Call for Contributions

ONNX is open source, and contributions from the community is welcome. Many tasks, (some are low-hanging fruits), are listed here. If you would like to contribute to the project, but don't know where to start, this list is worth to check.

Adding ONNX Backend Test Case

Ideally, we should have sample cases for every operator, which can generate backend test data and also serve as examples. However, many operators have no sample cases, and even not covered by ONNX backend test cases. So please contribute your useful cases to ONNX repository.

How to contribute ONNX backend test?

The operators which don't have examples:

• Abs
• Add
• And
• ArgMax
• ArgMin
• AveragePool
• BatchNormalization
• Cast
• Ceil
• Clip
• Concat
• Conv
• ConvTranspose
• DepthToSpace
• Div
• Dropout
• Elu
• Equal
• Exp
• Flatten
• Floor
• GRU
• Gather
• Gemm
• GlobalAveragePool
• GlobalLpPool
• GlobalMaxPool
• Greater
• HardSigmoid
• Hardmax
• InstanceNormalization
• LRN
• LSTM
• LeakyRelu
• Less
• Log
• LogSoftmax
• LpNormalization
• LpPool
• Max
• MaxPool
• MaxRoiPool
• Mean
• Min
• Mul
• Neg
• Not
• Or
• PRelu
• Pow
• RNN
• RandomNormal
• RandomNormalLike
• RandomUniform
• RandomUniformLike
• Reciprocal
• ReduceL1
• ReduceL2
• ReduceLogSum
• ReduceLogSumExp
• ReduceMax
• ReduceMean
• ReduceMin
• ReduceProd
• ReduceSum
• ReduceSumSquare
• Reshape
• Selu
• Sigmoid
• Softmax
• Softplus
• Softsign
• SpaceToDepth
• Split
• Sqrt
• Squeeze
• Sub
• Sum
• Tanh
• Tile
• TopK
• Transpose
• Unsqueeze
• Xor
• experimental ATen
• experimental Affine
• experimental ConstantFill
• experimental Crop
• experimental FC
• experimental GRUUnit
• experimental GivenTensorFill
• experimental Identity
• experimental If
• experimental ImageScaler
• experimental Loop
• experimental LoopIndexTensor
• experimental MeanVarianceNormalization
• experimental ParametricSoftplus
• experimental Scale
• experimental ScaledTanh
• experimental ThresholdedRelu
• experimental Upsample

Adding Export Support in PyTorch

Some simple operators are not supported by PyTorch exporter yet, call for contributions.

How to add ONNX support in PyTorch

How to generate ONNX backend test data in PyTorch operator export test

Here is a TO-DO list:

• Tile (Repeat in PyTorch)