Description
We observed a significant accuracy mismatch when converting an SegVit ONNX model to a TensorRT engine. The issue has been narrowed down using polygraphy debug reduce and appears to originate from normalization layers (InstanceNormalization / GroupNorm pattern).
The mismatch starts from very early layers in the model and propagates through the entire network, eventually causing large output deviations.
Environment
TensorRT version: 10.13.0.35
GPU: RTX 3080
CUDA version: 12.8
OS: Ubuntu 22.04
Steps To Reproduce
Run Polygraphy debug reduce:
polygraphy debug reduce vit_seg_simp.onnx --mode bisect --output reduced_model.onnx --check polygraphy run polygraphy_debug.onnx --onnxrt --trt
Observe accuracy mismatch between ONNX Runtime and TensorRT.
Thanks!
log.txt
onnx file
Description
We observed a significant accuracy mismatch when converting an SegVit ONNX model to a TensorRT engine. The issue has been narrowed down using polygraphy debug reduce and appears to originate from normalization layers (InstanceNormalization / GroupNorm pattern).
The mismatch starts from very early layers in the model and propagates through the entire network, eventually causing large output deviations.
Environment
TensorRT version: 10.13.0.35
GPU: RTX 3080
CUDA version: 12.8
OS: Ubuntu 22.04
Steps To Reproduce
Run Polygraphy debug reduce:
polygraphy debug reduce vit_seg_simp.onnx --mode bisect --output reduced_model.onnx --check polygraphy run polygraphy_debug.onnx --onnxrt --trtObserve accuracy mismatch between ONNX Runtime and TensorRT.
Thanks!
log.txt
onnx file