSQuantizedConv -- 1.0 int8
/model.2/m.0/cv1/act/Mul BPU id(0) HzLut -- 1.0 int8
/model.2/m.0/cv2/conv/Conv BPU id(0) HzSQuantizedConv -- 1.0 int8
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2026-08-07 10:39:51,565 file: print_info_dict.py func: print_info_dict line No: 72 The quantized model output:
============
Output
------------
2026-08-07 10:39:51,573 file: model_builder.py func: model_builder line No: 38 End to Horizon NN Model Convert.
2026-08-07 10:39:51,574 file: onnx2horizonrt.py func: onnx2horizonrt line No: 4326 ONNX model output num : 6
2026-08-07 10:39:51,577 file: onnx2horizonrt.py func: onnx2horizonrt line No: 4060 model_deps_info: {'hb_mapper_version': '1.24.3'}
2026-08-07 10:39:51,580 file: hb_mapper_checker.py func: hb_mapper_checker line No: 170 End model checking....

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#!/usr/bin/env python3
"""Extract YOLO26 raw detection heads from a standard Ultralytics ONNX export."""
from __future__ import annotations
import argparse
import re
from pathlib import Path
import onnx
from onnx import TensorProto, helper, shape_inference
TERMINAL_HEAD = re.compile(
r"one2one_cv([23])\.(\d+)/one2one_cv\1\.\2\.2/Conv$"
)
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--input", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
args = parser.parse_args()
model = shape_inference.infer_shapes(onnx.load(args.input))
shapes = {
value.name: [dim.dim_value for dim in value.type.tensor_type.shape.dim]
for value in list(model.graph.value_info) + list(model.graph.output)
}
heads: dict[tuple[int, int], str] = {}
for node in model.graph.node:
match = TERMINAL_HEAD.search(node.name)
if match and node.op_type == "Conv":
branch, scale = int(match.group(1)), int(match.group(2))
heads[(branch, scale)] = node.output[0]
expected = {(branch, scale) for branch in (2, 3) for scale in range(3)}
if set(heads) != expected:
missing = sorted(expected - set(heads))
raise SystemExit(f"could not locate all YOLO26 one-to-one heads; missing {missing}")
del model.graph.output[:]
output_names = []
for scale, stride in enumerate((8, 16, 32)):
for branch, kind in ((3, "cls"), (2, "box")):
source = heads[(branch, scale)]
source_shape = shapes[source]
if len(source_shape) != 4:
raise SystemExit(f"unexpected head shape for {source}: {source_shape}")
output_name = f"{kind}_s{stride}"
model.graph.node.append(
helper.make_node(
"Transpose",
[source],
[output_name],
name=f"bpu_output_{kind}_s{stride}",
perm=[0, 2, 3, 1],
)
)
model.graph.output.append(
helper.make_tensor_value_info(
output_name,
TensorProto.FLOAT,
[source_shape[0], source_shape[2], source_shape[3], source_shape[1]],
)
)
output_names.append(output_name)
args.output.parent.mkdir(parents=True, exist_ok=True)
temporary = args.output.with_suffix(".unpruned.onnx")
onnx.save(model, temporary)
onnx.utils.extract_model(str(temporary), str(args.output), [model.graph.input[0].name], output_names)
temporary.unlink()
extracted = onnx.load(args.output)
onnx.checker.check_model(extracted)
print(f"wrote {args.output} with outputs:")
for output in extracted.graph.output:
dims = [dim.dim_value for dim in output.type.tensor_type.shape.dim]
print(f" {output.name}: {dims}")
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
"""CPU post-processing for the six raw outputs of best_bpu.onnx."""
from __future__ import annotations
import numpy as np
def _nms(boxes: np.ndarray, scores: np.ndarray, iou_threshold: float) -> np.ndarray:
order = scores.argsort()[::-1]
kept: list[int] = []
while order.size:
current = int(order[0])
kept.append(current)
if order.size == 1:
break
rest = order[1:]
xx1 = np.maximum(boxes[current, 0], boxes[rest, 0])
yy1 = np.maximum(boxes[current, 1], boxes[rest, 1])
xx2 = np.minimum(boxes[current, 2], boxes[rest, 2])
yy2 = np.minimum(boxes[current, 3], boxes[rest, 3])
inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
union = area[current] + area[rest] - inter
order = rest[(inter / np.maximum(union, 1e-9)) <= iou_threshold]
return np.asarray(kept, dtype=np.int64)
def postprocess(
outputs: list[np.ndarray] | tuple[np.ndarray, ...],
classes: int = 4,
score_threshold: float = 0.25,
iou_threshold: float = 0.7,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Decode `(cls_s8, box_s8, cls_s16, box_s16, cls_s32, box_s32)` outputs.
Returns `(boxes_xyxy, scores, class_ids)` in the 640x640 model coordinate
system. Scale boxes back to the camera image after this function when using
letterbox preprocessing.
"""
if len(outputs) != 6:
raise ValueError(f"expected six outputs, got {len(outputs)}")
conf_raw = -np.log(1.0 / score_threshold - 1.0)
detections: list[np.ndarray] = []
for index, stride in enumerate((8, 16, 32)):
cls = np.asarray(outputs[index * 2]).reshape(-1, classes).astype(np.float32)
box = np.asarray(outputs[index * 2 + 1]).reshape(-1, 4).astype(np.float32)
max_logits = cls.max(axis=1)
valid = np.flatnonzero(max_logits >= conf_raw)
if valid.size == 0:
continue
height = width = 640 // stride
grid_y, grid_x = np.indices((height, width))
grid = np.stack((grid_x, grid_y), axis=-1).reshape(-1, 2).astype(np.float32) + 0.5
anchor = grid[valid]
distances = box[valid]
xyxy = np.column_stack(
((anchor[:, 0] - distances[:, 0]) * stride,
(anchor[:, 1] - distances[:, 1]) * stride,
(anchor[:, 0] + distances[:, 2]) * stride,
(anchor[:, 1] + distances[:, 3]) * stride)
)
scores = 1.0 / (1.0 + np.exp(-max_logits[valid]))
ids = cls[valid].argmax(axis=1).astype(np.float32)
detections.append(np.column_stack((xyxy, scores, ids)))
if not detections:
return np.empty((0, 4), np.float32), np.empty((0,), np.float32), np.empty((0,), np.int32)
dets = np.concatenate(detections, axis=0)
kept: list[np.ndarray] = []
for class_id in np.unique(dets[:, 5]).astype(np.int32):
class_dets = dets[dets[:, 5] == class_id]
indices = _nms(class_dets[:, :4], class_dets[:, 4], iou_threshold)
kept.append(class_dets[indices])
if not kept:
return np.empty((0, 4), np.float32), np.empty((0,), np.float32), np.empty((0,), np.int32)
result = np.concatenate(kept, axis=0)
return result[:, :4], result[:, 4], result[:, 5].astype(np.int32)

58
x5_quantization/prepare_calibration.py Normal file
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#!/usr/bin/env python3
"""Create Horizon hb_mapper calibration feature maps from the YOLO dataset."""
from __future__ import annotations
import argparse
from pathlib import Path
import numpy as np
from PIL import Image
IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp"}
def resize_rgb(image: Image.Image, size: int) -> np.ndarray:
image = image.convert("RGB")
resized = image.resize((size, size), Image.Resampling.BILINEAR)
array = np.asarray(resized, dtype=np.float32)
return np.transpose(array, (2, 0, 1))[None, ...]
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--samples", type=int, default=128)
parser.add_argument("--size", type=int, default=640)
args = parser.parse_args()
roots = [args.dataset / "images" / "train", args.dataset / "images" / "vel"]
images = sorted(
p for root in roots if root.is_dir() for p in root.iterdir() if p.suffix.lower() in IMAGE_EXTS
)
if not images:
raise SystemExit(f"no images found below {args.dataset}")
count = min(args.samples, len(images))
# Evenly spread samples over the sorted set so calibration is not dominated by one split.
indices = np.linspace(0, len(images) - 1, count, dtype=np.int64)
selected = [images[int(i)] for i in indices]
args.output.mkdir(parents=True, exist_ok=True)
for old in args.output.iterdir():
if old.is_file() and old.suffix in {".bin", ".rgbchw", ".txt"}:
old.unlink()
manifest = []
for index, path in enumerate(selected):
data = resize_rgb(Image.open(path), args.size)
data.tofile(args.output / f"{index:05d}.rgbchw")
manifest.append(str(path.relative_to(args.dataset)))
(args.output.parent / "calibration_manifest.txt").write_text(
"\n".join(manifest) + "\n", encoding="utf-8"
)
print(f"wrote {count} samples ({args.size}x{args.size}) to {args.output}")
if __name__ == "__main__":
main()

58
x5_quantization/quantize_x5.sh Executable file
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#!/usr/bin/env bash
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
MODEL_DIR="$(cd "${SCRIPT_DIR}/.." && pwd)"
REPO_DIR="$(cd "${MODEL_DIR}/.." && pwd)"
IMAGE="openexplorer/ai_toolchain_ubuntu_20_x5_cpu:v1.2.8"
docker run --rm --platform linux/amd64 \
-v "${REPO_DIR}:/workspace/smart_car_2026" \
"${IMAGE}" bash -lc '
set -euo pipefail
cd /workspace/smart_car_2026/model/x5_quantization
python3 extract_yolo26_bpu.py \
--input ../best.onnx \
--output best_bpu.onnx
python3 prepare_calibration.py \
--dataset /workspace/smart_car_2026/dataset \
--output calibration_data \
--samples 50
cat > best_bpu_int8.yaml <<"YAML"
model_parameters:
onnx_model: "./best_bpu.onnx"
march: "bayes-e"
layer_out_dump: false
working_dir: "mapper_output_bpu"
output_model_file_prefix: "best_bpu_bayese_640x640_nv12"
input_parameters:
input_name: "images"
input_type_rt: "nv12"
input_type_train: "rgb"
input_layout_train: "NCHW"
norm_type: "data_scale"
scale_value: 0.003921568627451
calibration_parameters:
cal_data_dir: "./calibration_data"
cal_data_type: "float32"
calibration_type: "default"
optimization: "set_Softmax_input_int8,set_Softmax_output_int8"
compiler_parameters:
jobs: 8
compile_mode: "latency"
debug: true
optimize_level: "O3"
YAML
hb_mapper checker \
--model best_bpu.onnx \
--model-type onnx \
--march bayes-e \
--input-shape images 1x3x640x640
hb_mapper makertbin \
--config best_bpu_int8.yaml \
--model-type onnx
ls -lh mapper_output_bpu/best_bpu_bayese_640x640_nv12.bin
'