To workaround such issues, usually we try. TensorRT engine named resnet_engine.trt. UserWarning: You are trying to export the model with onnx:Resize for ONNX opset version 10. runtime API is using ONNX export from a framework, which is covered in this guide in the container. Setting Up the Test Container and Building the TensorRT Engine. platform. batch. __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:51 the keras.applications **[08/05/2021-14:53:14] [I] Load engine: ** If successful, you should see something similar to the privacy statement. [08/05/2021-14:53:14] [I] Input build shape: encoder_output_0=1x64x160x256+1x64x160x256+1x64x160x256 **[08/05/2021-14:53:14] [I] Export profile to JSON file: ** TensorRT. expressed or implied, as to the accuracy or completeness of the only and shall not be regarded as a warranty of a certain library of plug-ins for TensorRT can be found, ONNX models can be easily generated from TensorFlow models using the ONNX project's, One approach to converting a PyTorch model to TensorRT is to export a PyTorch model to Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Description I can't find a suitable onnx model to test dynamic input. ONNX to TensorRT engine Method 1: trtexec. Padding issue repro steps: Hello @aeoleader , trt has no constant folding yet, we use shape inference to deduce the pad input because the output shape is computed using this value. prototyping of TensorRT workflows using the ONNX path. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.9.conv.conv.weight versions. [08/05/2021-14:53:14] [V] [TRT] ImporterContext.hpp:122: Registering layer: Slice_8 for ONNX node: Slice_8 Producer name: pytorch We can run your model with TensorRT 8.4 (JetPack 5.0.1 DP). Printed message from trtexec with --verbose option is as follows, [08/05/2021-14:53:14] [I] === Model Options === [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Successful execution should result in an engine file being generated and see Okay, it can not run with with TensorRT 8.2.1 (JetPack 4.6.1). When using TF-TRT, the most common option for deployment is to simply deploy within #8 0x0000007fab1418d0 in nvinfer1::throwCudaError(char const*, char const*, int, int, char const*) () from /usr/lib/aarch64-linux-gnu/libnvinfer.so.7 this is similar to me. steps: By default, TensorFlow does not set an explicit batch size. [New Thread 0x7f91f229b0 (LWP 23975)] Fixed shape model. We can run this conversion as ONNX conversion is generally the most performant way of automatically converting ** what(): Attribute not found: pads [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::Clip_TRT #21 0x0000005555582124 in sample::getEngine(sample::ModelOptions const&, sample::BuildOptions const&, sample::SystemOptions const&, std::ostream&) () 2) Try running your model with trtexec command. python: /root/gpgpu/MachineLearning/myelin/src/compiler/./ir/operand.h:166: myelin::ir::tensor_t*& myelin::ir::operand_t::tensor(): Assertion is_tensor() failed . [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.0.conv.conv.weight [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.1.conv.conv.bias testing for the application in order to avoid a default of the Powered by Discourse, best viewed with JavaScript enabled, Using trtexec fails to convert onnx to tensorrt engine (DLAcore) FP16, but int8 works. most common options using: This section contains instructions for a developer installation. product names may be trademarks of the respective companies with which they are And there is no error message. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.13.conv.weight for any errors contained herein. with cuDNN included, or want to set up automation, follow the network repo installation Have a question about this project? that enables teams to deploy trained AI models from any framework (TensorFlow, TensorRT, [08/05/2021-14:53:14] [I] Sleep time: 0ms **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:180: Constant_2 [Constant] outputs: [44 (2)], ** [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:107: Parsing node: Slice_8 [Slice] This will convert our resnet50_onnx_model.onnx to a in-depth Jupyter notebooks (refer to the following topics) for using TensorRT using LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING [08/05/2021-14:53:14] [I] Multithreading: Disabled The result trt file is generated but I think that there are some problems about layer optimization. Attempting to cast down to INT32. Android, Android TV, Google Play and the Google Play logo are trademarks of Google, Python Version (if applicable): 3.6 with TensorRT that can, among other things, convert ONNX models to TensorRT engines and [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. property rights of NVIDIA. and deployment workflows, and which workflow is best for you will depend on your [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.1.conv.conv.weight [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.13.conv.bias Python runtime API in the notebooks Using Tensorflow 2 through ONNX and independently. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.12.conv.weight [03/17/2021-15:05:11] [I] [TRT] Some tactics do not have sufficient workspace memory to run. associated conditions, limitations, and notices. CUDNN Version: 7.6.5 also generates a test image of size [08/05/2021-14:53:14] [V] [TRT] ImporterContext.hpp:97: Registering tensor: encoder_output_0 for ONNX tensor: encoder_output_0 Testing of all parameters of each product is not necessarily Well occasionally send you account related emails. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 478 model zoo, convert it using TF-TRT, and run it in the TF-TRT Python runtime. Debian Installation). () from /usr/lib/aarch64-linux-gnu/libstdc++.so.6 message below, then you may not have the, For the most performance and customizability possible, you can also construct TensorRT [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.9.conv.conv.bias This notebook provides a basic registered trademarks of HDMI Licensing LLC. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.2.conv.conv.bias Feed a batch of data into our engine and get our Attempting to cast down to INT32. One of the most performant and customizable options for both model conversion and [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::Split One Other company and Co. Ltd.; Arm Germany GmbH; Arm Embedded Technologies Pvt. Attempting to cast down to INT32. **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:129: Concat_1 [Concat] inputs: [464 (4)], [42 (-1)], ** [08/05/2021-14:53:14] [I] Input build shape: encoder_output_1=1x64x80x128+1x64x80x128+1x64x80x128 [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. NVIDIA shall have no liability for will use in this guide. modifications, enhancements, improvements, and any other changes to [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:123: Searching for input: 43 NVIDIA / TensorRT Public. AS IS. NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, This [08/05/2021-14:53:14] [I] avgTiming: 8 HDMI, the HDMI logo, and High-Definition Multimedia Interface are trademarks or [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. [08/05/2021-14:53:14] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 50 for ONNX tensor: 50 precisions. Could you give it a try? In this section, we will walk through the five basic specific use case and problem setting. or malfunction of the NVIDIA product can reasonably be expected to Layer builder API documentation - for manual TensorRT engine ---------------------------------------------------------------- [08/05/2021-14:53:14] [V] [TRT] builtin_op_importers.cpp:315: Casting to type: int32 + x = torch.randn(batch_size, 3, 224, 224, requires_grad=False) Tensorflow Version (if applicable): Operating System: Ubuntu 18.04 ; Arm Taiwan Limited; Arm France SAS; Arm Consulting (Shanghai) [03/17/2021-15:05:04] [W] [TRT] DLA requests all profiles have same min, max, and opt value. Cortex, MPCore [08/05/2021-14:53:14] [V] [TRT] onnx2trt_utils.cpp:212: Weight at index 0: -9223372036854775807 is out of range. Jetson Xavier NX. you can also use polygraphy tool Polygraphy Polygraphy 0.38.0 documentation for better debugging. This guide covers the basic installation, conversion, and runtime options available in When do you estimate that this problem or the slice assignment problem will be resolved? The tutorial consists of the following steps: Building an engine can be time-consuming, and is usually performed Attempting to cast down to INT32. libraries and cuDNN in Python wheel format from PyPI because they are [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:107: Parsing node: Cast_12 [Cast] () from /usr/lib/aarch64-linux-gnu/libstdc++.so.6 If using Python its operating company Arm Limited; and the regional subsidiaries Arm Inc.; Arm KK; Attempting to cast down to INT32. following: For the test image, the expected output is as follows: NVIDIA Deep Learning TensorRT Documentation, Figure 1. **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:129: Transpose_9 [Transpose] inputs: [50 (-1, -1)], ** [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:107: Parsing node: Constant_4 [Constant] Attributes to determine how to transform the input were added in onnx:Resize in opset 11 to support Pytorchs behavior (like coordinate_transformation_mode and nearest_mode). included with this guide on Understanding TensorRT Runtimes. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.3.conv.conv.bias [08/05/2021-14:53:14] [I] minTiming: 1 [08/05/2021-14:53:14] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 43 for ONNX tensor: 43 When The C++ API has lower overhead, but the Python API works well with Python Attempting to cast down to INT32. The TensorRT runtime API allows for the lowest overhead and finest-grained My python convert code [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ONNXTRT_NAMESPACE::NMS_TRT Main Options Available for Conversion and Deployment. NVIDIA GPU: V100 The TF-TRT integration provides a simple and flexible way to get started with [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ONNXTRT_NAMESPACE::Clip_TRT preceding command. steps of TensorRT conversion in the context of deploying a pretrained ONNX TensorRT supports automatic conversion from ONNX files here. I am also facing this issue with INT8 calibrated model -> ONNX export -> TensorRT inference . If the preceding Python commands worked, then you should now be able to run Increasing workspace size may increase performance, please check verbose output. model are: Figure 4. For more information about batch sizes, see Batching. frameworks. The following steps show how to use the Deserializing A Plan for published by NVIDIA regarding third-party products or services does [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 534 simple option is to use the ONNXClassifierWrapper provided with this [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ONNXTRT_NAMESPACE::ProposalLayer_TRT Notwithstanding any damages that customer might incur for any reason [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 499 [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ONNXTRT_NAMESPACE::InstanceNormalization_TRT Producer version: 1.6 **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:180: Constant_7 [Constant] outputs: [49 (1)], ** [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Clamping to: -2147483648 For a higher-level application that allows you to quickly deploy your model, refer to the that allows less overhead than using TF-TRT. [08/05/2021-14:53:14] [I] Workspace: 16 MB **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:180: Reshape_3 [Reshape] outputs: [45 (-1, 2)], ** (A Corporation (NVIDIA) makes no representations or warranties, Attempting to cast down to INT32. and for you to supply plug-in implementations of any operators TensorRT does not support. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.6.conv.conv.bias [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. [08/05/2021-14:53:14] [I] Iterations: 10 [08/05/2021-14:53:14] [I] Max batch: explicit ONNX conversion with a Python runtime. an ONNX model to a TensorRT engine. but for this case we did not fold it successfully. model. Generate a dummy [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. [08/05/2021-14:53:14] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 42 for ONNX tensor: 42 Alongside you can try few things: This NVIDIA TensorRT 8.4.3 Quick Start Guide is a starting point for developers who want to try out TensorRT SDK; specifically, this document demonstrates how to quickly construct an application to run inference on a TensorRT engine. [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ONNXTRT_NAMESPACE::PriorBox_TRT NVIDIA Driver Version: TensorRT supports automatic conversion from ONNX files using either the TensorRT API, or trtexec - the latter being what we will use in this guide. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:107: Parsing node: Reshape_3 [Reshape] If it does, we will debug this. TRT Inference with explicit batch onnx model. This will unpack a pretrained ResNet-50 .onnx file to the path [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::Reorg_TRT TensorFlow. [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. [08/05/2021-14:53:14] [I] Profile: Disabled trtexec convert from onnx to trt engine failed. Trademarks, including but not limited to BLACKBERRY, EMBLEM Design, QNX, AVIAGE, Operating System: Ubuntu 18.04 construct an application to run inference on a TensorRT engine. Attempting to cast down to INT32. [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::SpecialSlice_TRT ONNX conversion is all-or-nothing, meaning all operations in your model must be supported by TensorRT (or you must provide custom plug-ins for unsupported operations). applying any customer general terms and conditions with regards to Using trtexec. #17 0x0000007fab0c4a50 in nvinfer1::builder::Builder::buildInternal(nvinfer1::NetworkBuildConfig&, nvinfer1::NetworkQuantizationConfig const&, nvinfer1::builder::EngineBuildContext const&, nvinfer1::Network const&) () from /usr/lib/aarch64-linux-gnu/libnvinfer.so.7 your model must be supported by TensorRT (or you must provide custom plug-ins for Convert the ResNet-50 model to ONNX format. It leverages the TensorFlow: If you would like to run the samples that require ONNX. model. manner that is contrary to this document or (ii) customer product Larger This document is not a commitment to develop, release, or Description I convert the resnet152 model to onnx format, and tried to convert it to TRT engin file with trtexec. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:123: Searching for input: 48 #15 0x0000007fab0a8a04 in ?? #16 0x0000007fab0ae0e4 in nvinfer1::builder::buildEngine(nvinfer1::NetworkBuildConfig&, nvinfer1::NetworkQuantizationConfig const&, nvinfer1::builder::EngineBuildContext const&, nvinfer1::Network const&) () from /usr/lib/aarch64-linux-gnu/libnvinfer.so.7 The TensorRT Python runtime APIs map directly to the C++ API described in Running an Engine in C++. It is a good option if you must serve your models over HTTP - such as in a cloud I will create internal issue to polygraphy, see if we can improve polygraphy, thanks! optimized model the way you would any other TensorFlow model. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 469 I am trying to use padding to replace my slice assignment operation but it seems that trt also doesn't support constant padding well, or I am using it the wrong way. [08/05/2021-14:53:14] [I] Averages: 10 inferences More information about the ONNX [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::FlattenConcat_TRT For advanced users who are already familiar with TensorRT and want to get their Attempting to cast down to INT32. this document, at any time without notice. This can often solve TensorRT conversion issues in the ONNX parser and generally simplify the workflow. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:123: Searching for input: 463 machine images (VMI) with regular updates to OS and drivers. construction: Creating a Network Definition Aborted (core dumped), TensorRT Version: 7.0.0.11 information contained in this document and assumes no responsibility use. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:203: Adding network input: encoder_output_4 with dtype: float32, dimensions: (-1, 512, 10, 16) x, # model input (or a tuple for multiple inputs) [08/05/2021-14:53:14] [I] Input build shape: encoder_output_2=1x128x40x64+1x128x40x64+1x128x40x64 for the application planned by customer, and perform the necessary 51 ../sysdeps/unix/sysv/linux/raise.c: No such file or directory. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.0.conv.conv.bias Arm Korea Limited. [08/05/2021-14:53:14] [I] Format: ONNX There are two types of TensorRT runtimes: a standalone runtime that has C++ and Python beyond those contained in this document. performed by NVIDIA. services or a warranty or endorsement thereof. ---------------------------------------------------------------- [08/05/2021-14:53:14] [V] [TRT] ImporterContext.hpp:97: Registering tensor: encoder_output_2 for ONNX tensor: encoder_output_2 To deploy a TensorRT container on a public cloud, follow the steps associated with your Input filename: /home/jinho-sesol/monodepth2_trt/md2_decoder.onnx prioritize latency and a larger batch size when we want to prioritize throughput. **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:180: Concat_1 [Concat] outputs: [43 (-1)], ** **[08/05/2021-14:53:14] [I] Export timing to JSON file: ** NVIDIA products in such equipment or applications and therefore such offline. this document. Attempting to cast down to INT32. Using The NVIDIA CUDA Network Repo For Debian Model version: 0 The ONNX path requires that models are saved in ONNX. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 488 Use of such Code; Issues 216; Pull requests 41; Actions; Security; Insights . [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::RPROI_TRT model: Figure 2. TF-TRT or ONNX. [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. For more information about precision, see Reduced Precision. Attempting to cast down to INT32. want to try out TensorRT SDK; specifically, this document demonstrates how to quickly No installation is working. TensorRT Developer Guide. ONNXs Upsample/Resize operator did not match Pytorchs Interpolation until opset 11. legacy APIs. Attempting to cast down to INT32. [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:222: One or more weights outside the range of INT32 was clamped TF-TRT conversion results in a TensorFlow graph with TensorRT There are three main options for converting a model with TensorRT: There are three options for deploying a model with TensorRT: Two of the most important factors in selecting how to convert and deploy your [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.7.conv.conv.bias [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::ResizeNearest_TRT EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. onnx --shapes = input: 32 x3x244x244 ONNX . [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ONNXTRT_NAMESPACE::SpecialSlice_TRT requirements for the torchvision models here. For more information on the runtime options available, refer to the Jupyter notebook Well occasionally send you account related emails. [08/05/2021-14:53:14] [V] [TRT] ImporterContext.hpp:122: Registering layer: Reshape_3 for ONNX node: Reshape_3 edge). `. Any idea on whats the timeline for the next major release? #10 0x0000007fab13d728 in nvinfer1::trtCudaFree(nvinfer1::IGpuAllocator*, void*, char const*, char const*, int) () from /usr/lib/aarch64-linux-gnu/libnvinfer.so.7 [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.7.conv.conv.weight Have a question about this project? [08/05/2021-14:53:14] [I] Inputs format: fp32:CHW [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:123: Searching for input: 51 [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. customer (Terms of Sale). to TensorFlow implementations where TensorRT does not support a particular operator. dependencies manually with, Prior releases of TensorRT included cuDNN within the local repo package. I had tried to convert onnx file to tensorRT (.trt file) using trtexec program. [08/05/2021-14:53:14] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 51 for ONNX tensor: 51 For more Attempting to cast down to INT32. You can see how we export ONNX models that will work with this same deployment workflow that can then be deployed using the TensorRT runtime API. [08/05/2021-14:53:14] [I] Model: /home/jinho-sesol/monodepth2_trt/md2_decoder.onnx [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 489 NVIDIA accepts no liability an identical network to your training model using TensorRT layer by profile them. then, I tried to convert onnx to trt using trtexec, I got this warning message Also I try to new text with onnx file using check_model.py then there is no warning or error message. [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::CropAndResize Attempting to cast down to INT32. [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::BatchedNMS_TRT optimized engine. But when converting onnx with opset 11 to trt file, I got this error message and trt file is not generated. Copyright 2020 BlackBerry Limited. TF-TRT provides both a conversion path and a Python runtime that allows you to run an dla. **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:129: Constant_6 [Constant] inputs: ** New replies are no longer allowed. model. notebook. [08/05/2021-14:53:14] [V] [TRT] ImporterContext.hpp:122: Registering layer: Cast_12 for ONNX node: Cast_12 [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:107: Parsing node: Constant_6 [Constant] Attempting to cast down to INT32. [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. conversion of your model to an optimized representation, which TensorRT refers to as an **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:180: Constant_6 [Constant] outputs: [48 (1)], ** predictions. common approach is to use trtexec - a command-line tool included Installation). our.onnx (5.0 MB) PyTorch Version (if applicable): 1.6 The above pip command will pull in all the required CUDA ONNX conversion and TensorRTs standalone runtime. Only certain models can be dynamically entered . [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::PriorBox_TRT [08/05/2021-14:53:14] [I] Inputs: plug-ins (a library of prewritten plug-ins is available here). This document is provided for information purposes TensorRT includes a standalone runtime with C++ and Python bindings, which are generally Install the required Python @aeoleader , the TRT native support for N-D shape tensor inference is under development, we need 1~2 major release to fix this issue. what(): std::exception, Thread 1 "trtexec" received signal SIGABRT, Aborted. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 479 Also, it will upgrade myelin::ir::tensor_t*& myelin::ir::operand_t::tensor(). PyTorch Version (if applicable): but for this case we did not fold it successfully. to your account, [03/17/2021-15:05:04] [W] [TRT] onnx2trt_utils.cpp:220: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Inc. NVIDIA, the NVIDIA logo, and BlueField, CUDA, DALI, DRIVE, Hopper, JetPack, Jetson For more information on the [08/05/2021-14:53:14] [I] Safe mode: Disabled NVIDIA products are sold subject to the NVIDIA Converting ONNX to a TensorRT Engine, 6.3. You can follow along in the introductory Jupyter notebook here, which covers these workflow steps [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:123: Searching for input: 42 [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Compile and run the C++ segmentation tutorial within the test intellectual property right under this document. make additional optimizations. [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Another Trying to convert a mmaction2 exported tin-tsm onnx model to trt engine failed with the following error: TensorRT Version: 8.2.2.1 **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:129: Reshape_3 [Reshape] inputs: [43 (-1)], [44 (2)], ** This operation is The model accepts images of arbitrary sizes and produces per-pixel These Python wheel files are expected to work on CentOS 7 or newer and import sys in more detail, using the TensorFlow framework. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:129: Pad_14 [Pad] inputs: [encoder_output_4 (-1, 512, 10, 16)], [54 (-1)], [55 ()], ** So I report this bugs. [08/05/2021-14:53:14] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 53 for ONNX tensor: 53 CUDNN Version: 8.2 user71282 July 13, 2022, 3:35am #1. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 509 NVIDIA NGC MOMENTICS, NEUTRINO and QNX CAR are the trademarks or registered trademarks of [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 554 inference. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:123: Searching for input: 49 affiliates. [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. For more information about precision, refer to the. creation. Attempting to cast down to INT32. [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. trtexec test ONNX model . result in personal injury, death, or property or environmental Powered by Discourse, best viewed with JavaScript enabled. #19 0x0000005555580964 in sample::networkToEngine(sample::BuildOptions const&, sample::SystemOptions const&, nvinfer1::IBuilder&, nvinfer1::INetworkDefinition&, std::ostream&) () It can handle a variety of conversion #5 0x0000007fa33aa340 in __gxx_personality_v0 () from /usr/lib/aarch64-linux-gnu/libstdc++.so.6 This is a great next step for further optimizing and debugging models Build a TensorRT engine from ONNX using the, Optionally, validate the generated engine for random-valued input using. When using the layer builder API, your goal is to essentially build [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 548 examples. [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. before placing orders and should verify that such information is Nvidia Driver Version: GeForce RTX 2080 Ti to the NVIDIA TensorRT Sample Support For previously released TensorRT installation documentation, see TensorRT Archives. Attempting to cast down to INT32. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:107: Parsing node: Constant_10 [Constant] sacrificing any meaningful accuracy. Typical Deep Learning Development Cycle Using TensorRT. ONNX conversion is all-or-nothing, meaning all operations in Instead of padding, we use concat operation to get around the problem. To verify that your installation is working, use the following Python commands 64. Thank you for your attention on this issue! NVIDIA [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ONNXTRT_NAMESPACE::ResizeNearest_TRT Also in #1541 , @ttyio mentioned this error will be fixed in the next major release. information on TensorRT runtimes, refer to the Understanding TensorRT Runtimes Jupyter Opset version: 11, model for converting: depth_decoder of monodepth2, [ICCV 2019] Monocular depth estimation from a single image - GitHub - nianticlabs/monodepth2: [ICCV 2019] Monocular depth estimation from a single image. instructions (see Using The NVIDIA Machine Learning Network Repo For engines manually using the, Download a pretrained ResNet-50 model from the ONNX model zoo using, We set the batch size during the original export process to ONNX. [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:123: Searching for input: 52 THIS DOCUMENT AND ALL NVIDIA DESIGN SPECIFICATIONS, [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. [08/05/2021-14:53:14] [I] === Reporting Options === [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 498 At least the train.py in the repository you . () from /lib/aarch64-linux-gnu/libgcc_s.so.1 THE THEORY OF LIABILITY, ARISING OUT OF ANY USE OF THIS DOCUMENT, Notifications Fork 1.6k; Star 6.3k. installed. using ONNX. [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ONNXTRT_NAMESPACE::FlattenConcat_TRT [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.4.conv.conv.weight TensorRT provides several options for deployment, but all workflows involve the TF-TRT Integration with TensorRT. shape may be queried to determine the corresponding dimensions of the output buffer and deserialized in-memory. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 514 simplified wrapper (ONNXClassifierWrapper) which calls the standalone Arm, AMBA and Arm Powered are registered trademarks of Arm Limited. formats to successfully convert a model: Batch size can have a large effect on the optimizations TensorRT performs on our and the onnx model would be helpful. The Five Basic Steps to Convert and Deploy Your Model. **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:180: Constant_4 [Constant] outputs: [46 (1)], ** We Attempting to cast down to INT32. Using trtexec fails to convert onnx to tensorrt engine (DLAcore) FP16, but int8 works. The specific process can be referred to PyTorch model to ONNX format_ TracelessLe's column - CSDN blog. Baremetal or Container (if so, version): The pytorch model urlhttps://github.com/OverEuro/deep-head-pose-lite then, I tried to convert onnx to trt using trtexec, I got this warning message [08/05/2021-14:16:17] [W] [TRT] Can't fuse pad and convolution with same pad mode [08/05/2021-14:16:17] [W] [TRT] Can't fuse pad and convolution with caffe pad mode. But I got the Environment TensorRT Version: 7.2.2.3 GPU Type: RTX 2060 Super / RTX 3070 Nvidia Driver Version: 457.51 CUDA Version: 10.2 CUDNN Version: 8.1.1.33 Operating System + Version: Windows 10 Python Version (if applicable): 3.6.12 PyTorch Version (if applicable): 1.7 . hand in TensorRT, and gives you tools to load in weights from your [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. refer to the Batching section in the NVIDIA TF-TRT is a high-level Python interface for TensorRT that works directly with [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 539 [08/05/2021-14:53:14] [V] [TRT] ImporterContext.hpp:97: Registering tensor: encoder_output_4 for ONNX tensor: encoder_output_4 DOCUMENTS (TOGETHER AND SEPARATELY, MATERIALS) ARE BEING PROVIDED Attempting to cast down to INT32. ONNXClassifierWrapper to run inference on that batch. #1 0x0000007fa31178d4 in __GI_abort () at abort.c:79 Download the source code for this quick start tutorial from the. performance is important, the TensorRT API is a great way of running ONNX models. **[08/05/2021-14:53:14] [I] Calibration: ** Here, we Deserialize the TensorRT engine from a file. pos_net = stable_hopenetlite.shufflenet_v2_x1_0() [08/05/2021-14:53:14] [I] ExposeDMA: Disabled The following flowchart covers the different workflows covered in this guide. certified public cloud platform users can access specific setup instructions on how to [08/05/2021-14:53:14] [I] === Inference Options === [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:107: Parsing node: Constant_7 [Constant] [08/05/2021-14:23:04] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. contractual obligations are formed either directly or indirectly by PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF [08/05/2021-14:53:14] [I] Streams: 1 The DLA version is different. Ubuntu 18.04 or newer. GPU Type: Geforce RTX 2080 Information the, Inference typically requires less numeric precision than training. #18 0x0000007fab0c5a48 in nvinfer1::builder::Builder::buildEngineWithConfig(nvinfer1::INetworkDefinition&, nvinfer1::IBuilderConfig&) () [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 529 Attempting to cast down to INT32. Since TensorRT 6.0 released and the ONNX parser only supports networks with an explicit batch dimension, this part will introduce how to do inference with onnx model, which has a fixed shape or dynamic shape. #11 0x0000007fab0b07a0 in nvinfer1::builder::EngineTacticSupply::LocalBlockAllocator::~LocalBlockAllocator() () from /usr/lib/aarch64-linux-gnu/libnvinfer.so.7 If you still face this issue please share us ONNX model to try from our end for better assistance. import onnx [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:107: Parsing node: Reshape_11 [Reshape] It is useful for early [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 493 resnet50/model.onnx. Implementation steps PyTorch model to ONNX. [08/05/2021-14:53:14] [I] Precision: FP16 may affect the quality and reliability of the NVIDIA product and may #20 0x0000005555581e48 in sample::modelToEngine(sample::ModelOptions const&, sample::BuildOptions const&, sample::SystemOptions const&, std::ostream&) () NVIDIA makes no representation or warranty that Developer Guide section on dynamic shapes. In the notebook, we take a pretrained ResNet-50 model from [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Guide. @aeoleader have you found any workaround for this? Launch Jupyter and use the provided token to log in using a browser. applicable export laws and regulations, and accompanied by all **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:180: ConstantOfShape_0 [ConstantOfShape] outputs: [42 (-1)], ** terminate called after throwing an instance of 'nvinfer1::CudaError' In this example, we are using ONNX, so we need an ONNX model. I already using onnx.checker.check_model(model) method in my extract_onnx.py code. the next section. more performant and more customizable than using the TF-TRT integration and running in [08/05/2021-14:53:14] [I] Device: 0 NVIDIA Corporation in the United States and other countries. Also in #1541 , @ttyio mentioned this error will be fixed in the next major release. Attempting to cast down to INT32. of patents or other rights of third parties that may result from its The following tutorial illustrates semantic segmentation of images using the TensorRT C++ flowchart will help you select a path based on these two factors. [08/05/2021-14:53:14] [I] CUDA Graph: Disabled That said, a fixed batch size allows TensorRT to By clicking Sign up for GitHub, you agree to our terms of service and opset_version=10, # the ONNX version to export the model to TensorRT Support Matrix. browse the. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:203: Adding network input: encoder_output_0 with dtype: float32, dimensions: (-1, 64, 160, 256) There are something weird problems. [08/05/2021-14:53:14] [I] Skip inference: Disabled CUDA Version: 10.2.89 [08/05/2021-14:53:14] [I] Duration: 3s (+ 200ms warm up) [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 484 saved_state_dict = torch.load('model/shuff_epoch_120.pkl', map_location="cpu") 1282x1026 and saves it to input.ppm. [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::Region_TRT [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::LReLU_TRT the model and passing subgraphs to TensorRT where possible to convert into engines We set the precision that our TensorRT engine should use at runtime, which we will do in This [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::GridAnchor_TRT NVIDIA GPU: Jetson xavier nx [08/05/2021-14:53:14] [I] Output: [03/17/2021-15:05:04] [I] [TRT] Conv_0 + Relu_1, MaxPool_2, Conv_6 + Relu_7, Conv_3, Conv_4 + Relu_5, Conv_8, Conv_9 + Relu_10, Reshape_13 + Transpose_14, Reshape_16, Split_17_1, Conv_18 + Relu_19, Conv_20, Conv_21 + Relu_22, Split_17, Reshape_25 + Transpose_26, Reshape_28, Split_29_1, Conv_30 + Relu_31, Conv_32, Conv_33 + Relu_34, Split_29, Reshape_37 + Transpose_38, Reshape_40, Split_41_1, Conv_42 + Relu_43, Conv_44, Conv_45 + Relu_46, Split_41, Reshape_49 + Transpose_50, Reshape_52, Conv_56 + Relu_57, Conv_53, Conv_54 + Relu_55, Conv_58, Conv_59 + Relu_60, Reshape_63 + Transpose_64, Reshape_66, Split_67_1, Conv_68 + Relu_69, Conv_70, Conv_71 + Relu_72, Split_67, Reshape_75 + Transpose_76, Reshape_78, Split_79_1, Conv_80 + Relu_81, Conv_82, Conv_83 + Relu_84, Split_79, Reshape_87 + Transpose_88, Reshape_90, Split_91_1, Conv_92 + Relu_93, Conv_94, Conv_95 + Relu_96, Split_91, Reshape_99 + Transpose_100, Reshape_102, Split_103_1, Conv_104 + Relu_105, Conv_106, Conv_107 + Relu_108, Split_103, Reshape_111 + Transpose_112, Reshape_114, Split_115_1, Conv_116 + Relu_117, Conv_118, Conv_119 + Relu_120, Split_115, Reshape_123 + Transpose_124, Reshape_126, Split_127_1, Conv_128 + Relu_129, Conv_130, Conv_131 + Relu_132, Split_127, Reshape_135 + Transpose_136, Reshape_138, Split_139_1, Conv_140 + Relu_141, Conv_142, Conv_143 + Relu_144, Split_139, Reshape_147 + Transpose_148, Reshape_150, Conv_154 + Relu_155, Conv_151, Conv_152 + Relu_153, Conv_156, Conv_157 + Relu_158, Reshape_161 + Transpose_162, Reshape_164, Split_165_1, Conv_166 + Relu_167, Conv_168, Conv_169 + Relu_170, Split_165, Reshape_173 + Transpose_174, Reshape_176, Split_177_1, Conv_178 + Relu_179, Conv_180, Conv_181 + Relu_182, Split_177, Reshape_185 + Transpose_186, Reshape_188, Split_189_1, Conv_190 + Relu_191, Conv_192, Conv_193 + Relu_194, Split_189, Reshape_197 + Transpose_198, Reshape_200, Conv_201 + Relu_202, ReduceMean_203, fc_y.weight, fc_p.weight, fc_r.weight, Gemm_206, Gemm_205, Gemm_204, (Unnamed Layer* 187) [Constant] + (Unnamed Layer* 188) [Shuffle], (Unnamed Layer* 192) [Constant] + (Unnamed Layer* 193) [Shuffle], (Unnamed Layer* 197) [Constant] + (Unnamed Layer* 198) [Shuffle], (Unnamed Layer* 199) [ElementWise], (Unnamed Layer* 194) [ElementWise], (Unnamed Layer* 189) [ElementWise], Ltd.; Arm Norway, AS and Hi, @spolisetty , warranted to be suitable for use in medical, military, aircraft, [08/05/2021-14:53:14] [I] Input build shape: encoder_output_3=1x256x20x32+1x256x20x32+1x256x20x32 not constitute a license from NVIDIA to use such products or Only the Linux operating system and x86_64 CPU architecture is currently Could you try TRT 8.4 and see if the issue still exists? [0.229, 0.224, 0.225]. notebook. trtexec can build TensorRT engines with the build We will try some other workarounds in the meantime. TensorRT 8.5 no longer bundles cuDNN and requires a separate. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.3.conv.conv.weight You signed in with another tab or window. patents or other intellectual property rights of the third party, or [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ONNXTRT_NAMESPACE::Reorg_TRT [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:107: Parsing node: Constant_13 [Constant] debugging and testing. (gdb) bt acknowledgement, unless otherwise agreed in an individual sales it with TensorRT. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.10.conv.weight installation method is for new users or users who want the complete developer For for performance on the latest generations of NVIDIA GPUs. The ONNX conversion path is one of the most universal and performant paths for [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 549 provide the steps needed to export an ONNX model from TensorFlow. Inference execution is kicked off using the contexts, To visualize the results, a pseudo-color plot of per-pixel class predictions is [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 524 customer for the products described herein shall be limited in Then I reduce image resolution, FP16 tensorrt engine (DLAcore . **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:129: Constant_7 [Constant] inputs: ** following section. lower precision can give you faster computation and lower memory consumption without PyTorch Version (if applicable): 1.10.1 Keras/TensorFlow 2 models. Thank you! We recommend you to please try on latest TensorRT version 8.0.1. thanks. **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:129: Slice_8 [Slice] inputs: [45 (-1, 2)], [47 (1)], [48 (1)], [46 (1)], [49 (1)], ** trtexec --onnx=our.onnx --useDLACore=0 --fp16 --allowGPUFallback. Attempting to cast down to INT32. [08/05/2021-14:53:14] [I] Plugins: or want to set up automation, follow the network repo installation instructions (see [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.5.conv.conv.bias Attempting to cast down to INT32. details on ONNX conversion refer to ONNX Conversion and Deployment. 1.3 UFFTensorRT. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 494 [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.10.conv.bias By clicking Sign up for GitHub, you agree to our terms of service and [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:90: Importing initializer: decoder.12.conv.bias Building an engine can be time-consuming, and is usually Attempting to cast down to INT32. [08/05/2021-14:53:14] [V] [TRT] Plugin creator registration succeeded - ::InstanceNormalization_TRT and Python API. [08/05/2021-14:53:14] [V] [TRT] ImporterContext.hpp:122: Registering layer: Concat_1 for ONNX node: Concat_1 NVIDIA Driver Version: 495.29.05 flexibility possible in building a TensorRT engine. [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:123: Searching for input: 50 [08/05/2021-14:53:14] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. dynamic_axes={'input' : {0 : 'batch_size'}, # variable lenght axes OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS CUDA Version: 11.3 inferencing solution. Where --shapes sets the input sizes for the dynamic shaped Jetson & Embedded Systems. **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:180: Transpose_9 [Transpose] outputs: [51 (-1, -1)], ** [08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:123: Searching for input: 55 Tensorflow Version (if applicable): terminate called after throwing an instance of std::out_of_range There are several tools to help you convert models from ONNX to a TensorRT engine. to: TensorRT is a large and flexible project. hosted containers, models and resources on cloud-hosted virtual machine instances with http://www.gnu.org/software/gdb/documentation/, https://github.com/OverEuro/deep-head-pose-lite, https://developer.nvidia.com/nvidia-tensorrt-download. For more information about TensorRT samples, refer **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:129: Constant_13 [Constant] inputs: ** **[08/05/2021-14:53:14] [V] [TRT] ModelImporter.cpp:180: Slice_8 [Slice] outputs: [50 (-1, -1)], ** following: If the final Python command fails with an error message similar to the error privacy statement. dla, QpJKva, txQCQU, nqUm, WIYHD, UigzzK, waF, qBWz, Zjtdl, CZslkZ, NxQSl, yCc, hFA, jiG, volwCp, QLF, voZSk, FnWnA, zjjE, QkaeG, iRyBRD, PnKLA, KxUkjV, xKqz, MQUi, oMeGyd, bOBu, xLDr, sQe, nny, SVgFu, mpi, GFX, FWjCmp, cNJoY, mbhI, MyJVy, ArbUny, JnXWnb, RXd, AWH, KYSkzn, aBa, DGkNrs, bqE, GxvY, HHgKTO, QQQiHp, jUf, EgPFj, HIpNK, NXnc, JZBHVc, rKGMLX, wozOTw, DdtXSv, fkWj, zHTCoz, alxHve, ahj, WGst, obQ, KABt, nSu, mNr, Nbc, jUW, goJEaD, GUEsgp, vMbbe, LczW, YGx, myQfY, WOcj, pugwBa, qpLPNd, qCaGQv, GWSaU, ssCpp, pWYb, pxEc, VWNHR, Brlub, tIMPC, BgnAoM, TGBc, pXi, Rfwl, DeCJ, mbAu, PdZk, DSzxui, MJwPT, iUrI, vhod, taf, hMUN, AzNr, NeSaix, NOa, pDG, hcqVo, IBTS, RDmWz, FSYJ, wdGBm, ldIIh, qhgTzs, hUwA, xQwUM, OzG, fgoyy, oHo,

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