Tracking why operators are not covered

ONNX backend test script reports the coverage on the operators and attributes. But we have various of reasons for the missing test coverage on operators. This doc keeps tracking why operators are not covered by the testcases.

• 💚 The ONNX operator can map to a Caffe2 operator.
• 💛 The solution is not perfect/finished, for example, the operator can map to a combination of Caffe2 operators.
• 💔 Hard to find a solution with existing Caffe2 operators.

Operator	Test Coverage	PyTorch	Caffe2
Abs	Yes	OK	💚OK
Add	Yes	OK	💚OK
And		Support int tensor, but no bool tensor	💚OK
ArgMax			💔No op
ArgMin			💔No op
AveragePool	Yes	OK	💚OK
BatchNormalization	Yes	OK	💚OK
Cast			💔No op
Ceil			💔No op
Clip	Yes	OK	💚OK
Concat	Yes	OK	💚OK
Constant	Yes	OK	💛Special handling
Conv	Yes	OK	💚OK
ConvTranspose			💚OK
DepthToSpace			💛Should be BatchToSpace, no tests
Div	Yes	OK	💚OK
Dropout	Yes	OK	💚OK
Elu	Yes	OK	💚OK
Equal	Yes	OK	💚OK
Exp	Yes	OK	💚OK
Flatten	Yes	OK	💚OK
Floor			💔No op
GRU			💛Under development
Gather	Yes	OK	💛C2 only support axis=0 or 1
Gemm	Yes	OK	💛C2 use FC or MatMul + Add
GlobalAveragePool	Yes	No direct mapping	💚OK
GlobalLpPool			💔No op
GlobalMaxPool			💚OK
Greater			💔Only support int tensor
HardSigmoid			💔No op
Hardmax			💔No op
InstanceNormalization			💚OK
LRN	Yes	OK	💚OK
LSTM			💛Under development
LeakyRelu	Yes	OK	💚OK
Less			💔Only support int tensor
Log	Yes	OK	💚OK
LogSoftmax		OK	💛No op, translated in onnx-caffe2
LpNormalization			💚Should be LpNorm, no tests
LpPool			💚Should be LpPool, no tests
MatMul	Yes	OK	💚OK
Max	Yes	OK	💚OK
MaxPool	Yes	OK	💚OK
MaxRoiPool			💔No op
Mean			💔No op
Min	Yes	OK	💚OK
Mul	Yes	OK	💚OK
Neg	Yes	OK	💚OK
Not			💚OK
Or			💚OK
PRelu	Yes	OK	💚OK
Pad	Yes	OK	💚OK
Pow		OK	💛Under development, C2 only accepts exponent as argument, not an input
RNN			💛Under development
RandomNormal			💔No op
RandomNormalLike			💔No op
RandomUniform			💔No op
RandomUniformLike			💔No op
Reciprocal			💛Use Pow to implement
ReduceL1			💔No op
ReduceL2			💔No op
ReduceLogSum			💔No op
ReduceLogSumExp			💔No op
ReduceMax			💔No op
ReduceMean			💔No op
ReduceMin			💔No op
ReduceProd			💔No op
ReduceSum			💔No op
ReduceSumSquare			💔No op
Relu	Yes	OK	💚OK
Reshape	Yes	OK	💚OK
Selu	Yes	OK	💚OK
Sigmoid	Yes	OK	💚OK
Slice	Yes	OK	💔ScatterAssign + Cast, very hacky implementaion, Slice in C2 only supports one dimension
Softmax	Yes	OK	💔Axis and dim has different semantics
Softplus	Yes	OK	💚OK
Softsign			💚OK, no tests
SpaceToDepth			💛Should be SpaceToBatch, no tests
Split	Yes	OK	💚OK
Sqrt			💛Use Pow to implement
Squeeze			💚OK, no tests
Sub		OK	💚OK
Sum	Yes	OK	💚OK
Tanh	Yes	OK	💚OK
Tile			💚OK, no tests
Transpose	Yes	OK	💚OK
Xor			💚OK
experimental ATen			💚OK
experimental Affine			💔No op
experimental ConstantFill			💚OK
experimental Crop			💔No op
experimental FC			💚OK
experimental GRUUnit			💚OK, no tests
experimental GivenTensorFill			💚OK
experimental Identity			💚OK
experimental ImageScaler			💔No op
experimental MeanVarianceNormalization			💔No op
experimental ParametricSoftplus			💔No op
experimental Scale			💚OK
experimental ScaledTanh			💔No op
experimental ThresholdedRelu			💔No op
experimental Upsample			💔No bilinear