Skip to content

  • 体验新版
    • 正在加载...
  • 登录
  • PaddlePaddle
  • models
  • Issue
  • #2150

M
models
  • 项目概览

PaddlePaddle / models
大约 2 年 前同步成功

通知 232
Star 6828
Fork 2962
  • 代码
    • 文件
    • 提交
    • 分支
    • Tags
    • 贡献者
    • 分支图
    • Diff
  • Issue 602
    • 列表
    • 看板
    • 标记
    • 里程碑
  • 合并请求 255
  • Wiki 0
    • Wiki
  • 分析
    • 仓库
    • DevOps
  • 项目成员
  • Pages
M
models
  • 项目概览
    • 项目概览
    • 详情
    • 发布
  • 仓库
    • 仓库
    • 文件
    • 提交
    • 分支
    • 标签
    • 贡献者
    • 分支图
    • 比较
  • Issue 602
    • Issue 602
    • 列表
    • 看板
    • 标记
    • 里程碑
  • 合并请求 255
    • 合并请求 255
  • Pages
  • 分析
    • 分析
    • 仓库分析
    • DevOps
  • Wiki 0
    • Wiki
  • 成员
    • 成员
  • 收起侧边栏
  • 动态
  • 分支图
  • 创建新Issue
  • 提交
  • Issue看板
已关闭
开放中
Opened 4月 25, 2019 by saxon_zh@saxon_zhGuest

PaddleCV/image_classification/models/GoogleNet 模型训练时报错

Created by: liyutg

因为使用了自定义数据集,所以修改了读取数据的代码

系统信息

paddle 版本 1.3.2 clone image_classification作为demo 使用自己数据集 GPU 型号 Tesla K40m 11441MiB Please NOTE: device: 1, CUDA Capability: 35, Driver API Version: 10.1, Runtime API Version: 9.0 device: 0, cuDNN Version: 7.0.

  • 版本、环境信息:    1)GPU:Tesla K40m 11441MiB 、CUDA和CUDNN版本号 device: 0, CUDA Capability: 35, Driver API Version: 10.1, Runtime API Version: 9.0    2)系统环境:请您描述系统类型、版本,ubuntu 14.0,Python版本3.6

  • 训练信息    1)单机 单卡    2)显存信息 Tesla K40m 11441MiB

配置信息

-------------  Configuration Arguments -------------
               batch_size : 128
               checkpoint : None
                class_dim : 61
                 data_dir : ./data/ILSVRC2012
                enable_ce : False
                     fp16 : False
              image_shape : 3,224,224
              infer_model : ./infer_models
                 l2_decay : 0.0001
                       lr : 0.01
              lr_strategy : piecewise_decay
                    model : GoogleNet
           model_save_dir : output/
            momentum_rate : 0.9
               num_epochs : 100
         pretrained_model : None
               scale_loss : 1.0
              test_images : 4540
             total_images : 31718
                  use_gpu : True
               visual_num : 1000
             with_mem_opt : 1
----------------------------------------------------
W0425 00:56:32.982301 18073 device_context.cc:263] Please NOTE: device: 0, CUDA Capability: 35, Driver API Version: 10.1, Runtime API Version: 9.0
W0425 00:56:32.982357 18073 device_context.cc:271] device: 0, cuDNN Version: 7.0.

训练信息

image

报错信息

ass 0, trainbatch 0, loss 6.58166,                                             acc1 0.00781, acc5 0.05469, lr 0.01000, time 1.61 sec, scalar_train_index 0
Pass 0, trainbatch 24, loss 6.21634,                                             acc1 0.07812, acc5 0.18750, lr 0.01000, time 0.91 sec, scalar_train_index 1
Pass 0, trainbatch 48, loss 5.91886,                                             acc1 0.11719, acc5 0.31250, lr 0.01000, time 0.93 sec, scalar_train_index 2
Pass 0, trainbatch 72, loss 5.29668,                                             acc1 0.14062, acc5 0.45312, lr 0.01000, time 0.88 sec, scalar_train_index 3
Pass 0, trainbatch 96, loss 4.89611,                                             acc1 0.21875, acc5 0.55469, lr 0.01000, time 0.90 sec, scalar_train_index 4
Pass 0, trainbatch 120, loss 4.85930,                                             acc1 0.22656, acc5 0.51562, lr 0.01000, time 0.88 sec, scalar_train_index 5
Pass 0, trainbatch 144, loss 4.89562,                                             acc1 0.24219, acc5 0.53125, lr 0.01000, time 0.88 sec, scalar_train_index 6
Pass 0, trainbatch 168, loss 4.63307,                                             acc1 0.24219, acc5 0.53125, lr 0.01000, time 0.88 sec, scalar_train_index 7
Pass 0, trainbatch 192, loss 4.11545,                                             acc1 0.34375, acc5 0.67188, lr 0.01000, time 0.88 sec, scalar_train_index 8
Pass 0, trainbatch 216, loss 4.23675,                                             acc1 0.32031, acc5 0.60938, lr 0.01000, time 0.88 sec, scalar_train_index 9
Pass 0, trainbatch 240, loss 4.18755,                                             acc1 0.28125, acc5 0.63281, lr 0.01000, time 0.88 sec, scalar_train_index 10
Pass 0,testbatch 0,loss 6.68680,                                             acc1 0.00000,acc5 0.06250,time 0.34 sec ,scalar_test_index 0
Pass 0,testbatch 24,loss 1.63023,                                             acc1 0.31250,acc5 1.00000,time 0.07 sec ,scalar_test_index 1
Pass 0,testbatch 48,loss 4.17449,                                             acc1 0.43750,acc5 0.93750,time 0.08 sec ,scalar_test_index 2
Pass 0,testbatch 72,loss 2.61009,                                             acc1 0.43750,acc5 1.00000,time 0.08 sec ,scalar_test_index 3
Pass 0,testbatch 96,loss 6.07975,                                             acc1 0.18750,acc5 0.25000,time 0.08 sec ,scalar_test_index 4
Pass 0,testbatch 120,loss 1.64130,                                             acc1 0.62500,acc5 0.87500,time 0.08 sec ,scalar_test_index 5
Pass 0,testbatch 144,loss 1.27195,                                             acc1 0.75000,acc5 1.00000,time 0.08 sec ,scalar_test_index 6
Pass 0,testbatch 168,loss 6.13589,                                             acc1 0.00000,acc5 0.25000,time 0.08 sec ,scalar_test_index 7
Pass 0,testbatch 192,loss 2.45174,                                             acc1 0.87500,acc5 0.87500,time 0.07 sec ,scalar_test_index 8
Pass 0,testbatch 216,loss 4.23638,                                             acc1 0.37500,acc5 0.75000,time 0.07 sec ,scalar_test_index 9
Pass 0,testbatch 240,loss 1.77212,                                             acc1 0.68750,acc5 0.93750,time 0.07 sec ,scalar_test_index 10
Pass 0,testbatch 264,loss 3.91363,                                             acc1 0.37500,acc5 0.75000,time 0.07 sec ,scalar_test_index 11
End pass 0, train_loss 4.95904, train_acc1 0.22267, train_acc5 0.51104, test_loss 3.59753, test_acc1 0.40449, test_acc5 0.72014
Pass 1, trainbatch 0, loss 4.20429,                                             acc1 0.29688, acc5 0.73438, lr 0.01000, time 1.61 sec, scalar_train_index 11
Pass 1, trainbatch 24, loss 3.92477,                                             acc1 0.36719, acc5 0.68750, lr 0.01000, time 0.90 sec, scalar_train_index 12
Pass 1, trainbatch 48, loss 4.04725,                                             acc1 0.27344, acc5 0.66406, lr 0.01000, time 0.89 sec, scalar_train_index 13
Pass 1, trainbatch 72, loss 3.83627,                                             acc1 0.30469, acc5 0.71094, lr 0.01000, time 0.92 sec, scalar_train_index 14
Pass 1, trainbatch 96, loss 6.53986,                                             acc1 0.07031, acc5 0.20312, lr 0.01000, time 0.88 sec, scalar_train_index 15
Pass 1, trainbatch 120, loss 6.48425,                                             acc1 0.03906, acc5 0.28125, lr 0.01000, time 0.88 sec, scalar_train_index 16
Pass 1, trainbatch 144, loss 6.45244,                                             acc1 0.05469, acc5 0.28906, lr 0.01000, time 0.89 sec, scalar_train_index 17
Pass 1, trainbatch 168, loss 6.45208,                                             acc1 0.08594, acc5 0.20312, lr 0.01000, time 0.87 sec, scalar_train_index 18
Pass 1, trainbatch 192, loss 6.40190,                                             acc1 0.11719, acc5 0.34375, lr 0.01000, time 0.87 sec, scalar_train_index 19
Pass 1, trainbatch 216, loss 6.41929,                                             acc1 0.07812, acc5 0.19531, lr 0.01000, time 0.87 sec, scalar_train_index 20
Pass 1, trainbatch 240, loss 6.36903,                                             acc1 0.09375, acc5 0.28906, lr 0.01000, time 0.87 sec, scalar_train_index 21
Pass 1,testbatch 0,loss 6.78550,                                             acc1 0.00000,acc5 0.00000,time 0.15 sec ,scalar_test_index 12
Pass 1,testbatch 24,loss 5.59329,                                             acc1 1.00000,acc5 1.00000,time 0.06 sec ,scalar_test_index 13
Pass 1,testbatch 48,loss 6.63706,                                             acc1 0.00000,acc5 0.00000,time 0.07 sec ,scalar_test_index 14
Pass 1,testbatch 72,loss 6.58967,                                             acc1 0.00000,acc5 0.00000,time 0.08 sec ,scalar_test_index 15
Pass 1,testbatch 96,loss 6.71620,                                             acc1 0.00000,acc5 0.00000,time 0.06 sec ,scalar_test_index 16
Pass 1,testbatch 120,loss 5.87767,                                             acc1 0.00000,acc5 0.93750,time 0.07 sec ,scalar_test_index 17
Pass 1,testbatch 144,loss 5.82598,                                             acc1 0.00000,acc5 1.00000,time 0.06 sec ,scalar_test_index 18
Pass 1,testbatch 168,loss 6.67258,                                             acc1 0.00000,acc5 0.00000,time 0.06 sec ,scalar_test_index 19
Pass 1,testbatch 192,loss 6.20472,                                             acc1 0.00000,acc5 0.00000,time 0.06 sec ,scalar_test_index 20
Pass 1,testbatch 216,loss 6.26085,                                             acc1 0.00000,acc5 0.00000,time 0.06 sec ,scalar_test_index 21
Pass 1,testbatch 240,loss 6.48922,                                             acc1 0.00000,acc5 0.00000,time 0.06 sec ,scalar_test_index 22
Pass 1,testbatch 264,loss 6.29944,                                             acc1 0.00000,acc5 0.00000,time 0.06 sec ,scalar_test_index 23
End pass 1, train_loss 6.07456, train_acc1 0.16669, train_acc5 0.41099, test_loss 6.34295, test_acc1 0.07768, test_acc5 0.28389
Pass 2, trainbatch 0, loss 6.30323,                                             acc1 0.11719, acc5 0.32031, lr 0.01000, time 1.54 sec, scalar_train_index 22
Pass 2, trainbatch 24, loss 6.33684,                                             acc1 0.08594, acc5 0.25781, lr 0.01000, time 0.91 sec, scalar_train_index 23
Pass 2, trainbatch 48, loss 6.27985,                                             acc1 0.06250, acc5 0.28125, lr 0.01000, time 0.93 sec, scalar_train_index 24
Pass 2, trainbatch 72, loss 6.29569,                                             acc1 0.07031, acc5 0.28125, lr 0.01000, time 0.87 sec, scalar_train_index 25
Pass 2, trainbatch 96, loss 6.24136,                                             acc1 0.09375, acc5 0.28906, lr 0.01000, time 0.87 sec, scalar_train_index 26
Pass 2, trainbatch 120, loss 6.27440,                                             acc1 0.07812, acc5 0.30469, lr 0.01000, time 0.87 sec, scalar_train_index 27
Pass 2, trainbatch 144, loss 6.16809,                                             acc1 0.11719, acc5 0.33594, lr 0.01000, time 0.87 sec, scalar_train_index 28
Pass 2, trainbatch 168, loss 6.29105,                                             acc1 0.06250, acc5 0.21094, lr 0.01000, time 0.87 sec, scalar_train_index 29
Pass 2, trainbatch 192, loss 6.24925,                                             acc1 0.03906, acc5 0.26562, lr 0.01000, time 0.87 sec, scalar_train_index 30
Pass 2, trainbatch 216, loss 6.20490,                                             acc1 0.11719, acc5 0.25000, lr 0.01000, time 0.87 sec, scalar_train_index 31
Pass 2, trainbatch 240, loss 6.21943,                                             acc1 0.06250, acc5 0.25000, lr 0.01000, time 0.87 sec, scalar_train_index 32
Pass 2,testbatch 0,loss 7.05680,                                             acc1 0.00000,acc5 0.00000,time 0.43 sec ,scalar_test_index 24
Pass 2,testbatch 24,loss 4.81013,                                             acc1 1.00000,acc5 1.00000,time 0.07 sec ,scalar_test_index 25
Pass 2,testbatch 48,loss 6.74531,                                             acc1 0.00000,acc5 0.00000,time 0.07 sec ,scalar_test_index 26
Pass 2,testbatch 72,loss 6.61791,                                             acc1 0.00000,acc5 0.00000,time 0.07 sec ,scalar_test_index 27
Pass 2,testbatch 96,loss 6.89023,                                             acc1 0.00000,acc5 0.00000,time 0.07 sec ,scalar_test_index 28
Pass 2,testbatch 120,loss 5.39686,                                             acc1 0.00000,acc5 0.93750,time 0.07 sec ,scalar_test_index 29
Pass 2,testbatch 144,loss 5.29587,                                             acc1 0.00000,acc5 1.00000,time 0.07 sec ,scalar_test_index 30
Pass 2,testbatch 168,loss 6.78932,                                             acc1 0.00000,acc5 0.00000,time 0.06 sec ,scalar_test_index 31
Pass 2,testbatch 192,loss 5.88945,                                             acc1 0.00000,acc5 0.00000,time 0.07 sec ,scalar_test_index 32
Pass 2,testbatch 216,loss 6.10050,                                             acc1 0.00000,acc5 0.00000,time 0.07 sec ,scalar_test_index 33
Pass 2,testbatch 240,loss 6.39842,                                             acc1 0.00000,acc5 0.00000,time 0.06 sec ,scalar_test_index 34
Pass 2,testbatch 264,loss 6.07916,                                             acc1 0.00000,acc5 0.00000,time 0.06 sec ,scalar_test_index 35
End pass 2, train_loss 6.26088, train_acc1 0.07803, train_acc5 0.28214, test_loss 6.19724, test_acc1 0.07768, test_acc5 0.28389
Pass 3, trainbatch 0, loss 6.24757,                                             acc1 0.03125, acc5 0.25000, lr 0.01000, time 1.56 sec, scalar_train_index 33
Pass 3, trainbatch 24, loss 6.22961,                                             acc1 0.07812, acc5 0.25781, lr 0.01000, time 0.87 sec, scalar_train_index 34
Pass 3, trainbatch 48, loss 6.18353,                                             acc1 0.07812, acc5 0.24219, lr 0.01000, time 0.87 sec, scalar_train_index 35
Pass 3, trainbatch 72, loss 6.25078,                                             acc1 0.05469, acc5 0.21875, lr 0.01000, time 0.87 sec, scalar_train_index 36
Pass 3, trainbatch 96, loss 6.16289,                                             acc1 0.03906, acc5 0.29688, lr 0.01000, time 0.89 sec, scalar_train_index 37
Pass 3, trainbatch 120, loss 6.16478,                                             acc1 0.05469, acc5 0.29688, lr 0.01000, time 0.88 sec, scalar_train_index 38
Pass 3, trainbatch 144, loss 6.20497,                                             acc1 0.04688, acc5 0.24219, lr 0.01000, time 0.87 sec, scalar_train_index 39
Pass 3, trainbatch 168, loss 6.09273,                                             acc1 0.07031, acc5 0.32031, lr 0.01000, time 0.87 sec, scalar_train_index 40
Pass 3, trainbatch 192, loss 6.10254,                                             acc1 0.11719, acc5 0.28125, lr 0.01000, time 0.87 sec, scalar_train_index 41
Pass 3, trainbatch 216, loss 6.04383,                                             acc1 0.10938, acc5 0.30469, lr 0.01000, time 0.87 sec, scalar_train_index 42
Pass 3, trainbatch 240, loss 6.13893,                                             acc1 0.07812, acc5 0.25000, lr 0.01000, time 0.87 sec, scalar_train_index 43
Pass 3,testbatch 0,loss 7.25361,                                             acc1 0.00000,acc5 0.00000,time 0.20 sec ,scalar_test_index 36
Pass 3,testbatch 24,loss 4.50071,                                             acc1 1.00000,acc5 1.00000,time 0.07 sec ,scalar_test_index 37
Pass 3,testbatch 48,loss 6.80605,                                             acc1 0.00000,acc5 0.00000,time 0.06 sec ,scalar_test_index 38
Pass 3,testbatch 72,loss 6.62261,                                             acc1 0.00000,acc5 0.00000,time 0.07 sec ,scalar_test_index 39
Pass 3,testbatch 96,loss 7.07348,                                             acc1 0.00000,acc5 0.00000,time 0.08 sec ,scalar_test_index 40
Pass 3,testbatch 120,loss 5.02440,                                             acc1 0.00000,acc5 1.00000,time 0.08 sec ,scalar_test_index 41
Pass 3,testbatch 144,loss 5.04806,                                             acc1 0.00000,acc5 1.00000,time 0.06 sec ,scalar_test_index 42
Pass 3,testbatch 168,loss 6.88153,                                             acc1 0.00000,acc5 0.00000,time 0.06 sec ,scalar_test_index 43
Pass 3,testbatch 192,loss 5.70489,                                             acc1 0.00000,acc5 0.00000,time 0.06 sec ,scalar_test_index 44
Pass 3,testbatch 216,loss 5.98661,                                             acc1 0.00000,acc5 0.00000,time 0.07 sec ,scalar_test_index 45
Pass 3,testbatch 240,loss 6.33267,                                             acc1 0.00000,acc5 0.00000,time 0.06 sec ,scalar_test_index 46
Pass 3,testbatch 264,loss 5.92419,                                             acc1 0.00000,acc5 0.00000,time 0.07 sec ,scalar_test_index 47
End pass 3, train_loss 6.16399, train_acc1 0.07803, train_acc5 0.28201, test_loss 6.13189, test_acc1 0.07768, test_acc5 0.28389
Pass 4, trainbatch 0, loss 6.19262,                                             acc1 0.06250, acc5 0.27344, lr 0.01000, time 1.48 sec, scalar_train_index 44
Pass 4, trainbatch 24, loss 6.09134,                                             acc1 0.04688, acc5 0.29688, lr 0.01000, time 0.91 sec, scalar_train_index 45
Pass 4, trainbatch 48, loss 6.10587,                                             acc1 0.11719, acc5 0.28125, lr 0.01000, time 0.91 sec, scalar_train_index 46
Pass 4, trainbatch 72, loss 6.08864,                                             acc1 0.06250, acc5 0.32031, lr 0.01000, time 0.87 sec, scalar_train_index 47
Pass 4, trainbatch 96, loss 6.17017,                                             acc1 0.06250, acc5 0.26562, lr 0.01000, time 0.87 sec, scalar_train_index 48
Traceback (most recent call last):
  File "train.py", line 536, in <module>
    main()
  File "train.py", line 532, in main
    train(args)
  File "train.py", line 401, in train
    fetch_list=train_fetch_list)
  File "/home/fzuir/.local/lib/python3.6/site-packages/paddle/fluid/parallel_executor.py", line 303, in run
    self.executor.run(fetch_list, fetch_var_name)
paddle.fluid.core.EnforceNotMet: an illegal memory access was encountered at [/paddle/paddle/fluid/platform/device_context.cc:328]
PaddlePaddle Call Stacks: 
0       0x7f93dc29de45p void paddle::platform::EnforceNotMet::Init<char const*>(char const*, char const*, int) + 357
1       0x7f93dc29e1c9p paddle::platform::EnforceNotMet::EnforceNotMet(std::__exception_ptr::exception_ptr, char const*, int) + 137
2       0x7f93dddf8aa6p
3       0x7f93dde1b1b4p paddle::platform::TemporaryAllocator::Release(std::function<void ()> const&) + 100
4       0x7f93dddfaac1p paddle::platform::CUDADeviceContext::Wait() const + 113
5       0x7f93ddb7b285p paddle::framework::details::ScopeBufferedSSAGraphExecutor::Run(std::vector<std::string, std::allocator<std::string> > const&) + 1445
6       0x7f93dc3d91f2p paddle::framework::ParallelExecutor::Run(std::vector<std::string, std::allocator<std::string> > const&, std::string const&) + 562
7       0x7f93dc28dfcep
8       0x7f93dc2c91eep
9       0x7f942c334302p _PyCFunction_FastCallDict + 258
10      0x7f942c3b995bp
11      0x7f942c3bcd40p _PyEval_EvalFrameDefault + 11328
12      0x7f942c3b8100p
13      0x7f942c3b9b2ap
14      0x7f942c3bd2ccp _PyEval_EvalFrameDefault + 12748
15      0x7f942c3b8100p
16      0x7f942c3b9b2ap
17      0x7f942c3bcd40p _PyEval_EvalFrameDefault + 11328
18      0x7f942c3b7514p
19      0x7f942c3b9c88p
20      0x7f942c3bcd40p _PyEval_EvalFrameDefault + 11328
21      0x7f942c3b8100p
22      0x7f942c3b8583p PyEval_EvalCodeEx + 99
23      0x7f942c3b85cbp PyEval_EvalCode + 59
24      0x7f942c3eaee0p PyRun_FileExFlags + 304
25      0x7f942c3ec4a3p PyRun_SimpleFileExFlags + 371
26      0x7f942c4078d5p Py_Main + 3621
27            0x400c1dp main + 365
28      0x7f942b396f45p __libc_start_main + 245
29            0x4009e9p

terminate called after throwing an instance of 'paddle::platform::EnforceNotMet'
  what():  an illegal memory access was encountered at [/paddle/paddle/fluid/platform/device_context.cc:328]
PaddlePaddle Call Stacks: 
0       0x7f93dc29de45p void paddle::platform::EnforceNotMet::Init<char const*>(char const*, char const*, int) + 357
1       0x7f93dc29e1c9p paddle::platform::EnforceNotMet::EnforceNotMet(std::__exception_ptr::exception_ptr, char const*, int) + 137
2       0x7f93dddf8aa6p
3       0x7f93dde1b1b4p paddle::platform::TemporaryAllocator::Release(std::function<void ()> const&) + 100
4       0x7f93dddfaac1p paddle::platform::CUDADeviceContext::Wait() const + 113
5       0x7f93dc3d7102p paddle::framework::ParallelExecutor::~ParallelExecutor() + 98
6       0x7f93dc29933ap
7       0x7f93dc2ccbfap
8       0x7f93dc2ccd7fp
9       0x7f942c329c3dp
10      0x7f942c348bd5p
11      0x7f942c30e632p
12      0x7f942c3f65a7p
13      0x7f942c3f65b7p
14      0x7f942c3f65b7p
15      0x7f942c327e57p
16      0x7f942c328690p PyDict_SetItemString + 64
17      0x7f942c3db723p PyImport_Cleanup + 131
18      0x7f942c3e8523p Py_FinalizeEx + 115
19      0x7f942c40711ap Py_Main + 1642
20            0x400c1dp main + 365
21      0x7f942b396f45p __libc_start_main + 245
22            0x4009e9p

*** Aborted at 1556126047 (unix time) try "date -d @1556126047" if you are using GNU date ***
PC: @                0x0 (unknown)
*** SIGABRT (@0x3e800004699) received by PID 18073 (TID 0x7f942cb6e740) from PID 18073; stack trace: ***
    @     0x7f942c063330 (unknown)
    @     0x7f942b3abc37 gsignal
    @     0x7f942b3af028 abort
    @     0x7f9429eb6415 __gnu_cxx::__verbose_terminate_handler()
    @     0x7f9429eb4206 (unknown)
    @     0x7f9429eb31c9 (unknown)
    @     0x7f9429eb3b38 __gxx_personality_v0
    @     0x7f9429c42f43 (unknown)
    @     0x7f9429c4376e _Unwind_Resume
    @     0x7f93dddfab93 paddle::platform::CUDADeviceContext::Wait()
    @     0x7f93dc3d7102 paddle::framework::ParallelExecutor::~ParallelExecutor()
    @     0x7f93dc29933a pybind11::class_<>::dealloc()
    @     0x7f93dc2ccbfa pybind11::detail::clear_instance()
    @     0x7f93dc2ccd7f pybind11_object_dealloc
    @     0x7f942c329c3d dict_dealloc
    @     0x7f942c348bd5 subtype_dealloc
    @     0x7f942c30e632 frame_dealloc
    @     0x7f942c3f65a7 tb_dealloc
    @     0x7f942c3f65b7 tb_dealloc
    @     0x7f942c3f65b7 tb_dealloc
    @     0x7f942c327e57 insertdict
    @     0x7f942c328690 PyDict_SetItemString
    @     0x7f942c3db723 PyImport_Cleanup
    @     0x7f942c3e8523 Py_FinalizeEx
    @     0x7f942c40711a Py_Main
    @           0x400c1d main
    @     0x7f942b396f45 __libc_start_main
    @           0x4009e9 (unknown)
指派人
分配到
无
里程碑
无
分配里程碑
工时统计
无
截止日期
无
标识: paddlepaddle/models#2150
渝ICP备2023009037号

京公网安备11010502055752号

网络110报警服务 Powered by GitLab CE v13.7
开源知识
Git 入门 Pro Git 电子书 在线学 Git
Markdown 基础入门 IT 技术知识开源图谱
帮助
使用手册 反馈建议 博客
《GitCode 隐私声明》 《GitCode 服务条款》 关于GitCode
Powered by GitLab CE v13.7