TypeError: deserialize_cuda_engine(): incompatible function arguments. The following argument types are supported: 1. (self: tensorrt.tensorrt.Runtime, serialized_engine: buffer) -> tensorrt.tensorrt.ICudaEngine

[Tanzichang]

Hello,

I have got a error when I transport ONNX to TRT model by using sh samples/bevformer/tiny/onnx2trt.sh -d 0. The detailed log information is shown as following:

[02/25/2023-12:57:54] [TRT] [V] Loaded shared library libcublasLt.so.11 [02/25/2023-12:57:54] [TRT] [V] Using cublasLt as core library tactic source [02/25/2023-12:57:54] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +650, GPU +274, now: CPU 2219, GPU 2942 (MiB) [02/25/2023-12:57:54] [TRT] [V] Trying to load shared library libcudnn.so.8 [02/25/2023-12:57:54] [TRT] [V] Loaded shared library libcudnn.so.8 [02/25/2023-12:57:54] [TRT] [V] Using cuDNN as plugin tactic source [02/25/2023-12:57:55] [TRT] [V] Using cuDNN as core library tactic source [02/25/2023-12:57:55] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +178, GPU +266, now: CPU 2397, GPU 3208 (MiB) [02/25/2023-12:57:55] [TRT] [W] TensorRT was linked against cuDNN 8.6.0 but loaded cuDNN 8.1.1 [02/25/2023-12:57:55] [TRT] [I] Local timing cache in use. Profiling results in this builder pass will not be stored. [02/25/2023-12:57:55] [TRT] [V] Constructing optimization profile number 0 [1/1]. [02/25/2023-12:57:55] [TRT] [E] 1: [constantBuilder.cpp::addSupportedFormats::32] Error Code 1: Internal Error (Constant output type does not support bool datatype.) [02/25/2023-12:57:55] [TRT] [E] 2: [builder.cpp::buildSerializedNetwork::751] Error Code 2: Internal Error (Assertion engine != nullptr failed. ) Traceback (most recent call last): File "tools/bevformer/onnx2trt.py", line 262, in main() File "tools/bevformer/onnx2trt.py", line 257, in main calibrator=calibrator, File "/root/paddlejob/workspace/env_run/BEVFormer_Tensorrt/det2trt/convert/onnx2tensorrt.py", line 63, in build_engine engine = runtime.deserialize_cuda_engine(plan) TypeError: deserialize_cuda_engine(): incompatible function arguments. The following argument types are supported:

1. (self: tensorrt.tensorrt.Runtime, serialized_engine: buffer) -> tensorrt.tensorrt.ICudaEngine

Invoked with: <tensorrt.tensorrt.Runtime object at 0x7fae1710bd30>, None

[DerryHub]

Please provide your environments and tell the difference between yours and README.

[HerrYu123]

Hi, I got a similar error. My environments: Tensorrt 8.5.1.7 cudnn 8.5.0 CUDA-11.6 onnx 1.12.0 torch 1.12.0

[02/28/2023-10:51:30] [TRT] [E] 1: [constantBuilder.cpp::addSupportedFormats::32] Error Code 1: Internal Error (Constant output type does not support bool datatype.)
[02/28/2023-10:51:30] [TRT] [E] 2: [builder.cpp::buildSerializedNetwork::751] Error Code 2: Internal Error (Assertion engine != nullptr failed. )
Traceback (most recent call last):
  File "tools/bevformer/onnx2trt.py", line 255, in <module>
    main()
  File "tools/bevformer/onnx2trt.py", line 243, in main
    build_engine(
  File "/root/autodl-tmp/BEVFormer_tensorrt/./det2trt/convert/onnx2tensorrt.py", line 63, in build_engine
    engine = runtime.deserialize_cuda_engine(plan)
TypeError: deserialize_cuda_engine(): incompatible function arguments. The following argument types are supported:
    1. (self: tensorrt.tensorrt.Runtime, serialized_engine: buffer) -> tensorrt.tensorrt.ICudaEngine

Invoked with: <tensorrt.tensorrt.Runtime object at 0x7fdab6d48630>, None

[DerryHub]

Please give me your ONNX file and I will check it on my device.

[HerrYu123]

Thanks a lot! My onnx file is here

[DerryHub]

Thanks a lot! My onnx file is here

samples/bevformer/base/onnx2trt.sh requires a lot of GPU memory with tensorrt 8.5.1.7 as mentioned in README. Did you try bevformer small or tiny? I suggest you monitor GPU memory while running samples/bevformer/base/onnx2trt.sh. Or you can try small or tiny models or custom plugin version.

[HerrYu123]

Thanks a lot! My onnx file is here

samples/bevformer/base/onnx2trt.sh requires a lot of GPU memory with tensorrt 8.5.1.7 as mentioned in README. Did you try bevformer small or tiny? I suggest you monitor GPU memory while running samples/bevformer/base/onnx2trt.sh. Or you can try small or tiny models or custom plugin version.

Thanks for your reply. I used the RTX3090 with 24GB memory and didn't find out of GPU memory error while running samples/bevforemr/base/onnx2trt.sh. I'll try bevfomer small and tiny latter. By the way, I would appreciate it if you could provide me with a converted bevformer_base_trt file. Thanks again!

[Tanzichang]

Please provide your environments and tell the difference between yours and README.

Thank you. My problem has been solved after setting the environment to be the same as yours。

[DerryHub]

Thanks a lot! My onnx file is here

samples/bevformer/base/onnx2trt.sh requires a lot of GPU memory with tensorrt 8.5.1.7 as mentioned in README. Did you try bevformer small or tiny? I suggest you monitor GPU memory while running samples/bevformer/base/onnx2trt.sh. Or you can try small or tiny models or custom plugin version.

Thanks for your reply. I used the RTX3090 with 24GB memory and didn't find out of GPU memory error while running samples/bevforemr/base/onnx2trt.sh. I'll try bevfomer small and tiny latter. By the way, I would appreciate it if you could provide me with a converted bevformer_base_trt file. Thanks again!

You can change your tensorrt to 8.4.3.1 to convert bevformer base on 3090 as mentioned in README.

[HerrYu123]

Hello,

I have got a error when I transport ONNX to TRT model by using sh samples/bevformer/tiny/onnx2trt.sh -d 0. The detailed log information is shown as following:

[02/25/2023-12:57:54] [TRT] [V] Loaded shared library libcublasLt.so.11 [02/25/2023-12:57:54] [TRT] [V] Using cublasLt as core library tactic source [02/25/2023-12:57:54] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +650, GPU +274, now: CPU 2219, GPU 2942 (MiB) [02/25/2023-12:57:54] [TRT] [V] Trying to load shared library libcudnn.so.8 [02/25/2023-12:57:54] [TRT] [V] Loaded shared library libcudnn.so.8 [02/25/2023-12:57:54] [TRT] [V] Using cuDNN as plugin tactic source [02/25/2023-12:57:55] [TRT] [V] Using cuDNN as core library tactic source [02/25/2023-12:57:55] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +178, GPU +266, now: CPU 2397, GPU 3208 (MiB) [02/25/2023-12:57:55] [TRT] [W] TensorRT was linked against cuDNN 8.6.0 but loaded cuDNN 8.1.1 [02/25/2023-12:57:55] [TRT] [I] Local timing cache in use. Profiling results in this builder pass will not be stored. [02/25/2023-12:57:55] [TRT] [V] Constructing optimization profile number 0 [1/1]. [02/25/2023-12:57:55] [TRT] [E] 1: [constantBuilder.cpp::addSupportedFormats::32] Error Code 1: Internal Error (Constant output type does not support bool datatype.) [02/25/2023-12:57:55] [TRT] [E] 2: [builder.cpp::buildSerializedNetwork::751] Error Code 2: Internal Error (Assertion engine != nullptr failed. ) Traceback (most recent call last): File "tools/bevformer/onnx2trt.py", line 262, in main() File "tools/bevformer/onnx2trt.py", line 257, in main calibrator=calibrator, File "/root/paddlejob/workspace/env_run/BEVFormer_Tensorrt/det2trt/convert/onnx2tensorrt.py", line 63, in build_engine engine = runtime.deserialize_cuda_engine(plan) TypeError: deserialize_cuda_engine(): incompatible function arguments. The following argument types are supported: 1. (self: tensorrt.tensorrt.Runtime, serialized_engine: buffer) -> tensorrt.tensorrt.ICudaEngine

Invoked with: <tensorrt.tensorrt.Runtime object at 0x7fae1710bd30>, None

Hi, guy. I met the same problem as you while using sh samples/bevformer/tiny/onnx2trt.sh -d 0. Could you please tell me how you change your environment? Thanks a lot.

[shaoqb]

same problem， did you solve it？