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X2Paddle
未验证 提交 b11fa47b 编写于 作者: J Jason 提交者： GitHub
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Merge pull request #175 from mamingjie-China/develop 

add op list
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op_list.md 0 → 100644
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# 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 |
x2paddle/op_mapper/tf_op_mapper.py
浏览文件 @ b11fa47b
...@@ -1116,40 +1116,6 @@ class TFOpMapper(OpMapper): ...@@ -1116,40 +1116,6 @@ class TFOpMapper(OpMapper):
output=node, output=node,
param_attr=attr) 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): def ResizeNearestNeighbor(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True) input = self.graph.get_node(node.layer.input[0], copy=True)
resize_shape = self.graph.get_node(node.layer.input[1], copy=True) resize_shape = self.graph.get_node(node.layer.input[1], copy=True)
...@@ -1195,37 +1161,6 @@ class TFOpMapper(OpMapper): ...@@ -1195,37 +1161,6 @@ class TFOpMapper(OpMapper):
output=node, output=node,
param_attr=None) 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): def RandomUniform(self, node):
shape = self.graph.get_node(node.layer.input[0], copy=True) shape = self.graph.get_node(node.layer.input[0], copy=True)
self.add_omit_nodes(shape.layer_name, node.layer_name) self.add_omit_nodes(shape.layer_name, node.layer_name)
......
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