加载推理om模型很有问题，测试acl也是，想问一下我应该使用什么才能在华为服务器的npu卡下面进行加速推理模型，有没有合适的案例框架可以尝试，谢谢
以下是我的设备信息
-
驱动版本：24.1.rc3
-
Ascend HAL 版本：7.35.23
-
mindxsdk-nxvision_7.1
-
Linux 300I-Duo-12-47 5.4.0-216-generic aarch64 GNU/Linux 310P3
测试以下代码的时候
import numpy as np # 用于对多维数组进行计算
import cv2 # 图片处理三方库，用于对图片进行前后处理
from mindx.sdk import Tensor # mxVision 中的 Tensor 数据结构
from mindx.sdk import base # mxVision 推理接口
from mindx.sdk.base import post # post.Resnet50PostProcess 为 resnet50 后处理接口
from mindx.sdk.base import log # mxVision 日志接口
def process():
'''初始化资源和变量'''
base.mx_init() # 初始化 mxVision 资源
pic_path = 'data/test.jpg' # 单张图片
model_path = "model/resnet50.om" # 模型路径
device_id = 0 # 指定运算的Device
config_path='utils/resnet50.cfg' # 后处理配置文件
label_path='utils/resnet50_clsidx_to_labels.names' # 类别标签文件
img_size = 256
'''前处理'''
img_bgr = cv2.imread(pic_path)
img_rgb = img_bgr[:,:,::-1]
img = cv2.resize(img_rgb, (img_size, img_size)) # 缩放到目标大小
hw_off = (img_size - 224) // 2 # 对图片进行切分，取中间区域
crop_img = img[hw_off:img_size - hw_off, hw_off:img_size - hw_off, :]
img = crop_img.astype("float32") # 转为 float32 数据类型
img[:, :, 0] -= 104 # 常数 104,117,123 用于将图像转换到Caffe模型需要的颜色空间
img[:, :, 1] -= 117
img[:, :, 2] -= 123
img = np.expand_dims(img, axis=0) # 扩展第一维度，适应模型输入
img = img.transpose([0, 3, 1, 2]) # 将 (batch,height,width,channels) 转为 (batch,channels,height,width)
img = np.ascontiguousarray(img) # 将内存连续排列
img = Tensor(img) # 将numpy转为转为Tensor类
'''模型推理'''
model = base.model(modelPath=model_path, deviceId=device_id) # 初始化 base.model 类
output = model.infer([img])[0] # 执行推理。输入数据类型：List[base.Tensor]， 返回模型推理输出的 List[base.Tensor]
output.to_host()
'''后处理'''
postprocessor = post.Resnet50PostProcess(config_path=config_path, label_path=label_path) # 获取后处理对象
pred = postprocessor.process([output])[0][0] # 利用sdk接口进行后处理，pred：<ClassInfo classId=... confidence=... className=...>
confidence = pred.confidence # 获取类别置信度
className = pred.className # 获取类别名称
print('{}: {}'.format(className, confidence)) # 打印出结果
'''保存推理图片'''
img_res = cv2.putText(img_bgr, f'{className}: {confidence:.2f}', (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1) # 将预测的类别与置信度添加到图片
cv2.imwrite('result.png', img_res)
print('save infer result success')
if __name__ == "__main__":
base.mx_init() # 初始化 mxVision 资源
process()
base.mx_deinit() # 去初始化 mxVision 资源
最后爆出的错误信息
Begin to initialize Log.
WARNING: Logging before InitGoogleLogging() is written to STDERR
I20251114 17:02:10.530218 281472986571200 FileUtils.cpp:510] Check Owner permission: Current permission is 7, but required no greater than 6.
The output directory of logs file exist.
I20251114 17:02:10.530591 281472986571200 FileUtils.cpp:354] The input file is empty
I20251114 17:02:10.530617 281472986571200 FileUtils.cpp:510] Check Other group permission: Current permission is 4, but required no greater than 0.
Save logs information to specified directory.
E20251114 17:02:10.544812 281472986571200 FileUtils.cpp:506] Check Owner permission failed: Current permission is 7, but required no greater than 6. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.545192 281472986571200 LogManager.hpp:139] Invalid logging.conf file! (Code = 1004, Message = "Invalid parameter")
E20251114 17:02:10.545231 281472986571200 GlobalInit.cpp:67] Init log rotate thread failed. (Code = 1004, Message = "Invalid parameter")
E20251114 17:02:10.545324 281472986571200 FileUtils.cpp:506] Check Owner permission failed: Current permission is 7, but required no greater than 6. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.545360 281472986571200 LogManager.hpp:139] Invalid logging.conf file! (Code = 1004, Message = "Invalid parameter")
E20251114 17:02:10.545391 281472986571200 GlobalInit.cpp:67] Init log rotate thread failed. (Code = 1004, Message = "Invalid parameter")
E20251114 17:02:10.582126 281472986571200 FileUtils.cpp:506] Check Owner permission failed: Current permission is 7, but required no greater than 6. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.582208 281472986571200 DeviceManager.cpp:36] Invalid lib file! (Code = 1001, Message = "General Failed")
E20251114 17:02:10.582244 281472986571200 DeviceManager.cpp:166] Check user permission failed, please check the owner of process. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.582275 281472986571200 DeviceManager.cpp:290] Init device failed. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.582305 281472986571200 MxOmModelDesc.cpp:78] Model setDevice failed, please check the state of device. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.582337 281472986571200 Model.cpp:58] MxModel init failed. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.610692 281472986571200 MxOmModelDesc.cpp:368] Failed to unload model. (Calling Function = aclmdlUnload, Code = 145003, Message = "ACL error, please refer to the document of CANN.")
E20251114 17:02:10.610784 281472986571200 PyModel.cpp:41] Failed to create model_ object. (Code = 1007, Message = "memory allocation error")
Traceback (most recent call last):
File "/home/hkzy/workspace/test_model/resnet50_sdk_python_sample/main.py", line 54, in <module>
process()
File "/home/hkzy/workspace/test_model/resnet50_sdk_python_sample/main.py", line 36, in process
model = base.model(modelPath=model_path, deviceId=device_id) # 初始化 base.model 类
File "/home/hkzy/.local/lib/python3.10/site-packages/mindx/sdk/base/base.py", line 2215, in model
return _base.model(modelPath, deviceId)
RuntimeError: (Code = 1007, Message = "memory allocation error")
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加载推理om模型很有问题，测试acl也是，想问一下我应该使用什么才能在华为服务器的npu卡下面进行加速推理模型，有没有合适的案例框架可以尝试，谢谢
以下是我的设备信息
驱动版本：24.1.rc3
Ascend HAL 版本：7.35.23
mindxsdk-nxvision_7.1
Linux 300I-Duo-12-47 5.4.0-216-generic aarch64 GNU/Linux 310P3
测试以下代码的时候
import numpy as np # 用于对多维数组进行计算
import cv2 # 图片处理三方库，用于对图片进行前后处理
from mindx.sdk import Tensor # mxVision 中的 Tensor 数据结构
from mindx.sdk import base # mxVision 推理接口
from mindx.sdk.base import post # post.Resnet50PostProcess 为 resnet50 后处理接口
from mindx.sdk.base import log # mxVision 日志接口
def process():
'''初始化资源和变量'''
base.mx_init() # 初始化 mxVision 资源
pic_path = 'data/test.jpg' # 单张图片
model_path = "model/resnet50.om" # 模型路径
device_id = 0 # 指定运算的Device
config_path='utils/resnet50.cfg' # 后处理配置文件
label_path='utils/resnet50_clsidx_to_labels.names' # 类别标签文件
img_size = 256
'''前处理'''
img_bgr = cv2.imread(pic_path)
img_rgb = img_bgr[:,:,::-1]
img = cv2.resize(img_rgb, (img_size, img_size)) # 缩放到目标大小
hw_off = (img_size - 224) // 2 # 对图片进行切分，取中间区域
crop_img = img[hw_off:img_size - hw_off, hw_off:img_size - hw_off, :]
img = crop_img.astype("float32") # 转为 float32 数据类型
img[:, :, 0] -= 104 # 常数 104,117,123 用于将图像转换到Caffe模型需要的颜色空间
img[:, :, 1] -= 117
img[:, :, 2] -= 123
img = np.expand_dims(img, axis=0) # 扩展第一维度，适应模型输入
img = img.transpose([0, 3, 1, 2]) # 将 (batch,height,width,channels) 转为 (batch,channels,height,width)
img = np.ascontiguousarray(img) # 将内存连续排列
img = Tensor(img) # 将numpy转为转为Tensor类
'''模型推理'''
model = base.model(modelPath=model_path, deviceId=device_id) # 初始化 base.model 类
output = model.infer([img])[0] # 执行推理。输入数据类型：List[base.Tensor]， 返回模型推理输出的 List[base.Tensor]
output.to_host()
'''后处理'''
postprocessor = post.Resnet50PostProcess(config_path=config_path, label_path=label_path) # 获取后处理对象
pred = postprocessor.process([output])[0][0] # 利用sdk接口进行后处理，pred：<ClassInfo classId=... confidence=... className=...>
confidence = pred.confidence # 获取类别置信度
className = pred.className # 获取类别名称
print('{}: {}'.format(className, confidence)) # 打印出结果
'''保存推理图片'''
img_res = cv2.putText(img_bgr, f'{className}: {confidence:.2f}', (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1) # 将预测的类别与置信度添加到图片
cv2.imwrite('result.png', img_res)
print('save infer result success')
if __name__ == "__main__":
base.mx_init() # 初始化 mxVision 资源
process()
base.mx_deinit() # 去初始化 mxVision 资源
最后爆出的错误信息
Begin to initialize Log.
WARNING: Logging before InitGoogleLogging() is written to STDERR
I20251114 17:02:10.530218 281472986571200 FileUtils.cpp:510] Check Owner permission: Current permission is 7, but required no greater than 6.
The output directory of logs file exist.
I20251114 17:02:10.530591 281472986571200 FileUtils.cpp:354] The input file is empty
I20251114 17:02:10.530617 281472986571200 FileUtils.cpp:510] Check Other group permission: Current permission is 4, but required no greater than 0.
Save logs information to specified directory.
E20251114 17:02:10.544812 281472986571200 FileUtils.cpp:506] Check Owner permission failed: Current permission is 7, but required no greater than 6. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.545192 281472986571200 LogManager.hpp:139] Invalid logging.conf file! (Code = 1004, Message = "Invalid parameter")
E20251114 17:02:10.545231 281472986571200 GlobalInit.cpp:67] Init log rotate thread failed. (Code = 1004, Message = "Invalid parameter")
E20251114 17:02:10.545324 281472986571200 FileUtils.cpp:506] Check Owner permission failed: Current permission is 7, but required no greater than 6. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.545360 281472986571200 LogManager.hpp:139] Invalid logging.conf file! (Code = 1004, Message = "Invalid parameter")
E20251114 17:02:10.545391 281472986571200 GlobalInit.cpp:67] Init log rotate thread failed. (Code = 1004, Message = "Invalid parameter")
E20251114 17:02:10.582126 281472986571200 FileUtils.cpp:506] Check Owner permission failed: Current permission is 7, but required no greater than 6. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.582208 281472986571200 DeviceManager.cpp:36] Invalid lib file! (Code = 1001, Message = "General Failed")
E20251114 17:02:10.582244 281472986571200 DeviceManager.cpp:166] Check user permission failed, please check the owner of process. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.582275 281472986571200 DeviceManager.cpp:290] Init device failed. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.582305 281472986571200 MxOmModelDesc.cpp:78] Model setDevice failed, please check the state of device. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.582337 281472986571200 Model.cpp:58] MxModel init failed. (Code = 1001, Message = "General Failed")
E20251114 17:02:10.610692 281472986571200 MxOmModelDesc.cpp:368] Failed to unload model. (Calling Function = aclmdlUnload, Code = 145003, Message = "ACL error, please refer to the document of CANN.")
E20251114 17:02:10.610784 281472986571200 PyModel.cpp:41] Failed to create model_ object. (Code = 1007, Message = "memory allocation error")
Traceback (most recent call last):
File "/home/hkzy/workspace/test_model/resnet50_sdk_python_sample/main.py", line 54, in <module>
process()
File "/home/hkzy/workspace/test_model/resnet50_sdk_python_sample/main.py", line 36, in process
model = base.model(modelPath=model_path, deviceId=device_id) # 初始化 base.model 类
File "/home/hkzy/.local/lib/python3.10/site-packages/mindx/sdk/base/base.py", line 2215, in model
return _base.model(modelPath, deviceId)
RuntimeError: (Code = 1007, Message = "memory allocation error")