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chan4cc commented Apr 26, 2021

New Operator

Describe the operator

Why is this operator necessary? What does it accomplish?

This is a frequently used operator in tensorflow/keras

Can this operator be constructed using existing onnx operators?

If so, why not add it as a function?

I don't know.

Is this operator used by any model currently? Which one?

Are you willing to contribute it?

onlyonedaniel commented Nov 18, 2020

Describe the bug
when axis has duplicate value , onnxruntime compute result is all same value ,which is different with expect of tensorflow


System information
Linux Ubuntu 16.04

  • ONNX Runtime installed from binary
  • ONNX Runtime version:1.4.0
  • Python version:3.5

Expected behavior
When there are duplicate values, the duplicate can be removed. j

Vico-Lu commented Oct 6, 2020

🐞Describe the bug

Converting Frontend cannot be 100% converted when pytorch model is converted to MLmodel. I get a warning


`TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

  • Updated Jun 16, 2021
  • Python

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.

  • Updated Jun 8, 2021
  • Jupyter Notebook
ansh1204 commented Apr 27, 2020

I am trying to convert a custom pytorch model to tensorflow, I am abe to convert pytorch to onnx but converting onnx to tensorflow gives issue.

The code snippets are as follows-

pytorch to onnx

net = custom pytorch model
net.load_state_dict("pre-trained model")
dummyInput = np.random.uniform(0,1,(1,8,3,256,256))
dummyInput = Variable(torch.FloatTensor(dummyInput))

A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .onnx]

  • Updated Jul 22, 2021
  • Python

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