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X2Paddle
未验证 提交 345d458a 编写于 作者: M mamingjie-China 提交者： GitHub
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op_list.md
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| 序号 | OP | 序号 | OP |序号 | OP |序号 | OP | | 序号 | OP | 序号 | OP |序号 | OP |序号 | OP |
|------|------|------|------|------|------|------|------| |------|------|------|------|------|------|------|------|
| 1 | Relu | 2 | Relu6 | 3 | Shape | 4 | Abs | | 1 | Relu | 2 | Relu6 | 3 | Shape | 4 | Abs |
|5 | Sigmoid | 6 | Exp | 7 | Rsqrt | 8 |swish_f32 | | 5 | Sigmoid | 6 | Exp | 7 | Rsqrt | 8 | swish_f32 |
|9 | Tanh | 10 | LeakyRelu | 11 | Add | 12 | RealDiv | | 9 | Tanh | 10 | LeakyRelu | 11 | Add | 12 | RealDiv |
| 13 | Sub | 14 | Maximum | 15 | Mul | 16 | FloorDiv | | 13 | Sub | 14 | Maximum | 15 | Mul | 16 | FloorDiv |
|17 | Placeholder | 18 | Const | 19 | Transpose | 20 |FusedBatchNorm| | 17 | Placeholder | 18 | Const | 19 | Transpose | 20 | FusedBatchNorm |
|21 | Conv2D | 22 | BiasAdd | 23 | MaxPool | 24 | DepthwiseConv2dNative | | 21 | Conv2D | 22 | BiasAdd | 23 | MaxPool | 24 | DepthwiseConv2dNative |
| 25 | Reshape | 26 | AvgPool | 27 | SplitV | 28 | SquaredDifference| | 25 | Reshape | 26 | AvgPool | 27 | SplitV | 28 | SquaredDifference |
|29 | Tile | 30 | Pack | 31 | Pad | 32 | ResizeBilinear| | 29 | Tile | 30 | Pack | 31 | Pad | 32 | ResizeBilinear |
|33 | Mean | 34 | MatMul | 35 | ArgMax | 36 | StridedSlice | | 33 | Mean | 34 | MatMul | 35 | ArgMax | 36 | StridedSlice |
| 37 | Slice | 38 | Sum | 39 | Max | 40 | Conv2DBackpropInput| | 37 | Slice | 38 | Sum | 39 | Max | 40 | Conv2DBackpropInput |
|41 | Cast | 42 | Split | 43| Squeeze | 44 | ResizeNearestNeighbor| | 41 | Cast | 42 | Split | 43 | Squeeze | 44 | ResizeNearestNeighbor |
|45 | Softmax | 46 | Range | 47 | ConcatV2 | | 45 | Softmax | 46 | Range | 47 | ConcatV2 |
## Caffe ## Caffe
| 序号 | OP | 序号 | OP |序号 | OP |序号 | OP | | 序号 | OP | 序号 | OP |序号 | OP |序号 | OP |
|------|------|------|------|------|------|------|------| |------|------|------|------|------|------|------|------|
| 1 | Input | 2 | Convolution| 3 |Deconvolution | 4 | Pooling | | 1 | Input | 2 | Convolution | 3 | Deconvolution | 4 | Pooling |
|5 |LRN |6 |InnerProduct| 7 | Softmax | 8 | Slice | | 5 | LRN | 6 | InnerProduct | 7 | Softmax | 8 | Slice |
|9 |Concat |10 |PReLU| 11 | Accuracy | 12 | Eltwise | | 9 | Concat | 10 | PReLU | 11 | Accuracy | 12 | Eltwise |
|13 |BatchNorm |14 |Scale| 15 |Reshape| 16 |ArgMax| | 13 | BatchNorm | 14 | Scale | 15 | Reshape | 16 | ArgMax |
|17 | Crop |18 | Flatten | 19 | Power | 20 | Reduction | | 17 | Crop | 18 | Flatten | 19 | Power | 20 | Reduction |
|21 | Axpy |22 | ROIPolling | 23 | Permute | 24 | DetectionOutput | | 21 | Axpy | 22 | ROIPolling | 23 | Permute | 24 | DetectionOutput |
|25 |Normalize|26 | Select| 27 | ShuffleChannel | 28 | ConvolutionDepthwise | | 25 | Normalize | 26 | Select | 27 | ShuffleChannel | 28 | ConvolutionDepthwise |
|29 |ReLU|30 |AbsVal| 31 | Sigmoid | 32 | TanH | | 29 | ReLU | 30 | AbsVal | 31 | Sigmoid | 32 | TanH |
## ONNX ## ONNX
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