• [问题求助] 请问转模型处的input image preprocess和编排节点的preprocess是什么关系
    我看着两处都可能或可以处理减均值和乘以归一化因子等,他们有什么区别吗?是不是可以相互替换?我貌似没在文档中看到这两处的联系和区别说明,谢谢。
  • [问题求助] 测试分类网络Engine编排样例时,Image Result查看结果报错
    根据文档编译运行都通过了,但用Image Result查看结果时出现错误mindstudio的版本号是 1.1.1.B889开发板的 firmware 版本号是 1.1.8.750
  • [问题求助] 请问模型转换是不是不支持tf.image.resize_bilinear?
    根据log里面的信息,好像是tf.image.resize_bilinear这个地方出了问题。dims显示的输入和输出维度是对应这个函数的。我把log贴在下面,请专家解答下。如果不支持,我可以用自定义算子来代替吗?再就是语义分割里面resize是用得很多的,是否目前的工具还未考虑语义分割? FMK:2020-01-13-23:56:13.060.899 SetInputDesc:framework/domi/omg/../omg/model/model_builder.cpp:504:"dims[0]: 1" FMK:2020-01-13-23:56:13.061.019 SetInputDesc:framework/domi/omg/../omg/model/model_builder.cpp:504:"dims[1]: 256" FMK:2020-01-13-23:56:13.061.146 SetInputDesc:framework/domi/omg/../omg/model/model_builder.cpp:504:"dims[2]: 1" FMK:2020-01-13-23:56:13.061.384 SetInputDesc:framework/domi/omg/../omg/model/model_builder.cpp:504:"dims[3]: 1" FMK:2020-01-13-23:56:13.061.549 SetInputDesc:framework/domi/omg/../omg/model/model_builder.cpp:501:"__add ResizeBilinear input desc, dim_size: 2, mem_size: 8, real_dim_cnt: 2, format: 3." FMK:2020-01-13-23:56:13.061.676 SetInputDesc:framework/domi/omg/../omg/model/model_builder.cpp:504:"dims[0]: 1" FMK:2020-01-13-23:56:13.061.795 SetInputDesc:framework/domi/omg/../omg/model/model_builder.cpp:504:"dims[1]: 1" FMK:2020-01-13-23:56:13.062.104 GetOutputDesc:framework/domi/omg/../omg/model/op_builder/resize_bilinear_op_builder.cpp:74:"ResizeBilinear hi = 1 ,ho = 65 ,wi = 1, wo = 129,align_corners = 1" FMK:2020-01-13-23:56:13.062.606 GetOutputDesc:framework/domi/omg/../omg/model/op_builder/resize_bilinear_op_builder.cpp:79:"divisor may be zero."[ERROR] FMK:2020-01-13-23:56:13.062.725 Build:framework/domi/omg/../omg/model/op_builder/op_builder.cpp:38:""Get output descriptors" failed. Node: ResizeBilinear."[ERROR] FMK:2020-01-13-23:56:13.062.845 SetInputOutputDesc:framework/domi/omg/../omg/model/model_builder.cpp:575:"Op build failed. Node: ResizeBilinear."[ERROR] FMK:2020-01-13-23:56:13.063.121 Build:framework/domi/omg/../omg/model/model_builder.cpp:2838:"SetInputOutputDesc Failed!"[ERROR] FMK:2020-01-13-23:56:13.063.260 Generate:framework/domi/omg/omg.cpp:813:"OMG builder Build() return fail."[ERROR] FMK:2020-01-13-23:56:13.100.362 main:framework/domi/omg_main/main.cpp:791:"OMG Generate execute failed!!"OMG generate offline model failed. Please see the log or pre-checking report for more details. RUNTIME:2020-01-13-23:56:13.121.255 49662 runtime/feature/src/driver.cc:57 ~Driver:deconstruct driver
  • [Atlas200] 关于Linux kernel Image问题
    《Atlas 200 软件开发指南 02.pdf》第2章节 ‘安全启动特性’ 里面写到:由于安全启动校验特性,不允许替换表2-1中的7个二进制文件,否则会导致系统无法正常启动。但现在我需要修改Linux kernel 配置选项重新编译出Image,来增加外设驱动支持(不能以ko模块形式编译)。但编译出来的Image是不能启动,这问题怎么解决?另外,还需要修改dtb文件。按介绍说dtb同样也是不能替换的............
  • [问题求助] 提交模型判分时,显示build model image failed是什么原因
    求问模型在线预测正常但是在提交判分的时候,显示   build model image failed这种报错一般会是什么原因造成的
  • [问题求助] 预置算法里面的参数image_size填写报错
    absl.flags._exceptions.IllegalFlagValueError: flag --image_size=299*299: invalid literal for int() with base 10: '299*299 预置算法训练,填写参数image_size=299*299报错,算法选的inception_v3
  • [问题求助] PIL.Image读取图片失败
    我在ipynb文件读取同目录下的图片报错:请问有人知道为什么嘛,谢谢
  • [问题求助] build model image failed问题
    想问一下,模型在线部署和批量部署都预测成功,但在发布到比赛后。提交作品中的反馈信息一直出现build model image failed,也没有日志。目前有很多人遇到这个情况,请问这是什么原因呢?
  • [问题求助] 模型在线部署成功,提交格式也没问题,上交大赛平台判分,显示build model image failed
    求问在线部署 是不是和 后台modelhub判分系统一致?为什么在线部署预测成功,但是判分一直显示build model image failed呢?没有日志也无法参考,据猜测应该是连模型都没跑起来,挂在了环境配置上。之前成功出分的提交,基本需要耗时2小时,这些image failed提交基本几分钟就打回来了。
  • [问题求助] 使用docker image成功导入了模型,并且成功部署。为什么不能发布参赛呢?
    使用docker image成功导入了模型,并且成功部署。为什么不能发布参赛呢?比赛就是华为平台的AI大赛
  • [基础介绍] 镜像中可执行区域的标识符-Image$$...
    在阅读Lite OS 的los_startup_keil.s文件时,会碰到如下一些语句                AREA    RESET, CODE, READONLY                THUMB                IMPORT  ||Image$$ARM_LIB_STACKHEAP$$ZI$$Limit||                EXPORT  _BootVectors                EXPORT  Reset_Handler_BootVectors                DCD     ||Image$$ARM_LIB_STACKHEAP$$ZI$$Limit||这里的Image$$ARM_LIB_STACKHEAP$$ZI$$Limit是一种镜像文件中的可执行区域的标识符,即可以看出类似变量一样。在这里用IMPORT表示应用,即这里应用镜像文件中的标识符。在Keil 开发环境中,有如下的镜像文件中断标识符可以使用。Symbol 符号Description 描述Image$$region_name$$BaseExecution address of the region. 可执行区域的地址,一般指开始的地址Image$$region_name$$LengthExecution region length in bytes excluding ZI length. 区域的长度Image$$region_name$$LimitAddress of the byte beyond the end of the non-ZI part of the execution region. 区域中非ZI部分的结束地址Image$$region_name$$RO$$BaseExecution address of the RO output section in this region. RO区域的首地址Image$$region_name$$RO$$LengthLength of the RO output section in bytes. RO区域的长度,以字节记。Image$$region_name$$RO$$LimitAddress of the byte beyond the end of the RO output section in the execution region.RO 区域的结束地址。Image$$region_name$$RW$$BaseExecution address of the RW output section in this region.RW区域的首地址Image$$region_name$$RW$$LengthLength of the RW output section in bytes.RW区域的长度,以字节记Image$$region_name$$RW$$LimitAddress of the byte beyond the end of the RW output section in the execution region.RW区域的结束地址Image$$region_name$$XO$$BaseExecution address of the XO output section in this region.XO 输出区域的首地址Image$$region_name$$XO$$LengthLength of the XO output section in bytes.XO区域的长度,以字节记Image$$region_name$$XO$$LimitAddress of the byte beyond the end of the XO output section in the execution region.XO区域的结束地址Image$$region_name$$ZI$$BaseExecution address of the ZI output section in this region.ZI区域的首地址Image$$region_name$$ZI$$LengthLength of the ZI output section in bytes.ZI区域的长度,以自己记Image$$region_name$$ZI$$LimitAddress of the byte beyond the end of the ZI output section in the execution region.ZI区域的结束地址要在汇编语言中应用这些地址,就需要使用IMPORT关键字进行声明,例如IMPORT  ||Image$$ARM_LIB_STACKHEAP$$ZI$$Limit||这样就可以在汇编程序中使用||Image$$ARM_LIB_STACKHEAP$$ZI$$Limit||
  • [训练管理] IndexError: list index out of range ( read_image_to_list.py)
    训练管理, 创建训练作业, 使用的是flower的样例. 训练程序跑了一个多小时, 最后显示运行失败了.  File "/home/work/anaconda/lib/python2.7/site-packages/moxing/framework/common/data_utils/read_image_to_list.py", line 604, in get_image_classese_raw    image_list_train.append(image_set)IndexError: list index out of range详细的错误信息如下:do nothing[Modelarts Service Log]user: uid=1101(work) gid=1101(work) groups=1101(work)[Modelarts Service Log]pwd: /home/work[Modelarts Service Log]app_url: s3://cnnorth4-job-train-algorithm/internal-code/1-24-1/MXNet-1.1/cnn/[Modelarts Service Log]boot_file: cnn/train_wrapper.py[Modelarts Service Log]log_url: /tmp/log/trainjob-b68f.log[Modelarts Service Log]command: cnn/train_wrapper.py --data_url=s3://test-modelarts-20200726/test-modelarts/ --split_spec=0.8 --batch_size=4 --lr=0.0001 --save_frequency=1 --num_classes=1 --num_epoch=10 --num_gpus=1 --train_url=s3://test-modelarts-20200726/dataset-flower/ --model_name=resnet_v2_50 --checkpoint_url=s3://cnnorth4-job-train-algorithm/pretrained-models/1-24-1/MXNet/resnet_v2_50/[Modelarts Service Log]dependencies_file_dir: /home/work/user-job-dir/cnn[Modelarts Service Log][modelarts_create_log] modelarts-pipe found[Modelarts Service Log]handle inputs of training jobINFO:root:Using MoXing-v1.16.5-c084e6c6INFO:root:Using OBS-Python-SDK-3.1.2[ModelArts Service Log][2020/07/27 00:11:30]: env MA_INPUTS is not found, skip the inputs handlerINFO:root:Using MoXing-v1.16.5-c084e6c6INFO:root:Using OBS-Python-SDK-3.1.2[ModelArts Service Log]2020-07-27 00:11:31,705 - modelarts-downloader.py[line:619] - INFO: Main: modelarts-downloader starting with Namespace(dst='./', recursive=True, skip_creating_dir=False, src='s3://cnnorth4-job-train-algorithm/internal-code/1-24-1/MXNet-1.1/cnn/', trace=False, type='common', verbose=False)/home/work/user-job-dir[Modelarts Service Log][modelarts_logger] modelarts-pipe foundINFO:root:Using MoXing-v1.16.5-c084e6c6INFO:root:Using OBS-Python-SDK-3.1.2INFO:root:Listing OBS: 1000INFO:root:Listing OBS: 2000INFO:root:Listing OBS: 3000INFO:root:Listing OBS: 4000INFO:root:Listing OBS: 5000INFO:root:Listing OBS: 6000INFO:root:Listing OBS: 7000INFO:root:pid: None. 1000/7338INFO:root:pid: None. 2000/7338INFO:root:pid: None. 3000/7338INFO:root:pid: None. 4000/7338INFO:root:pid: None. 5000/7338INFO:root:pid: None. 6000/7338INFO:root:pid: None. 7000/7338INFO:root:Listing OBS: 1000INFO:root:Listing OBS: 2000INFO:root:Listing OBS: 3000INFO:root:Listing OBS: 4000INFO:root:Listing OBS: 5000INFO:root:Listing OBS: 6000INFO:root:Listing OBS: 7000INFO:root:Listing OBS: 1000INFO:root:Listing OBS: 2000INFO:root:Listing OBS: 3000INFO:root:Listing OBS: 4000INFO:root:Listing OBS: 5000INFO:root:Listing OBS: 6000INFO:root:Listing OBS: 7000Traceback (most recent call last):  File "cnn/train_wrapper.py", line 108, in <module>    train.train_cnn()  File "/cache/user-job-dir/cnn/train.py", line 358, in train_cnn    mox.read_list_url(args, 'cnn')  File "/home/work/anaconda/lib/python2.7/site-packages/moxing/mxnet/utils/read_list_data_url.py", line 35, in read_list_url    train_list, val_list, class_name, train_path, val_path = fine_tune(data_path, args)  File "/home/work/anaconda/lib/python2.7/site-packages/moxing/mxnet/utils/read_list_data_url.py", line 132, in fine_tune    train_list, val_list, class_name = get_image_list(train_path, args.split_spec, 'image_classification', False)  File "/home/work/anaconda/lib/python2.7/site-packages/moxing/framework/common/data_utils/read_image_to_list.py", line 213, in get_image_list    image_list_train, image_list_eval, class_name = get_image_classese_raw(data_path, split_spec, is_tf)  File "/home/work/anaconda/lib/python2.7/site-packages/moxing/framework/common/data_utils/read_image_to_list.py", line 604, in get_image_classese_raw    image_list_train.append(image_set)IndexError: list index out of range[Modelarts Service Log]Training end with return code: 1[Modelarts Service Log]Training completed.
  • [问题求助] 在运行facedetection样例时,send JPEG image失败
    【功能模块】在运行facedetection样例时,已启动presenter server,然后run facedetection,但是报错send JPEG image to presenter failed, error 1,请问有可能是什么问题?【截图信息】【日志信息】(可选,上传日志内容或者附件)
  • [问题求助] 【Mind Studio产品】【AIPP功能】Model Image Format和Input Image Format的区别
    Model Image Format和Input Image Format的区别是什么?我处理的是RGB图像,那么我的Input Image Format应该是RGB,Model Image Format就是BGR吗?
  • [交流分享] 图像预处理中Crop/Padding相关参数配置说明
    对于YUV420SP_U8图片类型,load_start_pos_w、load_start_pos_h、crop_size_w与crop_size_h四个参数必须配置为偶数。抠图后的图像的宽、高需和网络模型输入定义的w和h相等。配置样例如下:aipp_op {    aipp_mode: static    input_format : YUV420SP_U8    src_image_size_w :320    src_image_size_h :240    crop :true    load_start_pos_w :10    load_start_pos_h :20    crop_size_w :50    crop_size_h :60        padding : true    left_padding_size :10    right_padding_size :20    top_padding_size :10    bottom_padding_size :20}
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