“星瑞” O6 评测 ——NPU 部署 face parser 模型
前言
瑞莎星睿 O6 (Radxa Orion O6) 拥有高达 28.8TOPs NPU (Neural Processing Unit) 算力，支持 INT4 / INT8 / INT16 / FP16 / BF16 和 TF32 类型的加速。这里通过通过官方的工具链进行FaceParsingBiSeNet的部署
1. FaceParsingBiSeNet onnx 推理
- 首先从百度网盘 提取码 8gin，下载开源的模型：face_parsing_512x512.onnx
- 编写 onnx 的推理脚本，如下
import os
import cv2
import argparse
import numpy as np
from PIL import Image
import onnxruntime
import time
def letterbox(image, new_shape=(640, 640), color=(114, 114, 114), auto=False, scaleFill=False, scaleup=True):
"""
对图像进行letterbox操作，保持宽高比缩放并填充到指定尺寸
:param image: 输入的图像，格式为numpy数组 (height, width, channels)
:param new_shape: 目标尺寸，格式为 (height, width)
:param color: 填充颜色，默认为 (114, 114, 114)
:param auto: 是否自动计算最小矩形，默认为True
:param scaleFill: 是否不保持宽高比直接缩放，默认为False
:param scaleup: 是否只放大不缩小，默认为True
:return: 处理后的图像，缩放比例，填充大小
"""
shape = image.shape[:2] # 当前图像的高度和宽度
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # 只缩小不放大（为了更好的效果）
r = min(r, 1.0)
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # 计算填充尺寸
if auto: # 最小矩形
dw, dh = np.mod(dw, 64), np.mod(dh, 64) # 强制为 64 的倍数
dw /= 2 # 从两侧填充
dh /= 2
if shape[::-1] != new_unpad: # 缩放图像
image = cv2.resize(image, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # 添加填充
scale_ratio = r
pad_size = (dw, dh)
return image, scale_ratio, pad_size
def preprocess_image(image, shape, bgr2rgb=True):
"""图片预处理"""
img, scale_ratio, pad_size = letterbox(image, new_shape=shape)
if bgr2rgb:
img = img[:, :, ::-1]
img = img.transpose(2, 0, 1) # HWC2CHW
img = np.ascontiguousarray(img, dtype=np.float32)
return img, scale_ratio, pad_size
def generate_mask(img, seg, outpath, scale=0.4):
'分割结果可视化'
color = [
[255, 0, 0],
[255, 85, 0],
[255, 170, 0],
[255, 0, 85],
[255, 0, 170],
[0, 255, 0],
[85, 255, 0],
[170, 255, 0],
[0, 255, 85],
[0, 255, 170],
[0, 0, 255],
[85, 0, 255],
[170, 0, 255],
[0, 85, 255],
[0, 170, 255],
[255, 255, 0],
[255, 255, 85],
[255, 255, 170],
[255, 0, 255],
[255, 85, 255]
]
img = img.transpose(1, 2, 0) # HWC2CHW
minidx = int(seg.min())
maxidx = int(seg.max())
color_img = np.zeros_like(img)
for i in range(minidx, maxidx):
if i <= 0:
continue
color_img[seg == i] = color[i]
showimg = scale * img + (1 - scale) * color_img
Image.fromarray(showimg.astype(np.uint8)).save(outpath)
if __name__ == '__main__':
# define cmd arguments
parser = argparse.ArgumentParser()
parser.add_argument('--image-path', type=str, help='path of the input image (a file)')
parser.add_argument('--output-path', type=str, help='paht for saving the predicted alpha matte (a file)')
parser.add_argument('--model-path', type=str, help='path of the ONNX model')
args = parser.parse_args()
# check input arguments
if not os.path.exists(args.image_path):
print('Cannot find the input image: {0}'.format(args.image_path))
exit()
if not os.path.exists(args.model_path):
print('Cannot find the ONXX model: {0}'.format(args.model_path))
exit()
ref_size = [512, 512]
# read image
im = cv2.imread(args.image_path)
img, scale_ratio, pad_size = preprocess_image(im, ref_size)
showimg = img.copy()[::-1, ...]
mean = np.asarray([0.485, 0.456, 0.406])
scale = np.asarray([0.229, 0.224, 0.225])
mean = mean.reshape((3, 1, 1))
scale = scale.reshape((3, 1, 1))
img = (img / 255 - mean) * scale
im = img[None].astype(np.float32)
np.save("models/ComputeVision/Semantic_Segmentation/onnx_faceparse/datasets/calibration_data.npy", im)
# Initialize session and get prediction
session = onnxruntime.InferenceSession(args.model_path, None)
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
output = session.run([output_name], {input_name: im})
start_time = time.perf_counter()
for _ in range(5):
output = session.run([output_name], {input_name: im})
end_time = time.perf_counter()
use_time = (end_time - start_time) * 1000
fps = 1000 / use_time
print(f"推理耗时:{use_time:.2f} ms, fps:{fps:.2f}")
# refine matte
seg = np.argmax(output[0], axis=1).squeeze()
generate_mask(showimg, seg, args.output_path)- 推理
python models/ComputeVision/Semantic_Segmentation/onnx_faceparse/inference_onnx.py --image-path models/ComputeVision/Semantic_Segmentation/onnx_faceparse/test_data/test_lite_face_parsing.png --output-path output/face_parsering.jpg --model-path asserts/models/bisenet/face_parsing_512x512.onnx打印输出
推理耗时:1544.36 ms, fps:0.65可视化效果
- 代码解释
- np.save("models/ComputeVision/Semantic_Segmentation/onnx_faceparse/datasets/calibration_data.npy", im)
这里是将输入保存供 NPU PTQ 量化使用.
2. FaceParsingBiSeNet NPU 推理
- 创建 cfg 配置文件，具体如下。
[Common]
mode = build
[Parser]
model_type = onnx
model_name = face_parsing_512x512
detection_postprocess =
model_domain = image_segmentation
input_model = /home/5_radxa/ai_model_hub/asserts/models/bisenet/face_parsing_512x512.onnx
input = input
input_shape = [1, 3, 512, 512]
output = out
output_dir = ./
[Optimizer]
output_dir = ./
calibration_data = ./datasets/calibration_data.npy
calibration_batch_size = 1
metric_batch_size = 1
dataset = NumpyDataset
quantize_method_for_weight = per_channel_symmetric_restricted_range
quantize_method_for_activation = per_tensor_asymmetric
save_statistic_info = True
[GBuilder]
outputs = bisenet.cix
target = X2_1204MP3
profile = True
tiling = fps注意： [Parser]中的 input，output 是输入，输出 tensor 的名字，可以通过 netron 打开 onnx 模型看。输入，输出名字不匹配时，会有报错：
[I] Build with version 6.1.3119
[I] Parsing model....
[I] [Parser]: Begin to parse onnx model face_parsing_512x512...
2025-04-18 11:13:53.104146: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /root/miniconda3/envs/radxa/lib/python3.8/site-packages/cv2/../../lib64:/root/miniconda3/envs/radxa/lib/python3.8/site-packages/AIPUBuilder/simulator-lib/:/root/miniconda3/envs/radxa/lib/python3.8/site-packages/AIPUBuilder/simulator-lib
2025-04-18 11:13:53.104217: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2025-04-18 11:13:54.266791: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /root/miniconda3/envs/radxa/lib/python3.8/site-packages/cv2/../../lib64:/root/miniconda3/envs/radxa/lib/python3.8/site-packages/AIPUBuilder/simulator-lib/:/root/miniconda3/envs/radxa/lib/python3.8/site-packages/AIPUBuilder/simulator-lib
2025-04-18 11:13:54.266893: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
2025-04-18 11:13:54.266959: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (chenjun): /proc/driver/nvidia/version does not exist
[E] [Parser]: Graph does not contain such a node/tensor name:output
[E] [Parser]: Parser Failed!- 编译模型
在 x86 主机上编译模型
cd models/ComputeVision/Semantic_Segmentation/onnx_faceparse
cixbuild ./cfg/onnx_bisenet.cfg报错: ImportError: libaipu_simulator_x2.so: cannot open shared object file: No such file or directory
解决方案
- 通过
find / -name libaipu_simulator_x2.so - 通过
export LD_LIBRARY_PATH=/root/miniconda3/envs/radxa/lib/python3.8/site-packages/AIPUBuilder/simulator-lib/libaipu_simulator_x2.so:$LD_LIBRARY_PATH添加环境变量
编译成功的打印信息
[I] Build with version 6.1.3119
[I] Parsing model....
[I] [Parser]: Begin to parse onnx model face_parsing_512x512...
2025-04-18 11:20:16.726111: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /root/miniconda3/envs/radxa/lib/python3.8/site-packages/cv2/../../lib64:/root/miniconda3/envs/radxa/lib/python3.8/site-packages/AIPUBuilder/simulator-lib/:/root/miniconda3/envs/radxa/lib/python3.8/site-packages/AIPUBuilder/simulator-lib
2025-04-18 11:20:16.726199: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2025-04-18 11:20:17.908202: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /root/miniconda3/envs/radxa/lib/python3.8/site-packages/cv2/../../lib64:/root/miniconda3/envs/radxa/lib/python3.8/site-packages/AIPUBuilder/simulator-lib/:/root/miniconda3/envs/radxa/lib/python3.8/site-packages/AIPUBuilder/simulator-lib
2025-04-18 11:20:17.908267: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
2025-04-18 11:20:17.908283: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (chenjun): /proc/driver/nvidia/version does not exist
[W] [Parser]: The output name out is not a node but a tensor. However, we will use the node Resize_161 as output node.
2025-04-18 11:20:21.229063: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
[I] [Parser]: The input tensor(s) is/are: input_0
[I] [Parser]: Input input from cfg is shown as tensor input_0 in IR!
[I] [Parser]: Output out from cfg is shown as tensor Resize_161_post_transpose_0 in IR!
[I] [Parser]: 0 error(s), 1 warning(s) generated.
[I] [Parser]: Parser done!
[I] Parse model complete
[I] Simplifying float model.
[I] [IRChecker] Start to check IR: /home/5_radxa/ai_model_hub/models/ComputeVision/Semantic_Segmentation/onnx_faceparse/internal/face_parsing_512x512.txt
[I] [IRChecker] model_name: face_parsing_512x512
[I] [IRChecker] IRChecker: All IR pass (Checker Plugin disabled)
[I] [graph.cpp :1600] loading graph weight: /home/5_radxa/ai_model_hub/models/ComputeVision/Semantic_Segmentation/onnx_faceparse/./internal/face_parsing_512x512.bin size: 0x322454c
[I] Start to simplify the graph...
[I] Using fixed-point full optimization, it may take long long time ....
[I] GSim simplified result:
------------------------------------------------------------------------
OpType.Eltwise: -3
OpType.Mul: +3
OpType.Tile: -3
------------------------------------------------------------------------
略
略
略
略
略
[I] [builder.cpp:1939] Read and Write:80.21MB
[I] [builder.cpp:1080] Reduce constants memory size: 3.477MB
[I] [builder.cpp:2411] memory statistics for this graph (face_parsing_512x512)
[I] [builder.cpp: 585] Total memory : 0x00d52b98 Bytes ( 13.323MB)
[I] [builder.cpp: 585] Text section: 0x00042200 Bytes ( 0.258MB)
[I] [builder.cpp: 585] RO section: 0x00006d00 Bytes ( 0.027MB)
[I] [builder.cpp: 585] Desc section: 0x0002ea00 Bytes ( 0.182MB)
[I] [builder.cpp: 585] Data section: 0x00c8b360 Bytes ( 12.544MB)
[I] [builder.cpp: 585] BSS section: 0x0000fb38 Bytes ( 0.061MB)
[I] [builder.cpp: 585] Stack : 0x00040400 Bytes ( 0.251MB)
[I] [builder.cpp: 585] Workspace(BSS) : 0x004c0000 Bytes ( 4.750MB)
[I] [builder.cpp:2427]
[I] [tools.cpp :1181] - compile time: 20.726 s
[I] [tools.cpp :1087] With GM optimization, DDR Footprint stastic(estimation):
[I] [tools.cpp :1094] Read and Write:92.67MB
[I] [tools.cpp :1137] - draw graph time: 0.03 s
[I] [tools.cpp :1954] remove global cwd: /tmp/af3c1da8ea81cc1cf85dba1587ff72126ee96222bb098b52633050918b4c7
build success.......
Total errors: 0, warnings: 15- NPU 推理可视化
编写 npu 推理脚本, 可视化推理结果，统计推理耗时
import numpy as np
import cv2
import argparse
import os
import sys
import time
# Define the absolute path to the utils package by going up four directory levels from the current file location
_abs_path = "/home/radxa/1_AI_models/ai_model_hub"
# Append the utils package path to the system path, making it accessible for imports
sys.path.append(_abs_path)
from utils.tools import get_file_list
from utils.NOE_Engine import EngineInfer
import os
import cv2
import argparse
import numpy as np
from PIL import Image
def letterbox(image, new_shape=(640, 640), color=(114, 114, 114), auto=False, scaleFill=False, scaleup=True):
"""
对图像进行letterbox操作，保持宽高比缩放并填充到指定尺寸
:param image: 输入的图像，格式为numpy数组 (height, width, channels)
:param new_shape: 目标尺寸，格式为 (height, width)
:param color: 填充颜色，默认为 (114, 114, 114)
:param auto: 是否自动计算最小矩形，默认为True
:param scaleFill: 是否不保持宽高比直接缩放，默认为False
:param scaleup: 是否只放大不缩小，默认为True
:return: 处理后的图像，缩放比例，填充大小
"""
shape = image.shape[:2] # 当前图像的高度和宽度
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # 只缩小不放大（为了更好的效果）
r = min(r, 1.0)
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # 计算填充尺寸
if auto: # 最小矩形
dw, dh = np.mod(dw, 64), np.mod(dh, 64) # 强制为 64 的倍数
dw /= 2 # 从两侧填充
dh /= 2
if shape[::-1] != new_unpad: # 缩放图像
image = cv2.resize(image, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # 添加填充
scale_ratio = r
pad_size = (dw, dh)
return image, scale_ratio, pad_size
def preprocess_image(image, shape, bgr2rgb=True):
"""图片预处理"""
img, scale_ratio, pad_size = letterbox(image, new_shape=shape)
if bgr2rgb:
img = img[:, :, ::-1]
img = img.transpose(2, 0, 1) # HWC2CHW
img = np.ascontiguousarray(img, dtype=np.float32)
return img, scale_ratio, pad_size
def generate_mask(img, seg, outpath, scale=0.4):
'分割结果可视化'
color = [
[255, 0, 0],
[255, 85, 0],
[255, 170, 0],
[255, 0, 85],
[255, 0, 170],
[0, 255, 0],
[85, 255, 0],
[170, 255, 0],
[0, 255, 85],
[0, 255, 170],
[0, 0, 255],
[85, 0, 255],
[170, 0, 255],
[0, 85, 255],
[0, 170, 255],
[255, 255, 0],
[255, 255, 85],
[255, 255, 170],
[255, 0, 255],
[255, 85, 255]
]
img = img.transpose(1, 2, 0) # HWC2CHW
minidx = int(seg.min())
maxidx = int(seg.max())
color_img = np.zeros_like(img)
for i in range(minidx, maxidx):
if i <= 0:
continue
color_img[seg == i] = color[i]
showimg = scale * img + (1 - scale) * color_img
Image.fromarray(showimg.astype(np.uint8)).save(outpath)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--image-path', type=str, help='path of the input image (a file)')
parser.add_argument('--output-path', type=str, help='paht for saving the predicted alpha matte (a file)')
parser.add_argument('--model-path', type=str, help='path of the ONNX model')
args = parser.parse_args()
model = EngineInfer(args.model_path)
ref_size = [512, 512]
# read image
im = cv2.imread(args.image_path)
img, scale_ratio, pad_size = preprocess_image(im, ref_size)
showimg = img.copy()[::-1, ...]
mean = np.asarray([0.485, 0.456, 0.406])
scale = np.asarray([0.229, 0.224, 0.225])
mean = mean.reshape((3, 1, 1))
scale = scale.reshape((3, 1, 1))
img = (img / 255 - mean) * scale
im = img[None].astype(np.float32)
## infer
input_data = [im]
# output = model.forward(input_data)[0]
N = 5
start_time = time.perf_counter()
for _ in range(N):
output = model.forward(input_data)[0]
end_time = time.perf_counter()
use_time = (end_time - start_time) * 1000 / N
fps = N / (end_time - start_time)
print(f"包含输入量化，输出反量化，推理耗时:{use_time:.2f} ms, fps:{fps:.2f}")
fps = model.get_ave_fps()
use_time2 = 1000 / fps
print(f"NPU 计算部分耗时：{use_time2:.2f} ms， fps: {fps:.2f}")
# refine matte
output = np.reshape(output, (1, 19, 512, 512))
seg = np.argmax(output, axis=1).squeeze()
generate_mask(showimg, seg, args.output_path)
# release model
model.clean()
推理
source /home/radxa/1_AI_models/ai_model_hub/.venv/bin/activate
python models/ComputeVision/Semantic_Segmentation/onnx_faceparse/inference_npu.py --image-path models/ComputeVision/Semantic_Segmentation/onnx_faceparse/test_data/test_lite_face_parsing.png --output-path output/face_parsering.jpg --model-path models/ComputeVision/Semantic_Segmentation/onnx_faceparse/bisenet.cix推理耗时
npu: noe_init_context success
npu: noe_load_graph success
Input tensor count is 1.
Output tensor count is 1.
npu: noe_create_job success
包含输入量化，输出反量化，推理耗时:379.63 ms, fps:2.63
NPU 计算部分耗时：10.70 ms， fps: 93.43
npu: noe_clean_job success
npu: noe_unload_graph success
npu: noe_deinit_context success可以看到这里输入量化，输出反量化还是很耗时
可视化效果ok.
3. benchmark
| 序号 | 硬件 | 模型 | 输入分辨率 | 量化类型 | 执行 engine | 推理耗时/ms | fps |
|---|---|---|---|---|---|---|---|
| cpu | bisenet | 512x512 | fp32 | onnxruntime | 309.75 | 3.23 | |
| npu | bisenet | 512x512 | A8W8 | 周易 NPU | 10.70 | 93.43 |
