add merge prelu

x2paddle/convert.py

@@ -107,6 +107,7 @@ def tf2paddle(model_path,
         optimizer.merge_activation()
         optimizer.merge_bias()
         optimizer.merge_batch_norm()
+        optimizer.merge_prelu()
     else:
         mapper = TFOpMapperNHWC(model)
         optimizer = TFOptimizer(mapper)

x2paddle/optimizer/tf_optimizer.py

@@ -16,6 +16,7 @@
 from x2paddle.op_mapper.tf_op_mapper import TFOpMapper
 from x2paddle.core.fluid_code import Layer
 from x2paddle.core.util import *
+import numpy
 import copy as cp
 
 
@@ -418,6 +419,40 @@ class TFOptimizer(object):
                     is_batch_norm = False
                     continue
 
+                if len(in_nodes0[0].outputs) != 1:
+                    is_batch_norm = False
+                    continue
+                if len(in_nodes0[1].outputs) != 1:
+                    is_batch_norm = False
+                    continue
+                if len(in_nodes1[1].outputs) != 2:
+                    is_batch_norm = False
+                    continue
+                if len(in_nodes2[0].outputs) != 1:
+                    is_batch_norm = False
+                    continue
+                if len(in_nodes2[1].outputs) != 1:
+                    is_batch_norm = False
+                    continue
+                if len(in_nodes3[0].outputs) != 1:
+                    is_batch_norm = False
+                    continue
+                if len(in_nodes3[1].outputs) != 1:
+                    is_batch_norm = False
+                    continue
+                if len(in_nodes4[0].outputs) != 1:
+                    is_batch_norm = False
+                    continue
+                if len(in_nodes5.outputs) != 1:
+                    is_batch_norm = False
+                    continue
+                if len(in_nodes6[0].outputs) != 1:
+                    is_batch_norm = False
+                    continue
+                if len(in_nodes6[1].outputs) != 1:
+                    is_batch_norm = False
+                    continue
+
                 conv_shape = in_nodes1[0].out_shapes[0]
                 if conv_shape[3] < 0:
                     is_batch_norm = False
@@ -466,8 +501,9 @@ class TFOptimizer(object):
                         "act": act
                     }
 
-                    node.fluid_code.add_layer("batch_norm",
-                                              inputs=cp.copy(in_nodes1[0]),
+                    node.fluid_code.add_layer(
+                        "batch_norm",
+                        inputs=in_nodes1[0].fluid_code.layers[-1].output,
                         output=node,
                         param_attr=attr)
 
@@ -478,3 +514,149 @@ class TFOptimizer(object):
                 del self.graph.node_map[in_nodes3[0].layer_name]
                 del self.graph.node_map[in_nodes4[0].layer_name]
                 del self.graph.node_map[in_nodes5.layer_name]
+
+    def merge_prelu(self):
+        for i, name in enumerate(self.graph.topo_sort):
+            node = self.graph.get_node(name)
+            if node is None:
+                continue
+            is_prelu = True
+            if node.layer_type == "Add":
+                in_nodes0 = [
+                    self.graph.get_node(in_name) for in_name in node.inputs
+                ]
+                if in_nodes0[0].layer_type != "Relu" or in_nodes0[
+                        1].layer_type != "Mul":
+                    is_prelu = False
+                    continue
+                if len(in_nodes0[0].outputs) != 1 or len(
+                        in_nodes0[1].outputs) != 1:
+                    is_prelu = False
+                    continue
+
+                in_nodes1 = self.graph.get_node(in_nodes0[0].inputs[0])
+                in_nodes2 = [
+                    self.graph.get_node(in_name)
+                    for in_name in in_nodes0[1].inputs
+                ]
+                if in_nodes2[1].layer_type != "Const" or numpy.fabs(
+                        in_nodes2[1].value - 0.5) > 1e-06:
+                    is_prelu = False
+                    continue
+                if in_nodes2[0].layer_type != "Mul":
+                    is_prelu = False
+                    continue
+                if len(in_nodes2[1].outputs) != 1 or len(
+                        in_nodes2[0].outputs) != 1:
+                    is_prelu = False
+                    continue
+
+                in_nodes3 = [
+                    self.graph.get_node(in_name)
+                    for in_name in in_nodes2[0].inputs
+                ]
+                if in_nodes3[0].layer_type != "Const" or in_nodes3[
+                        1].layer_type != "Sub":
+                    is_prelu = False
+                    continue
+                if len(in_nodes3[0].outputs) != 1 or len(
+                        in_nodes3[1].outputs) != 1:
+                    is_prelu = False
+                    continue
+
+                in_nodes4 = [
+                    self.graph.get_node(in_name)
+                    for in_name in in_nodes3[1].inputs
+                ]
+                if in_nodes4[0].layer_name != in_nodes1.layer_name or in_nodes4[
+                        1].layer_type != "Abs":
+                    is_prelu = False
+                    continue
+                if len(in_nodes4[1].outputs) != 1:
+                    is_prelu = False
+                    continue
+
+                in_nodes5 = self.graph.get_node(in_nodes4[1].inputs[0])
+                if in_nodes5.layer_name != in_nodes1.layer_name:
+                    is_prelu = False
+                    continue
+
+                if len(in_nodes0[0].outputs) != 1:
+                    is_prelu = false
+                    continue
+                if len(in_nodes0[1].outputs) != 1:
+                    is_prelu = False
+                    continue
+                if len(in_nodes1.outputs) < 3:
+                    is_prelu = False
+                    continue
+                if len(in_nodes2[0].outputs) != 1:
+                    is_prelu = false
+                    continue
+                if len(in_nodes2[1].outputs) != 1:
+                    is_prelu = False
+                    continue
+                if len(in_nodes3[0].outputs) != 1:
+                    is_prelu = False
+                    continue
+                if len(in_nodes3[1].outputs) != 1:
+                    is_prelu = false
+                    continue
+                if len(in_nodes4[1].outputs) != 1:
+                    is_prelu = False
+                    continue
+
+                mode = None
+                in_shape = in_nodes1.out_shapes[0]
+                if in_shape == list(in_nodes3[0].value.shape):
+                    mode = "element"
+                elif len(in_nodes3[0].value.shape) == 0:
+                    mode = "all"
+                elif len(in_nodes3[0].value.shape
+                         ) == 1 and in_nodes3[0].value.shape[0] == 1:
+                    mode = "all"
+                elif len(in_shape) == 4 and len(
+                        in_nodes3[0].value.shape
+                ) == 1 and in_nodes3[0].value.shape[0] == in_shape[-1]:
+                    mode = "channel"
+                    weight = self.op_mapper.weights[in_nodes3[0].layer_name]
+                    weight = numpy.expand_dims(weight, 0)
+                    weight = numpy.expand_dims(weight, 2)
+                    weight = numpy.expand_dims(weight, 3)
+                    self.op_mapper.weights[in_nodes3[0].layer_name] = weight
+                    in_nodes3[0].fluid_code.layers[0].param_attr["shape"] = [
+                        1, in_shape[-1], 1, 1
+                    ]
+                else:
+                    is_prelu = False
+                    continue
+
+                if is_prelu:
+                    index = in_nodes1.outputs.index(in_nodes0[0].layer_name)
+                    del in_nodes1.outputs[index]
+                    index = in_nodes1.outputs.index(in_nodes3[1].layer_name)
+                    del in_nodes1.outputs[index]
+                    index = in_nodes1.outputs.index(in_nodes4[1].layer_name)
+                    del in_nodes1.outputs[index]
+
+                    node.layer_type = "Prelu"
+                    node.inputs = [in_nodes1.layer_name]
+                    node.outputs = node.outputs
+                    act = node.fluid_code.layers[-1].param_attr.get("act", None)
+                    node.fluid_code.clear()
+                    attr = {
+                        "mode": string(mode),
+                        "param_attr": string(in_nodes3[0].layer_name)
+                    }
+
+                    node.fluid_code.add_layer(
+                        "prelu",
+                        inputs=in_nodes1.fluid_code.layers[-1].output,
+                        output=node,
+                        param_attr=attr)
+                del self.graph.node_map[in_nodes0[0].layer_name]
+                del self.graph.node_map[in_nodes0[1].layer_name]
+                del self.graph.node_map[in_nodes2[0].layer_name]
+                del self.graph.node_map[in_nodes2[1].layer_name]
+                del self.graph.node_map[in_nodes3[1].layer_name]
+                del self.graph.node_map[in_nodes4[1].layer_name]