diff --git a/op_list.md b/op_list.md new file mode 100644 index 0000000000000000000000000000000000000000..7f12eb48923e64dd6205e98c30d1672492fa2000 --- /dev/null +++ b/op_list.md @@ -0,0 +1,52 @@ +# X2Paddle支持OP列表 +> 目前X2Paddle支持40+的TensorFlow OP，30+的Caffe Layer，覆盖了大部分CV分类模型常用的操作。我们在如下列表中给出了目前X2Paddle支持的全部OP。 + +**注：** 目前，部分OP暂未支持，如您在转换过程中出现OP不支持的情况，可自行添加或反馈给我们。欢迎通过[ISSUE反馈](https://github.com/PaddlePaddle/X2Paddle/issues/new)的方式告知我们(模型名，代码实现或模型获取方式)，我们会及时跟进：） + +## TensorFlow + +| 序号 | OP | 序号 | OP | 序号 | OP | 序号 | OP | +|------|------|------|------|------|------|------|------| +| 1 | Relu | 2 | Relu6 | 3 | Shape | 4 | Abs | +| 5 | Sigmoid | 6 | Exp | 7 | Rsqrt | 8 | swish_f32 | +| 9 | Tanh | 10 | LeakyRelu | 11 | Add | 12 | RealDiv | +| 13 | Sub | 14 | Maximum | 15 | Mul | 16 | FloorDiv | +| 17 | Placeholder | 18 | Const | 19 | Transpose | 20 | FusedBatchNorm | +| 21 | Conv2D | 22 | BiasAdd | 23 | MaxPool | 24 | DepthwiseConv2dNative | +| 25 | Reshape | 26 | AvgPool | 27 | SplitV | 28 | SquaredDifference | +| 29 | Tile | 30 | Pack | 31 | Pad | 32 | ResizeBilinear | +| 33 | Mean | 34 | MatMul | 35 | ArgMax | 36 | StridedSlice | +| 37 | Slice | 38 | Sum | 39 | Max | 40 | Conv2DBackpropInput | +| 41 | Cast | 42 | Split | 43 | Squeeze | 44 | ResizeNearestNeighbor | +| 45 | Softmax | 46 | Range | 47 | ConcatV2 | + +## Caffe + +| 序号 | OP | 序号 | OP | 序号 | OP | 序号 | OP | +|------|------|------|------|------|------|------|------| +| 1 | Input | 2 | Convolution | 3 | Deconvolution | 4 | Pooling | +| 5 | LRN | 6 | InnerProduct | 7 | Softmax | 8 | Slice | +| 9 | Concat | 10 | PReLU | 11 | Accuracy | 12 | Eltwise | +| 13 | BatchNorm | 14 | Scale | 15 | Reshape | 16 | ArgMax | +| 17 | Crop | 18 | Flatten | 19 | Power | 20 | Reduction | +| 21 | Axpy | 22 | ROIPolling | 23 | Permute | 24 | DetectionOutput | +| 25 | Normalize | 26 | Select | 27 | ShuffleChannel | 28 | ConvolutionDepthwise | +| 29 | ReLU | 30 | AbsVal | 31 | Sigmoid | 32 | TanH | + +## ONNX + +| 序号 | OP | 序号 | OP | 序号 | OP | 序号 | OP | +|------|------|------|------|------|------|------|------| +| 1 | Relu | 2 | LeakyRelu | 3 | Elu | 4 | ThresholdedRelu | +| 5 | Prelu | 6 | Tanh | 7 | Shrink | 8 | Sigmoid | +| 9 | Pow | 10 | Softplus | 11 | Softsign | 12 | HardSigmoid | +| 13 | Exp | 14 | Add | 15 | Div | 16 | Sub | +| 17 | Mul | 18 | Shape | 19 | Clip | 20 | AveragePool | +| 21 | Sqrt | 22 | ReduceSum | 23 | ReduceMin | 24 | ReduceMean | +| 25 | Constant | 26 | Pad | 27 | Unsqueeze | 28 | Resize | +| 29 | Upsample | 30 | Expand | 31 | Gather | 32 | Slice | +| 33 | Cast | 34 | Split | 35 | Reshape | 36 | ConstantOfShape | +| 37 | Ceil | 38 | Concat | 39 | Flatten | 40 | ConvTranspose | +| 41 | MatMul | 42 | Sum | 43 | Transpose | 44 | BatchNormalization | +| 45 | Squeeze | 46 | Equal | 47 | Identity | 48 | GlobalAveragePool | +| 49 | MaxPool | 50 | Conv | 51 | Gemm | diff --git a/x2paddle/op_mapper/tf_op_mapper.py b/x2paddle/op_mapper/tf_op_mapper.py index 4eb68166e964e4c5b944012486cc68857c631f22..bf2d5519ebd0a738b1680088e6b28a2470e366bc 100644 --- a/x2paddle/op_mapper/tf_op_mapper.py +++ b/x2paddle/op_mapper/tf_op_mapper.py @@ -1116,40 +1116,6 @@ class TFOpMapper(OpMapper): output=node, param_attr=attr) - def ResizeNearestNeighbor(self, node): - input = self.graph.get_node(node.layer.input[0], copy=True) - resize_shape = self.graph.get_node(node.layer.input[1], copy=True) - self.add_omit_nodes(resize_shape.layer_name, node.layer_name) - if resize_shape.layer_type == "Const": - resize_shape = resize_shape.value.tolist() - else: - resize_shape = self.decoder.infer_shape_tensor(resize_shape) - align_corners = node.get_attr("align_corners") - attr = {"align_corners": align_corners, "out_shape": resize_shape} - node.fluid_code.add_layer("resize_nearest", - inputs=input, - output=node, - param_attr=attr) - - def ResizeBilinear(self, node): - input = self.graph.get_node(node.layer.input[0], copy=True) - resize_shape = self.graph.get_node(node.layer.input[1], copy=True) - self.add_omit_nodes(resize_shape.layer_name, node.layer_name) - if resize_shape.layer_type == "Const": - resize_shape = resize_shape.value.tolist() - else: - resize_shape = self.decoder.infer_shape_tensor(resize_shape) - align_corners = node.get_attr("align_corners") - attr = { - "align_corners": align_corners, - "out_shape": resize_shape, - "align_mode": 1 - } - node.fluid_code.add_layer("resize_bilinear", - inputs=input, - output=node, - param_attr=attr) - def ResizeNearestNeighbor(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) resize_shape = self.graph.get_node(node.layer.input[1], copy=True) @@ -1195,37 +1161,6 @@ class TFOpMapper(OpMapper): output=node, param_attr=None) - def RandomUniform(self, node): - shape = self.graph.get_node(node.layer.input[0], copy=True) - self.add_omit_nodes(shape.layer_name, node.layer_name) - if shape.layer_type == "Const": - shape = shape.value.tolist() - else: - shape = self.decoder.infer_shape_tensor(shape) - if node.tf_data_format == "NHWC" and len(shape) == 4: - shape = [shape[i] for i in [0, 3, 1, 2]] - attr = {"shape": shape, "min": 0.0, "max": 0.9999} - if shape[0] < 0: - input = self.batch_node - node.fluid_code.add_layer("uniform_random_batch_size_like", - inputs=input, - output=node, - param_attr=attr) - else: - node.fluid_code.add_layer("uniform_random", - inputs=None, - output=node, - param_attr=attr) - - def GreaterEqual(self, node): - x = self.graph.get_node(node.layer.input[0], copy=True) - y = self.graph.get_node(node.layer.input[1], copy=True) - inputs = {"x": x, "y": y} - node.fluid_code.add_layer("greater_equal", - inputs=inputs, - output=node, - param_attr=None) - def RandomUniform(self, node): shape = self.graph.get_node(node.layer.input[0], copy=True) self.add_omit_nodes(shape.layer_name, node.layer_name)