Merge pull request #176 from mamingjie-China/develop-1.6

add op list

op_list.md

+# 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 | * | 2 | *| 3 |* | 4 | * |

x2paddle/op_mapper/tf_op_mapper.py

@@ -1043,40 +1043,6 @@ 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)