add lrn

docs/introduction/op_list.md

@@ -42,7 +42,7 @@
 | 21 | Axpy      | 22 | ROIPolling   | 23 | Permute        | 24 | DetectionOutput      |
 | 25 | Normalize | 26 | Select       | 27 | ShuffleChannel | 28 | ConvolutionDepthwise |
 | 29 | ReLU      | 30 | AbsVal       | 31 | Sigmoid        | 32 | TanH                 |
-| 33 | ReLU6     | 34 | Upsample | |          |   |                   |
+| 33 | ReLU6     | 34 | Upsample     | 35 | MemoryData     |    |                      |
 
 ## ONNX
 
@@ -63,7 +63,7 @@
 | 49 | MaxPool  | 50 | Conv      | 51 | Gemm      | 52 | NonZero            |
 | 53 | Abs      | 54 | Floor     | 56 | ArgMax    | 57 | Sign               |
 | 58 | Reciprocal  | 59 | Size     | 60 | OneHot    | 61 | ReduceProd       |
-| 62 | LogSoftmax  | 63 | LSTM     |  |     |  |        |
+| 62 | LogSoftmax  | 63 | LSTM     | 64 |   LRN  |  |        |
 
 
 
@@ -99,7 +99,7 @@ Aten:
 | 101 | aten::upsample\_bilinear2d | 102 | aten::values |103|aten::view|104|aten::warn|
 | 105 | aten::where | 106 | aten::zeros |107|aten::zeros\_like|108|aten::bmm|
 | 109 | aten::sub\_ | 110 | aten:erf |111|aten::lstm|112|aten::gather|
-| 113 | aten::upsample\_nearest2d ||  |||||
+| 113 | aten::upsample\_nearest2d | 114 |aten::split\_with\_sizes  |||||
 
 Prim:
 | 序号 | OP | 序号 | OP | 序号 | OP | 序号 | OP |

docs/introduction/x2paddle_model_zoo.md

 # X2Paddle模型测试库
-> 目前X2Paddle支持70+的TensorFlow OP，40+的Caffe Layer，覆盖了大部分CV分类模型常用的操作。我们在如下模型列表中测试了X2Paddle的转换。
+> 目前X2Paddle支持80+的TensorFlow OP，30+的Caffe Layer，60+的ONNX OP，110+的PyTorch Aten，10+的PyTorch Prim，覆盖了大部分CV分类模型常用的操作。我们在如下模型列表中测试了X2Paddle的转换。
 
 **注：** 受限于不同框架的差异，部分模型可能会存在目前无法转换的情况，如TensorFlow中包含控制流的模型，NLP模型等。对于CV常见的模型，如若您发现无法转换或转换失败，存在较大diff等问题，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：）
 

x2paddle/convert.py

@@ -229,6 +229,11 @@ def main():
     assert args.paddle_type in ["dygraph", "static"], "--paddle_type must be 'dygraph' or 'static'"
 
     try:
+        import platform
+        v0, v1, v2 = platform.python_version().split('.')
+        if not(int(v0) >= 3 and int(v1) >= 5):
+            print("[ERROR] python>=3.5 is required")
+            return
         import paddle
         v0, v1, v2 = paddle.__version__.split('.')
         print("paddle.__version__ = {}".format(paddle.__version__))

x2paddle/decoder/tf_decoder.py

@@ -184,7 +184,7 @@ class TFGraph(Graph):
         node = super(TFGraph, self).get_node(new_node_name, copy)
         if node is None:
             return None
-        if node.layer_type == "Switch":
+        if node.layer_type not in ["Unpack", "Split"]:
             if hasattr(node, 'index'):
                 del node.index
         if len(items) == 1 and node.layer_type in self.multi_out_ops:

x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py

@@ -2013,3 +2013,25 @@ class OpSet9():
                     "k": val_k.name}, 
             outputs=["{}_p{}".format(node.layer_name, 0), "{}_p{}".format(node.layer_name, 1)],
             **layer_attrs)
+        
+    @print_mapping_info
+    def LRN(self, node):
+        op_name = name_generator("lrn", self.nn_name2id)
+        output_name = node.name
+        layer_outputs = [op_name, output_name]
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        alpha = node.get_attr('alpha', 0.0001)
+        beta = node.get_attr('beta', 0.75)
+        bias = node.get_attr('bias', 1.0)
+        size = node.get_attr('size')
+        layer_attrs = {
+            'size': size,
+            'alpha': alpha,
+            'beta': beta,
+            'k': bias
+        }
+        self.paddle_graph.add_layer(
+            "paddle.nn.LocalResponseNorm", 
+            inputs={"x": val_x.name}, 
+            outputs=layer_outputs, 
+            **layer_attrs)

x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py

@@ -1781,3 +1781,23 @@ class OpSet9():
                     "k": val_k.name}, 
             outputs=["{}_p{}".format(node.layer_name, 0), "{}_p{}".format(node.layer_name, 1)],
             **layer_attrs)
+        
+    @print_mapping_info
+    def LRN(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        alpha = node.get_attr('alpha', 0.0001)
+        beta = node.get_attr('beta', 0.75)
+        bias = node.get_attr('bias', 1.0)
+        size = node.get_attr('size')
+        layer_attrs = {
+            'size': size,
+            'alpha': alpha,
+            'beta': beta,
+            'k': bias
+        }
+        self.paddle_graph.add_layer(
+            'paddle.nn.functional.local_response_norm', 
+            inputs={"x": val_x.name}, 
+            outputs=[node.name], 
+            **layer_attrs)