Hi! I am looking for an example preferably in Python for deploying a custom ResNet model from Tensorflow to the Jetson Xavier. My model has already been converted to the .onnx format for the Xavier. I have seen the Jetson Inference API example in Python for loading a model using the ImageNet() comm…

Hi, Based on the log, it seems that the image size and the network input are not aligned. Please noted you will need to add a resizer if the image size doesn’t equals to network input (ex.224x224x3). Thanks.

Hi, The error comes from dynamic shape usage. If you don’t need to reshape the model at the runtime, please set EXPLICIT_BATCH to use static shape instead. Here is an example of onnx model for your reference: import cv2 import time import numpy as np import tensorrt as trt import pycuda.autoinit…

Thanks for the reply! When I use your Python program with my custom model I do see the TensorRT ERRORS: Network has dynamic or shape inputs, but no optimization profile has been defined, Network validation failed. I understand you’re saying this is because of “dynamic shape” but I’m not sure what t…

Hi, May I know how do you train the customized model? If it is PyTorch, a static model can be generated via setting dynamic_axes=None. You can find an example for dynamic vs. static onnx model below: Tianxiaomo/pytorch-YOLOv4/blob/master/tool/darknet2onnx.py#L20 m…

Thank you! The custom model is trained using TensorFlow 2.3.1. I am using TensorFlow’s ResNet50 with imagenet weights, image data with dimensions 224x224x3, and vary the batch-size between 1 and 32. The loss function is categorical_crossentropy. Here is a snippet of the model definition: baseMode…

Hi, For your use case, you can rewrite the batch size to the ONNX model directly. Please find the following comment for the instruction details: [image] Error when convert onnxt to tensorRT with batch size more than 1 DeepStream SDK Hi, Would you mind the check this scr…

Hi AastaLLL, Thanks for all of your help, you have been amazing! The script you mentioned using onnx_graphsurgeon seems to have worked without error. Since my model used a BS = 32 I used the onnx_graphsurgeon to rewrite the BS to the ONNX model directly. I have also confirmed my model exported pr…

Hi, Since your batch size is 32, please allocate the input and output buffer for the 32 batches. This can be done by updating the EXPLICIT_BATCH parameter directly: EXPLICIT_BATCH = 32 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) Please check the correct comment on Feb. 1. Thank…

Thank you, I tried modifying the batchsize to 32 but now there is another error show below in the image. Please let me know if you have any suggestions resolving this and I will continue to debug it from my end. [trt_builder_error_2021-01-07 17-51-18]

Hi, Sorry that the comment shared on Jan. 15 is incorrect. The parameter that needs to be updated is builder.max_batch_size rather than EXPLICIT_BATCH. EXPLICIT_BATCH is a parameter to specify either explicit batch or implicit batch. And the real batch number is defined in the max_batch_size. P…

Thanks, there was no inconvenience, although this [val_error_shape_2021-02-02 11-57-50] error still persists after the changes you mentioned.

Custom ResNet Jetson Xavier

Robotics & Edge Computing Jetson Systems Jetson Xavier NX

AastaLLL December 18, 2020, 6:27am 7

Hi,

For your use case, you can rewrite the batch size to the ONNX model directly.
Please find the following comment for the instruction details:

Thanks.

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Custom ResNet Jetson Xavier

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