# 算子支持列表
# 小程序AI推理负责以最优化的方式运行您的模型，并在可用时利用特定设备的硬件加速。此页面提供关于目前各设备支持哪些Op的信息。
注：
1.以下算子若无特殊备注说明，一般符合ONNX 算子定义，参考：https://github.com/onnx/onnx/blob/main/docs/Operators.md
2.目前GPU推理暂未对外开放，敬请期待。
| Operator | CPU | IOS NPU | IOS GPU | Android GPU | 备注 |
|---|---|---|---|---|---|
| Activation | ✔ | ✔ | ✔ | ✔ | 详细支持的Activation种类参考下方Activation列表 |
| ArgMax | ✔ | ✔ | ✔ | ||
| ArgMin | ✔ | ✔ | ✔ | ||
| BatchNorm | ✔ | ✔ | ✔ | ✔ | |
| Bias | ✔ | ✔ | ✔ | ✔ | |
| Binary | ✔ | ✔ | ✔ | ✔ | 详细支持的Binary操作种类参考下方Binary列表 |
| Bucketize | ✔ | ||||
| Cast | ✔ | ✔ | |||
| Concat | ✔ | ✔ | ✔ | ✔ | |
| Const | ✔ | ✔ | |||
| ConstOfShape | ✔ | ||||
| Conv1D | ✔ | ✔ | ✔ | ||
| Conv1DTranspose | ✔ | ✔ | |||
| Conv2D | ✔ | ✔ | ✔ | ✔ | |
| Conv2DTranspose | ✔ | ✔ | ✔ | ✔ | |
| Conv3D | ✔ | ||||
| Conv3DTranspose | ✔ | ||||
| Crop | ✔ | ||||
| CropAndResize | ✔ | ||||
| CumSum | ✔ | ||||
| DepthToSpace | ✔ | ✔ | |||
| Dropout | ✔ | ||||
| ElementWise | ✔ | ✔ | ✔ | ✔ | |
| Expand | ✔ | ✔ | |||
| FakeQuantize | ✔ | ||||
| Flatten | ✔ | ✔ | |||
| FullyConnected | ✔ | ✔ | ✔ | ✔ | |
| Gather | ✔ | ✔ | |||
| GatherND | ✔ | ||||
| Gemm | ✔ | ✔ | ✔ | ||
| GlobalPooling | ✔ | ✔ | ✔ | ✔ | |
| GroupNorm | ✔ | ||||
| Gru | ✔ | ||||
| InstanceNorm | ✔ | ✔ | ✔ | ||
| LayerNorm | ✔ | ||||
| LpNorm | ✔ | ||||
| Lrn | ✔ | ||||
| Lstm | ✔ | ||||
| MatMul | ✔ | ✔ | |||
| Normalize | ✔ | ✔ | ✔ | ||
| OneHot | ✔ | ✔ | |||
| Pad | ✔ | ✔ | ✔ | ||
| Permute | ✔ | ✔ | ✔ | ||
| Pooling1D | ✔ | ✔ | ✔ | ||
| Pooling2D | ✔ | ✔ | ✔ | ✔ | |
| Pooling3D | ✔ | ||||
| PriorBox | ✔ | ||||
| Range | ✔ | ||||
| Reduce | ✔ | ✔ | ✔ | ✔ | |
| Reshape | ✔ | ✔ | |||
| Resize2D | ✔ | ✔ | ✔ | ✔ | |
| Rnn | ✔ | ||||
| Scale | ✔ | ✔ | ✔ | ||
| ScatterND | ✔ | ✔ | |||
| Shape | ✔ | ||||
| ShuffleChannel | ✔ | ||||
| SpaceToDepth | ✔ | ✔ | |||
| Split | ✔ | ✔ | |||
| Slice | ✔ | ✔ | ✔ | ||
| Softmax | ✔ | ✔ | ✔ | ✔ | |
| Squeeze | ✔ | ✔ | ✔ | ✔ | |
| Tile | ✔ | ✔ | |||
| Unary | ✔ | ✔ | ✔ | ✔ | 详细支持的Unary种类参考下方Unary列表 |
| Unsqueeze | ✔ | ✔ | ✔ | ✔ | |
| Where | ✔ |
# Activation列表:
| 名称 | 描述 |
|---|---|
| None | f(x) = x |
| Abs | f(x) = [x] |
| Clip | f(x) = min(max(x, constA), constB) |
| HardSigmoid | f(x) = min(max(x * constA + constB, 0), 1) |
| HardSwish | f(x) = min(max(x * constA + constB, 0), 1) * x |
| HSigmoid | f(x) = (ReLU6(x + 3) / 6) |
| HSwish | f(x) = (ReLU6(x + 3) / 6) * x |
| LeakyReLU | f(x) = min(x, 0) * constA + max(x, 0) |
| Linear | f(x) = x * constA + constB |
| PReLU | f(x) = min(x, 0) * weight + max(x, 0) (Caffe1's) |
| ReLU | f(x) = max(x, 0) |
| ReLUN | f(x) = min(x, 0) * constA + min(max(x, 0), constB) |
| SELU | f(x) = (x >= 0 ? x : (exp(x)-1) * constA) * constB |
| Sigmoid | f(x) = 1 / (1 + exp(-x)), a.k.a. Logistic |
| SoftPlus | f(x) = log(1 + exp(x * constB)) * constA |
| SoftSign | f(x) = x / (1 + |x|) |
| Swish | f(x) = x / (1 + exp(-x * constA)) |
| Tanh | f(x) = tanh(x * constB) * constA |
| Threshold | f(x) = (x > constA ? 1 : 0) |
| ThrReLU | f(x) = (x > constA ? x : 0) (Thresholded ReLU) |
# Binary列表:
| 名称 | 描述 |
|---|---|
| Add | f(x, y) = x + y |
| Sub | f(x, y) = x - y |
| Mul | f(x, y) = x * y |
| Div | f(x, y) = x / y |
| Pow | f(x, y) = pow(x, y) |
| Max | f(x, y) = max(x, y) |
| Min | f(x, y) = min(x, y) |
| Mean | f(x, y) = (x + y) / 2 |
| And | f(x, y) = x & y |
| Or | f(x, y) = x | y |
| Xor | f(x, y) = x ^ y |
| BitShiftLeft | f(x, y) = x << y |
| BitShiftRight | f(x, y) = x >> y |
| Equal | f(x, y) = (x == y) |
| NotEqual | f(x, y) = (x != y) |
| Greater | f(x, y) = (x > y) |
| GreaterEqual | f(x, y) = (x >= y) |
| Less | f(x, y) = (x < y) |
| LessEqual | f(x, y) = (x <= y) |
# Unary列表:
| 名称 | 描述 |
|---|---|
| Abs | f(x) = [x] |
| Neg | f(x) = -x |
| Celi | f(x) = ceil(x) |
| Floor | f(x) = floor(x) |
| Reciprocal | f(x) = 1 / x |
| Sqrt | f(x) = sqrt(x) |
| Exp | f(x) = exp(x) |
| Log | f(x) = log(x) |
| Erf | f(x) = erf(x) |
| Acos | f(x) = acos(x) |
| Acosh | f(x) = acosh(x) |
| Cos | f(x) = cos(x) |
| Cosh | f(x) = cosh(x) |
| Sin | f(x) = sin(x) |
| Sinh | f(x) = sinh(x) |
| Atan | f(x) = atan(x) |
| Atanh | f(x) = atanh(x) |
| Tan | f(x) = tan(x) |
| Tanh | f(x) = tanh(x) |
| ExpM1 | f(x) = expm1(x) |
| Log1P | f(x) = log1p(x) |