90f205e79c
### Description
QNN EP has 2 unit tests failing:
TEST_F(QnnHTPBackendTests, DISABLED_PadReflectMode)
TEST_F(QnnHTPBackendTests, DISABLED_Pad4dOutOfRangePadConstantValue)
For the first unit test, in QNN's master definition, it is stated that
when using MIRROR_REFLECT, the before and after pad amounts must not be
greater than shape(in[0])[i] - 1. Therefore, we need to change the pad
amount from {0,2,0,0} to {0,1,0,0}.
For second unit test, QNN does not have limitations stating that pad
constant should be smaller than input[0]. The reason that the test is
failing is because the unit test did not take the pad constant into
consideration when doing quantization.
### Motivation and Context
Fix the 2 unit tests mentioned in description.
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started & Resources
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General Information: onnxruntime.ai
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Usage documention and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
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