add save inference model func

x2paddle/convert.py

@@ -58,8 +58,7 @@ def tf2paddle(model_path, save_dir):
     print("Now translating model from tensorflow to paddle.")
     model = TFDecoder(model_path)
     mapper = TFOpMapper(model)
-    mapper.run()
-    mapper.save_python_model(save_dir)
+    mapper.save_inference_model(save_dir)
 
 
 def caffe2paddle(proto, weight, save_dir, caffe_proto):
@@ -69,8 +68,7 @@ def caffe2paddle(proto, weight, save_dir, caffe_proto):
     print("Now translating model from caffe to paddle.")
     model = CaffeDecoder(proto, weight, caffe_proto)
     mapper = CaffeOpMapper(model)
-    mapper.run()
-    mapper.save_python_model(save_dir)
+    mapper.save_inference_model(save_dir)
 
 
 def main():

x2paddle/core/graph.py

@@ -113,6 +113,13 @@ class Graph(object):
         idx = self.topo_sort.index(node_name)
         del self.topo_sort[idx]
 
+        if node_name in self.input_nodes:
+            idx = self.input_nodes.index(node_name)
+            del self.input_nodes[idx]
+        if node_name in self.output_nodes:
+            idx = self.output_nodes.index(node_name)
+            del self.output_nodes[idx]
+
     def print(self):
         for i, tmp in enumerate(self.topo_sort):
             print(tmp, self.node_map[tmp].layer_type, self.node_map[tmp].inputs,

x2paddle/core/op_mapper.py

@@ -23,6 +23,8 @@ class OpMapper(object):
         self.tab = "    "
         self.net_code = list()
         self.weights = dict()
+        self.inputs = list()
+        self.outputs = list()
 
     def op_checker(self):
         unsupported_ops = set()
@@ -56,16 +58,83 @@ class OpMapper(object):
         self.add_codes("import paddle.fluid as fluid")
         self.add_codes("")
 
-    def save_inference_model(self):
-        print("Not Implement")
+    def save_inference_model(self, save_dir):
+        self.save_python_model(save_dir)
+
+        import sys
+        import paddle.fluid as fluid
+        py_code_dir = os.path.join(save_dir, "model_with_code")
+        sys.path.append(py_code_dir)
+        import model
+        try:
+            inputs, outputs = model.x2paddle_net()
+            input_names = [input.name for input in inputs]
+            exe = fluid.Executor(fluid.CPUPlace())
+            exe.run(fluid.default_startup_program())
+
+            def if_exist(var):
+                b = os.path.exists(
+                    os.path.join(os.path.join(save_dir, var.name)))
+                return b
+
+            fluid.io.load_vars(exe,
+                               save_dir,
+                               fluid.default_main_program(),
+                               predicate=if_exist)
+
+            fluid.io.save_inference_model(dirname=os.path.join(
+                save_dir, "inference_model"),
+                                          feeded_var_names=input_names,
+                                          target_vars=outputs,
+                                          executor=exe,
+                                          params_filename="__params__")
+
+        except:
+            raise Exception(
+                "Paddle code was saved in {}/model.py, but seems there's wrong exist, please check model.py manually."
+                .format(py_code_dir))
 
     def save_python_model(self, save_dir):
+        if not os.path.exists(save_dir):
+            os.makedirs(save_dir)
+
+        py_code_dir = os.path.join(save_dir, "model_with_code")
+        if not os.path.exists(py_code_dir):
+            os.makedirs(py_code_dir)
+
         for name, param in self.weights.items():
-            export_paddle_param(param, name, save_dir)
+            export_paddle_param(param, name, py_code_dir)
         self.add_heads()
-        self.add_codes(self.net_code)
-        self.add_codes("")
-        self.add_codes(inspect.getsourcelines(init_net)[0])
-        fp = open(os.path.join(save_dir, "model.py"), 'w')
+
+        if hasattr(self, "used_custom_layers"):
+            for _, layer_code in self.used_custom_layers.items():
+                self.add_codes(layer_code, 0)
+
+        self.add_codes("\ndef x2paddle_net():", 0)
+        for i in range(len(self.graph.topo_sort)):
+            node_name = self.graph.topo_sort[i]
+            if hasattr(self, "omit_nodes") and node_name in self.omit_nodes:
+                continue
+            node = self.graph.get_node(node_name)
+            self.add_codes(node.fluid_code.gen_codes(), 1)
+
+        self.add_codes("", 0)
+
+        input_str = "["
+        for name in self.graph.input_nodes:
+            input_str += (name + ", ")
+        input_str = input_str.strip(", ") + "]"
+        output_str = "["
+        for name in self.graph.output_nodes:
+            output_str += (name + ", ")
+        output_str = output_str.strip(", ") + "]"
+
+        return_code = "return {}, {}".format(input_str, output_str)
+
+        self.add_codes(return_code, 1)
+        self.add_codes("", 0)
+
+        self.add_codes(inspect.getsourcelines(run_net)[0])
+        fp = open(os.path.join(py_code_dir, "model.py"), 'w')
         fp.write(self.paddle_codes)
         fp.close()

x2paddle/core/util.py

@@ -55,8 +55,6 @@ def export_paddle_param(param, param_name, dir):
         assert param.size == 1, "Unexpected situation happend!"
         shape = [1]
     assert str(param.dtype) in dtype_map, "Unknown dtype of params."
-    if not os.path.exists(dir):
-        os.makedirs(dir)
 
     fp = open(os.path.join(dir, param_name), 'wb')
     numpy.array([0], dtype='int32').tofile(fp)
@@ -72,8 +70,9 @@ def export_paddle_param(param, param_name, dir):
     fp.close()
 
 
-def init_net(param_dir="./"):
+def run_net(param_dir="./"):
     import os
+    inputs, outputs = x2paddle_net()
     exe = fluid.Executor(fluid.CUDAPlace(0))
     exe.run(fluid.default_startup_program())
 
@@ -85,3 +84,9 @@ def init_net(param_dir="./"):
                        param_dir,
                        fluid.default_main_program(),
                        predicate=if_exist)
+
+    fluid.io.save_inference_model(dirname='inference_model',
+                                  feeded_var_names=[i.name for i in inputs],
+                                  target_vars=outputs,
+                                  executor=exe,
+                                  params_filename="__params__")

x2paddle/decoder/tf_decoder.py

@@ -163,6 +163,10 @@ class TFGraph(Graph):
             idx = self.topo_sort.index(node_name)
             del self.topo_sort[idx]
 
+            if node_name in self.output_nodes:
+                idx = self.output_nodes.index(node_name)
+                self.output_nodes[idx] = input_node.layer_name
+
 
 class TFDecoder(object):
     def __init__(self, pb_model):

x2paddle/op_mapper/caffe_op_mapper.py

@@ -26,7 +26,7 @@ class CaffeOpMapper(OpMapper):
         self.graph = decoder.caffe_graph
         self.weights = dict()
         resolver = decoder.resolver
-        self.mylayers = {}
+        self.used_custom_layers = {}
         self.inputs = self.graph.input_nodes
         self.outputs = self.graph.output_nodes
         if resolver.has_pycaffe():
@@ -67,8 +67,8 @@ class CaffeOpMapper(OpMapper):
                 self.deal_custom_layer(node)
             else:
                 raise Exception("Model are not supported yet.")
-        for key in self.mylayers:
-            self.net_code.append(self.mylayers[key])
+        for key in self.used_custom_layers:
+            self.net_code.append(self.used_custom_layers[key])
 
         for i in range(len(self.graph.topo_sort)):
             node_name = self.graph.topo_sort[i]
@@ -1050,5 +1050,5 @@ class CaffeOpMapper(OpMapper):
                                   output=node,
                                   param_attr=kwargs,
                                   is_custom_layer=True)
-        if op not in self.mylayers:
-            self.mylayers[op] = custom_code
+        if op not in self.used_custom_layers:
+            self.used_custom_layers[op] = custom_code

x2paddle/op_mapper/tf_op_mapper.py

@@ -51,12 +51,15 @@ class TFOpMapper(OpMapper):
         self.weights = dict()
         self.omit_nodes = list()
 
-    def run(self):
-        print("Total nodes: {}".format(len(self.graph.topo_sort)))
-
-        # check if ops in model are all supported
-        # TODO
+        not_placeholder = list()
+        for name in self.graph.input_nodes:
+            if self.graph.get_node(name).layer_type != "Placeholder":
+                not_placeholder.append(name)
+        for name in not_placeholder:
+            idx = self.graph.input_nodes.index(name)
+            del self.graph.input_nodes[idx]
 
+        print("Total nodes: {}".format(len(self.graph.topo_sort)))
         for node_name in self.graph.topo_sort:
             node = self.graph.get_node(node_name)
             op = node.layer_type
@@ -70,13 +73,6 @@ class TFOpMapper(OpMapper):
             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]
-            if node_name in self.omit_nodes:
-                continue
-            node = self.graph.get_node(node_name)
-            self.net_code += node.fluid_code.gen_codes()
-
     def directly_map(self, node):
         assert node.layer_type in self.directly_map_ops
         op_info = self.directly_map_ops[node.layer_type]