Merge pull request #2 from PaddlePaddle/develop

Develop

x2paddle/core/fluid_code.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 from x2paddle.core.graph import GraphNode
+import collections
 
 
 class Layer(object):
@@ -36,25 +37,34 @@ class Layer(object):
         if isinstance(self.inputs, list):
             in_list = "["
             for input in self.inputs:
-                assert isinstance(
-                    input, GraphNode), "Type of input should be GraphNode"
+                if isinstance(input, GraphNode):
                     if hasattr(input, "index"):
-                    in_list += (input.layer_name + "[{}]".format(input.index) +
-                                ", ")
+                        in_list += (input.layer_name +
+                                    "[{}]".format(input.index) + ", ")
                     else:
                         in_list += (input.layer_name + ", ")
+                elif isinstance(input, str):
+                    in_list += (input + ", ")
+                else:
+                    raise Exception(
+                        "Element of inputs should GraphNode or String")
             in_list = in_list.strip(", ") + "], "
             layer_code += in_list
         elif isinstance(self.inputs, dict):
-            for key, input in self.inputs.items():
-                assert isinstance(
-                    input, GraphNode), "Type of input should be GraphNode"
+            inputs = collections.OrderedDict(self.inputs)
+            for key, input in inputs.items():
+                if isinstance(input, GraphNode):
                     if hasattr(input, "index"):
                         layer_code = layer_code + key + "={}, ".format(
                             input.layer_name + "[{}]".format(input.index))
                     else:
                         layer_code = layer_code + key + "={}, ".format(
                             input.layer_name)
+                elif isinstance(input, str):
+                    layer_code = layer_code + key + "={}, ".format(input)
+                else:
+                    raise Exception(
+                        "Element of inputs should GraphNode or String")
         elif isinstance(self.inputs, GraphNode):
             if hasattr(self.inputs, "index"):
                 layer_code += (self.inputs.layer_name +
@@ -66,7 +76,8 @@ class Layer(object):
         else:
             raise Exception("Unknown type of inputs.")
 
-        for key, value in self.param_attr.items():
+        param_attr = collections.OrderedDict(self.param_attr)
+        for key, value in param_attr.items():
             layer_code = layer_code + key + "={}, ".format(value)
         layer_code = layer_code.strip(", ")
 
@@ -97,7 +108,8 @@ class Layer(object):
         else:
             raise Exception("Unknown type of inputs.")
 
-        for key, value in self.param_attr.items():
+        param_attr = collections.OrderedDict(self.param_attr)
+        for key, value in param_attr.items():
             layer_code = layer_code + key + "={}, ".format(value)
         layer_code = layer_code.strip(", ")
 

x2paddle/decoder/tf_decoder.py

@@ -176,8 +176,9 @@ class TFDecoder(object):
             self.sess.graph.as_default()
             tf.import_graph_def(graph_def, name='', input_map=input_map)
 
-        for node in graph_def.node:
-            print(node.name, node.op, node.input)
+
+#        for node in graph_def.node:
+#            print(node.name, node.op, node.input)
 
         self.sess.run(tf.global_variables_initializer())
 

x2paddle/op_mapper/tf_op_mapper.py

@@ -19,6 +19,31 @@ import numpy
 
 
 class TFOpMapper(OpMapper):
+
+    directly_map_ops = {
+        'Relu': ['relu'],
+        'Relu6': ['relu6'],
+        'Shape': ['shape'],
+        'Abs': ['abs'],
+        'Sigmoid': ['sigmoid'],
+        'Exp': ['exp'],
+        'Rsqrt': ['rsqrt'],
+        'Squeeze': ['squeeze', {
+            'squeeze_dims': 'axes'
+        }],
+        'Softmax': ['softmax', {
+            'axis': 'axis'
+        }],
+    }
+    elementwise_ops = {
+        'Add': 'elementwise_add',
+        'RealDiv': 'elementwise_div',
+        'BiasAdd': 'elementwise_add',
+        'Sub': 'elementwise_sub',
+        'Maximum': 'elementwise_max',
+        'Mul': 'elementwise_mul'
+    }
+
     def __init__(self, decoder):
         super(TFOpMapper, self).__init__()
         self.decoder = decoder
@@ -30,15 +55,20 @@ class TFOpMapper(OpMapper):
         print("Total nodes: {}".format(len(self.graph.topo_sort)))
 
         # check if ops in model are all supported
-        if not self.op_checker():
-            raise Exception("Model are not supported yet.")
+        # TODO
 
         for node_name in self.graph.topo_sort:
             node = self.graph.get_node(node_name)
             op = node.layer_type
-            if hasattr(self, op):
+            if op in self.directly_map_ops:
+                self.directly_map(node)
+            elif op in self.elementwise_ops:
+                self.elementwise_map(node)
+            elif hasattr(self, op):
                 func = getattr(self, op)
                 func(node)
+            else:
+                raise Exception("OP: [{}] not support yet".format(op))
 
         for i in range(len(self.graph.topo_sort)):
             node_name = self.graph.topo_sort[i]
@@ -47,7 +77,24 @@ class TFOpMapper(OpMapper):
             node = self.graph.get_node(node_name)
             self.net_code += node.fluid_code.gen_codes()
 
-    def elementwise_operator(self, node, op_type):
+    def directly_map(self, node):
+        assert node.layer_type in self.directly_map_ops
+        op_info = self.directly_map_ops[node.layer_type]
+        input = self.graph.get_node(node.layer.input[0], copy=True)
+        attr = dict()
+        for param in op_info[1:]:
+            tf_param_name = list(param.keys())[0]
+            pd_param_name = list(param.values())[0]
+            tf_param = node.get_attr(tf_param_name)
+            attr[pd_param_name] = tf_param
+        node.fluid_code.add_layer(op_info[0],
+                                  inputs=input,
+                                  output=node,
+                                  param_attr=attr)
+
+    def elementwise_map(self, node):
+        assert node.layer_type in self.elementwise_ops
+        op_type = self.elementwise_ops[node.layer_type]
         x = self.graph.get_node(node.layer.input[0], copy=True)
         y = self.graph.get_node(node.layer.input[1], copy=True)
         x_shape = x.out_shapes[0]
@@ -161,41 +208,6 @@ class TFOpMapper(OpMapper):
                                   output=node,
                                   param_attr=attr)
 
-    def RealDiv(self, node):
-        self.elementwise_operator(node, "elementwise_div")
-
-    def Relu(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
-        node.fluid_code.add_layer("relu",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=None)
-
-    def Squeeze(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
-        squeeze_dims = node.get_attr('squeeze_dims')
-        attr = {'axes': squeeze_dims}
-        node.fluid_code.add_layer("squeeze",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
-
-    def BiasAdd(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
-        bias = self.graph.get_node(node.layer.input[1], copy=True)
-        inputs = {'x': input, 'y': bias}
-        node.fluid_code.add_layer("elementwise_add",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=None)
-
-    def Identity(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
-        node.fluid_code.add_layer("assign",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=None)
-
     def MaxPool(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
 
@@ -314,13 +326,6 @@ class TFOpMapper(OpMapper):
                                       output=node,
                                       param_attr=attr)
 
-    def Relu6(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
-        node.fluid_code.add_layer("relu6",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=None)
-
     def FusedBatchNorm(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
         gamma = self.graph.get_node(node.layer.input[1], copy=True)
@@ -433,13 +438,6 @@ class TFOpMapper(OpMapper):
                                       output=node,
                                       param_attr=attr)
 
-    def Shape(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
-        node.fluid_code.add_layer("shape",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=None)
-
     def Reshape(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
         param = self.graph.get_node(node.layer.input[1], copy=True)
@@ -474,27 +472,6 @@ class TFOpMapper(OpMapper):
                                   inputs=input,
                                   output=node,
                                   param_attr=attr)
-        # temporary shape inference fix
-
-
-#        if param.layer_type == "Pack":
-#            shape_slices = list()
-#            for i in range(len(param.layer.input)):
-#                slice = self.graph.get_node(param.layer.input[i], copy=True)
-#                if slice.layer_type == "Const":
-#                    shape_slices.append(slice.value.tolist())
-#                else:
-#                    shape_slices.append(0)
-#            if shape_slices.count(-1) == 0:
-#                shape_slices[shape_slices.index(0)] = -1
-#            attr = {"shape": shape_slices}
-#            node.fluid_code.add_layer("reshape",
-#                                      inputs=node,
-#                                      output=node,
-#                                      param_attr=attr)
-
-    def Add(self, node):
-        self.elementwise_operator(node, "elementwise_add")
 
     def AvgPool(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -542,23 +519,6 @@ class TFOpMapper(OpMapper):
                                       output=node,
                                       param_attr=attr)
 
-    def Softmax(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
-        node.fluid_code.add_layer("softmax",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=None)
-
-    def Sigmoid(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
-        node.fluid_code.add_layer("sigmoid",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=None)
-
-    def Maximum(self, node):
-        self.elementwise_operator(node, "elementwise_max")
-
     def SplitV(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
         num_sections = self.graph.get_node(node.layer.input[1], copy=True)
@@ -576,13 +536,6 @@ class TFOpMapper(OpMapper):
                                   output=node,
                                   param_attr=attr)
 
-    def Exp(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
-        node.fluid_code.add_layer("exp",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=None)
-
     def ConcatV2(self, node):
         inputs = [
             self.graph.get_node(name, copy=True)
@@ -649,19 +602,6 @@ class TFOpMapper(OpMapper):
                                output=node,
                                param_attr=None)
 
-    def Mul(self, node):
-        self.elementwise_operator(node, "elementwise_mul")
-
-    def Sub(self, node):
-        self.elementwise_operator(node, "elementwise_sub")
-
-    def Rsqrt(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
-        node.fluid_code.add_layer("rsqrt",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=None)
-
     def swish_f32(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
         node.fluid_code.add_layer("sigmoid",
@@ -765,13 +705,6 @@ class TFOpMapper(OpMapper):
                                   output=node,
                                   param_attr=attr)
 
-    def Abs(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
-        node.fluid_code.add_layer("abs",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=None)
-
     def Conv2DBackpropInput(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
         kernel = self.graph.get_node(node.layer.input[1], copy=True)

x2paddle/optimizer/tf_optimizer.py

@@ -15,56 +15,8 @@
 # TODO useless node remove
 from x2paddle.decoder.tf_decoder import TFGraph
 
+# TODO bn merge
 
-class TFGraphOptimizer(object):
-    def __init__(self):
-        print("Doint Nothing")
+# TODO activation merge
 
-    def remove_isolated_node(self, graph):
-        # delete isolated nodes
-        isolated_nodes = list()
-        for node_name in graph.node_map.keys():
-            if len(graph.get_node(node_name).inputs) == 0 or len(
-                    graph.get_node(node_name).outputs) == 0:
-                isolated_nodes.append(node_name)
-
-        graph.remove_node(node_name)
-
-    def remove_identity_node(self, graph):
-        identity_node = list()
-        for node_name, node in graph.node_map.items():
-            if node.layer_type == "Identity":
-                identity_node.append(node_name)
-
-        for node_name in identity_node:
-            node = graph.get_node(node_name)
-            # Remind: Only 1 input for Identity node
-            input_node = graph.get_node(node.inputs[0])
-
-            # remove identity node from graph
-            idx = input_node.outputs.index(node_name)
-            del input_node.outputs[idx]
-
-            output_names = node.outputs
-            for output_name in output_names:
-                output_node = graph.get_node(output_name)
-                idx = output_node.inputs.index(node_name)
-                output_node.inputs[idx] = input_node.layer_name
-
-            idx = graph.topo_sort.index(node_name)
-            del graph.topo_sort[idx]
-
-    def run(self, graph):
-        self.remove_isolated_node(graph)
-        self.remove_identity_node(graph)
-
-
-# TODO identity node remove
-
-# TODO subgraph optimize
-
-# TODO compute optimize
-
-# activation merge
-
-# biasadd merge
+# TODO biasadd merge