add tanh and squareddifference in tensorflow_nhwc

x2paddle/op_mapper/tf_op_mapper_nhwc.py

@@ -41,6 +41,7 @@ class TFOpMapperNHWC(OpMapper):
         'Exp': ['exp'],
         'Rsqrt': ['rsqrt'],
         'swish_f32': ['swish'],
+        'Tanh': ['tanh'],
         'LeakyRelu': ['leaky_relu', {
             'alpha': 'alpha'
         }]
@@ -913,6 +914,12 @@ class TFOpMapperNHWC(OpMapper):
         self.add_omit_nodes(kernel.layer_name, node.layer_name)
         self.add_omit_nodes(out_shape.layer_name, node.layer_name)
 
+        if out_shape.layer_type == "Const":
+            out_shape = out_shape.value.tolist()
+        else:
+            out_shape = self.decoder.infer_shape_tensor(out_shape,
+                                                        node.out_shapes[0])
+
         in_shape = input.out_shapes[0]
         if in_shape.count(-1) > 2:
             in_shape = self.decoder.infer_tensor(input).shape
@@ -920,7 +927,7 @@ class TFOpMapperNHWC(OpMapper):
         if k_size.count(-1) > 2:
             k_size = self.decoder.infer_tensor(kernel).shape
 
-        pad_mode = node.get_attr("padding")
+        pad_mode = node.get_attr("padding").decode()
         strides = node.get_attr("strides")
         dilations = node.get_attr("dilations")
         data_format = node.get_attr("data_format").decode()
@@ -969,6 +976,24 @@ class TFOpMapperNHWC(OpMapper):
                                   output=node,
                                   param_attr=attr)
 
+        if pad_mode == "SAME":
+            if node.tf_data_format == "NHWC":
+                print(out_shape)
+                out_shape = [out_shape[i] for i in [0, 3, 1, 2]]
+                print(out_shape)
+            for i in range(4):
+                if out_shape[i] < 0:
+                    out_shape[i] = 999999
+            attr = {
+                "axes": [0, 1, 2, 3],
+                "starts": [0, 0, 0, 0],
+                "ends": out_shape
+            }
+            node.fluid_code.add_layer("slice",
+                                      inputs=node,
+                                      output=node,
+                                      param_attr=attr)
+
         if not channel_first:
             attr = {"perm": [0, 2, 3, 1]}
             node.fluid_code.add_layer("transpose",
@@ -1127,3 +1152,17 @@ class TFOpMapperNHWC(OpMapper):
                                       inputs=None,
                                       output=node,
                                       param_attr=attr)
+
+    def SquaredDifference(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("elementwise_sub",
+                                  inputs=inputs,
+                                  output=node,
+                                  param_attr=None)
+        inputs = {"x": node, "y": node}
+        node.fluid_code.add_layer("elementwise_mul",
+                                  inputs=inputs,
+                                  output=node,
+                                  param_attr=None)