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deepjavalibrary / djl

An Engine-Agnostic Deep Learning Framework in Java
https://djl.ai
Apache License 2.0
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TrainPikachu.main() works fine, but TrainPikachuWithYOLOV3.main() doesn't. #2226

Open saf59 opened 1 year ago

saf59 commented 1 year ago

Description

Error while running TrainPikachuWithYOLOV3.main()

Expected Behavior

Successful run of the example.

Error Message

Task :examples:TrainPikachuWithYOLOV3.main() [DEBUG] - Registering EngineProvider: PyTorch [DEBUG] - Registering EngineProvider: TensorFlow [DEBUG] - Registering EngineProvider: MXNet [DEBUG] - Found default engine: MXNet [DEBUG] - Found cudart: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin\cudart64_102.dll [DEBUG] - Using cache dir: C:\Users\alsh.djl.ai\mxnet\1.9.1-cu102mkl-win-x86_64 [DEBUG] - Loading mxnet library from: C:\Users\alsh.djl.ai\mxnet\1.9.1-cu102mkl-win-x86_64\mxnet.dll [DEBUG] - Preparing artifact: BasicDataset, ai.djl.basicdataset/pikachu/1.0/null {} [DEBUG] - Files have been downloaded already: C:\Users\alsh.djl.ai\cache\repo\dataset\cv\ai\djl\basicdataset\pikachu\1.0 [DEBUG] - Preparing artifact: BasicDataset, ai.djl.basicdataset/pikachu/1.0/null {} [DEBUG] - Files have been downloaded already: C:\Users\alsh.djl.ai\cache\repo\dataset\cv\ai\djl\basicdataset\pikachu\1.0 [ERROR] - Resource close failed. ai.djl.engine.EngineException: MXNet engine call failed: MXNetError: Check failed: param.interp == 1: interp should be 1 for using Resize on GPU. Stack trace: File "C:\source\mxnet\src\operator\image./resize-inl.h", line 181

at ai.djl.mxnet.jna.JnaUtils.checkCall(JnaUtils.java:1942) ~[mxnet-engine-0.21.0-SNAPSHOT.jar:?]
at ai.djl.mxnet.jna.JnaUtils.waitToRead(JnaUtils.java:475) ~[mxnet-engine-0.21.0-SNAPSHOT.jar:?]
at ai.djl.mxnet.engine.MxNDArray.close(MxNDArray.java:1637) ~[mxnet-engine-0.21.0-SNAPSHOT.jar:?]
at ai.djl.ndarray.BaseNDManager.close(BaseNDManager.java:435) ~[api-0.21.0-SNAPSHOT.jar:?]
at ai.djl.nn.LambdaBlock.getOutputShapes(LambdaBlock.java:127) ~[api-0.21.0-SNAPSHOT.jar:?]
at ai.djl.nn.Block.getOutputShapes(Block.java:263) ~[api-0.21.0-SNAPSHOT.jar:?]
at ai.djl.nn.SequentialBlock.initializeChildBlocks(SequentialBlock.java:226) ~[api-0.21.0-SNAPSHOT.jar:?]
at ai.djl.nn.AbstractBaseBlock.initialize(AbstractBaseBlock.java:187) ~[api-0.21.0-SNAPSHOT.jar:?]
at ai.djl.basicmodelzoo.cv.object_detection.yolo.YOLOV3.initializeChildBlocks(YOLOV3.java:309) ~[model-zoo-0.21.0-SNAPSHOT.jar:?]
at ai.djl.nn.AbstractBaseBlock.initialize(AbstractBaseBlock.java:187) ~[api-0.21.0-SNAPSHOT.jar:?]
at ai.djl.training.Trainer.initialize(Trainer.java:118) ~[api-0.21.0-SNAPSHOT.jar:?]
at ai.djl.examples.training.TrainPikachuWithYOLOV3.runExample(TrainPikachuWithYOLOV3.java:64) ~[main/:?]
at ai.djl.examples.training.TrainPikachuWithYOLOV3.main(TrainPikachuWithYOLOV3.java:44) ~[main/:?]

[ERROR] - Resource close failed.

How to Reproduce?

Run with or without CUDA.

Steps to reproduce

git clone https://github.com/deepjavalibrary/djl open project in IDEA run TrainPikachuWithYOLOV3.main()

What have you tried to solve it?

  1. disable CUDA
  2. change mxnet and cuda versions

Environment Info

Please run the command ./gradlew debugEnv from the root directory of DJL (if necessary, clone DJL first). It will output information about your system, environment, and installation that can help us debug your issue. Paste the output of the command below:

----------- System Properties -----------
java.specification.version: 19
sun.cpu.isalist: amd64
sun.jnu.encoding: UTF-8
java.class.path: W:\tmp\djl\integration\build\classes\java\main;W:\tmp\djl\integration\build\resources\main;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\commons-cli\commons-cli\1.5.0\dc98be5d5390230684a092589d70ea76a147925c\commons-cli-1.5.0.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\org.apache.logging.log4j\log4j-slf4j-impl\2.19.0\1a0c9615ba9fd5b96db8c1136afbef4394286e93\log4j-slf4j-impl-2.19.0.jar;W:\tmp\djl\basicdataset\build\libs\basicdataset-0.21.0-SNAPSHOT.jar;W:\tmp\djl\model-zoo\build\libs\model-zoo-0.21.0-SNAPSHOT.jar;W:\tmp\djl\testing\build\libs\testing-0.21.0-SNAPSHOT.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\org.testng\testng\7.5\1416a607fae667c14e390b484e8d02b5824c0674\testng-7.5.jar;W:\tmp\djl\engines\mxnet\mxnet-model-zoo\build\libs\mxnet-model-zoo-0.21.0-SNAPSHOT.jar;W:\tmp\djl\engines\pytorch\pytorch-model-zoo\build\libs\pytorch-model-zoo-0.21.0-SNAPSHOT.jar;W:\tmp\djl\engines\pytorch\pytorch-jni\build\libs\pytorch-jni-1.13.0-0.21.0-SNAPSHOT.jar;W:\tmp\djl\engines\tensorflow\tensorflow-model-zoo\build\libs\tensorflow-model-zoo-0.21.0-SNAPSHOT.jar;W:\tmp\djl\engines\ml\xgboost\build\libs\xgboost-0.21.0-SNAPSHOT.jar;W:\tmp\djl\engines\ml\lightgbm\build\libs\lightgbm-0.21.0-SNAPSHOT.jar;W:\tmp\djl\engines\mxnet\mxnet-engine\build\libs\mxnet-engine-0.21.0-SNAPSHOT.jar;W:\tmp\djl\engines\pytorch\pytorch-engine\build\libs\pytorch-engine-0.21.0-SNAPSHOT.jar;W:\tmp\djl\engines\tensorflow\tensorflow-engine\build\libs\tensorflow-engine-0.21.0-SNAPSHOT.jar;W:\tmp\djl\api\build\libs\api-0.21.0-SNAPSHOT.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\org.slf4j\slf4j-api\1.7.36\6c62681a2f655b49963a5983b8b0950a6120ae14\slf4j-api-1.7.36.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\org.apache.logging.log4j\log4j-core\2.19.0\3b6eeb4de4c49c0fe38a4ee27188ff5fee44d0bb\log4j-core-2.19.0.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\org.apache.logging.log4j\log4j-api\2.19.0\ea1b37f38c327596b216542bc636cfdc0b8036fa\log4j-api-2.19.0.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\org.apache.commons\commons-csv\1.9.0\b59d8f64cd0b83ee1c04ff1748de2504457018c1\commons-csv-1.9.0.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\com.google.code.findbugs\jsr305\3.0.1\f7be08ec23c21485b9b5a1cf1654c2ec8c58168d\jsr305-3.0.1.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\com.beust\jcommander\1.78\a3927de9bd6f351429bcf763712c9890629d8f51\jcommander-1.78.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\org.webjars\jquery\3.5.1\2392938e374f561c27c53872bdc9b6b351b6ba34\jquery-3.5.1.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\ml.dmlc\xgboost4j_2.12\1.7.1\badb8561aab6c1534353d951f409d883c08aeefb\xgboost4j_2.12-1.7.1.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\commons-logging\commons-logging\1.2\4bfc12adfe4842bf07b657f0369c4cb522955686\commons-logging-1.2.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\com.microsoft.ml.lightgbm\lightgbmlib\3.2.110\f6c85e5d7cc44d49c4544240ea5c96004680007b\lightgbmlib-3.2.110.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\com.google.code.gson\gson\2.10\dd9b193aef96e973d5a11ab13cd17430c2e4306b\gson-2.10.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\net.java.dev.jna\jna\5.12.1\b1e93a735caea94f503e95e6fe79bf9cdc1e985d\jna-5.12.1.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\org.apache.commons\commons-compress\1.22\691a8b4e6cf4248c3bc72c8b719337d5cb7359fa\commons-compress-1.22.jar;W:\tmp\djl\engines\tensorflow\tensorflow-api\build\libs\tensorflow-api-0.21.0-SNAPSHOT.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\org.tensorflow\tensorflow-core-api\0.4.2\3a40c3304bede0e750c9247800c41e627e094c39\tensorflow-core-api-0.4.2.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\org.bytedeco\javacpp\1.5.6\1f18a820aadd943577b0b372554f9e35e1232e25\javacpp-1.5.6.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\com.google.protobuf\protobuf-java\3.21.9\ed1240d9231044ce6ccf1978512f6e44416bb7e7\protobuf-java-3.21.9.jar;C:\Users\alsh\.gradle\caches\modules-2\files-2.1\org.tensorflow\ndarray\0.3.3\1b6d8cc3e3762f6e465b884580d9fc17ab7aeb4\ndarray-0.3.3.jar
java.vm.vendor: GraalVM Community
sun.arch.data.model: 64
user.variant: 
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user.timezone: Europe/Kiev
java.vm.specification.version: 19
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sun.boot.library.path: C:\Program Files\Java\graalvm-ce-java19-22.3.0\bin;C:\Program Files\Java\graalvm-ce-java19-22.3.0\bin
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library.jansi.path: C:\Users\alsh\.gradle\native\jansi\1.18\windows64
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DIRNAME: W:\tmp\djl\
USERNAME: alsh
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ConEmuArgs2: 
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2
PROCESSOR_REVISION: 2a07
CLASSPATH: W:\tmp\djl\\gradle\wrapper\gradle-wrapper.jar
CommonProgramW6432: C:\Program Files\Common Files
ConEmuPID: 18216
ConEmuDir: C:\Program Files (x86)\ConEmu
NVCUDASAMPLES11_4_ROOT: C:\ProgramData\NVIDIA Corporation\CUDA Samples\v11.4
SystemRoot: C:\WINDOWS
TEMP: C:\Temp
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TMP: C:\Temp
ConEmuPalette: <Default Windows scheme>
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-------------- Directories --------------
temp directory: C:\Temp
DJL cache directory: C:\Users\alsh\.djl.ai
Engine cache directory: C:\Users\alsh\.djl.ai

------------------ CUDA -----------------
[DEBUG] - Found cudart: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin\cudart64_102.dll
GPU Count: 1
CUDA: 102
ARCH: 61
GPU(0) memory used: 1034682368 bytes

----------------- Engines ---------------
DJL version: 0.21.0-SNAPSHOT
[DEBUG] - Using cache dir: C:\Users\alsh\.djl.ai\mxnet\1.9.1-cu102mkl-win-x86_64
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Default Device: gpu(0)
PyTorch: 2
MXNet: 0
XGBoost: 10
LightGBM: 10
TensorFlow: 3

--------------- Hardware --------------
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Byte Order: LITTLE_ENDIAN
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Maximum memory (bytes): 4284481536
Total memory available to JVM (bytes): 268435456
Heap committed: 268435456
Heap nonCommitted: 25165824
KexinFeng commented 1 year ago

Sorry for the late reply. The reason it failed on Yolov3 looks like that the MXNet engine is used. Currently for Yolov3 training, it runs succesfully on PyTorch. This can be specified by setting -Dai.djl.default_engine=PyTorch in VM option.

saf59 commented 1 year ago

Hello!

Thanks for your reply! Do you have plans to expand on YOLO examples, such as YOLO8 or predictions?

Regards, Alexandr.

On Mon, 30 Jan 2023 at 21:31, KexinFeng @.***> wrote:

Sorry for the late reply. The reason it failed on Yolov3 looks like that the MXNet engine is used. Currently for Yolov3 training, it runs succesfully on PyTorch. This can be specified by setting -Dai.djl.default_engine=PyTorch in VM option.

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