Merge pull request #442 from SunAhong1993/paddle-2.0-new

for same structure

x2paddle/decoder/caffe_decoder.py

@@ -18,6 +18,7 @@ from google.protobuf import text_format
 import numpy as np
 from x2paddle.core.graph import GraphNode, Graph
 from x2paddle.core.fluid_code import FluidCode
+from x2paddle.decoder import caffe_shape_inference
 
 
 class CaffeResolver(object):
@@ -59,6 +60,28 @@ class CaffeGraphNode(GraphNode):
 
     def set_params(self, params):
         self.data = params
+        
+    @property
+    def name(self):
+        if hasattr(self, 'index'):
+            return "{}_p{}".format(self.layer_name, self.index)
+        return self.layer_name
+    
+    @property
+    def out_shapes(self):
+        return self._out_shapes
+
+    @out_shapes.setter
+    def out_shapes(self, value):
+        self._out_shapes = value
+        
+    @property
+    def in_shapes(self):
+        return self._in_shapes
+
+    @in_shapes.setter
+    def in_shapes(self, value):
+        self._in_shapes = value
 
 
 class CaffeGraph(Graph):
@@ -226,8 +249,11 @@ class CaffeGraph(Graph):
                        layer_name)
 
         super(CaffeGraph, self).build()
+        for i, node_name in enumerate(self.topo_sort):
+            node = self.get_node(node_name)
+            self.set_node_shape(node)
 
-    def get_bottom_node(self, node, idx=0, copy=False):
+    def get_input_node(self, node, idx=0, copy=False):
         input_node_name = node.inputs[idx]
         assert input_node_name in self.node_map, 'The {} isn\'t a valid node'.format(
             name)
@@ -238,6 +264,19 @@ class CaffeGraph(Graph):
         else:
             name = input_node_name
         return self.get_node(name, copy=copy)
+    
+    def set_node_shape(self, node):
+        inputs = node.inputs
+        input_shape = []
+        for i, nm in enumerate(inputs):
+            last_node = self.get_node(nm)
+            tmp = node.layer.bottom[i]
+            idx = list(last_node.layer.top).index(tmp)
+            input_shape.append(last_node.out_shapes[idx])
+        node.in_shapes = input_shape
+        func_name = 'shape_' + node.layer_type.lower()
+        node.out_shapes = getattr(caffe_shape_inference, func_name)(node.layer,
+                                                                    input_shape)
 
 
 class CaffeDecoder(object):

x2paddle/op_mapper/dygraph/caffe2paddle/caffe_shape.py → x2paddle/decoder/caffe_shape_inference.py

@@ -83,6 +83,10 @@ def shape_convolution(layer, input_shape):
     return get_strided_kernel_output_shape(params, input_shape[0], math.floor)
 
 
+def shape_depthwiseconvolution(layer, input_shape):
+    return shape_convolution(layer, input_shape)
+
+
 def shape_deconvolution(layer, input_shape):
 
     h_i = input_shape[0][2]

x2paddle/decoder/onnx_decoder.py

@@ -64,6 +64,12 @@ class ONNXGraphNode(GraphNode):
         if 'value' not in self.attr_map:
             return None
         return self.attr_map['value']
+    
+    @property
+    def name(self):
+        if hasattr(self, 'index'):
+            return "{}_p{}".format(self.layer_name, self.index)
+        return self.layer_name
 
     def get_attribute_value(self, attr):
         """
@@ -118,6 +124,10 @@ class ONNXGraphDataNode(GraphNode):
             out_shapes = list()
             out_shapes.append(values)
             return out_shapes
+        
+    @property
+    def name(self):
+        return self.layer_name
 
     @property
     def dtype(self):
@@ -308,6 +318,7 @@ class ONNXGraph(Graph):
             if ipt_node.layer_name in node.which_child:
                 ipt_node.index = node.which_child[ipt_node.layer_name]
             return ipt_node
+        
 
     def graph_weights(self):
         """

x2paddle/decoder/tf_decoder.py

@@ -189,6 +189,10 @@ class TFGraph(Graph):
         if len(items) == 1 and node.layer_type in self.multi_out_ops:
             node.index = 0
         return node
+    
+    def get_input_node(self, node, idx=0, copy=False):
+        input_node_name = node.layer.input[idx]
+        return self.get_node(input_node_name, copy)
 
     def remove_node(self, node_name):
         if node_name not in self.node_map:
@@ -316,7 +320,7 @@ class TFDecoder(object):
             self.sess = tf.compat.v1.Session()
         except:
             self.sess = tf.Session()
-        self.input_info = dict()
+        self.inputs_info = dict()
         self.define_input_shape = define_input_shape
         with open(pb_model, 'rb') as f:
             try:
@@ -426,50 +430,40 @@ class TFDecoder(object):
                 input_map["{}:0".format(layer.name)] = x2paddle_input
                 if shape.count(None) > 0:
                     shape[shape.index(None)] = -1
-                self.input_info["x2paddle_{}".format(layer.name)] = (shape,
+                self.inputs_info["x2paddle_{}".format(layer.name)] = (shape,
                                                                      dtype)
             else:
                 value = graph_node.layer.attr["shape"].shape
                 shape = [dim.size for dim in value.dim]
-                self.input_info[layer.name] = (shape, dtype)
+                self.inputs_info[layer.name] = (shape, dtype)
 
         return input_map
 
     # trick method
     # should be removed after PaddlePaddle V1.6 been released
-    def infer_tensor(self, graph_node):
+    def infer_tensor(self, graph_node, out_shape=None, use_diff_inputs=True):
         if hasattr(graph_node, "index"):
             tensor_name = graph_node.layer.name + ":{}".format(graph_node.index)
         else:
             tensor_name = graph_node.layer.name + ":0"
         feed = dict()
-        for input_name, info in self.input_info.items():
-            (shape, dtype) = cp.deepcopy(info)
-            input_tensor = self.sess.graph.get_tensor_by_name(input_name + ":0")
-            if shape.count(-1) > 0:
-                shape[shape.index(-1)] = 2
-            feed[input_tensor] = numpy.random.random_sample(shape)
-        output_tensor = self.sess.graph.get_tensor_by_name(tensor_name)
-        return self.sess.run([output_tensor], feed)[0]
-
-    def infer_shape_tensor(self, graph_node, out_shape=None):
-        if hasattr(graph_node, "index"):
-            tensor_name = graph_node.layer.name + ":{}".format(graph_node.index)
+        if use_diff_inputs:
+            batch_size = [2, 3, 5]
         else:
-            tensor_name = graph_node.layer.name + ":0"
-        feed = dict()
-        batch_size = [2, 3, 5]
+            batch_size = [2]
         results = list()
         for b in batch_size:
-            for input_name, info in self.input_info.items():
+            for input_name, info in self.inputs_info.items():
                 (shape, dtype) = cp.deepcopy(info)
-                input_tensor = self.sess.graph.get_tensor_by_name(input_name +
-                                                                  ":0")
+                input_tensor = self.sess.graph.get_tensor_by_name(input_name + ":0")
                 if shape.count(-1) > 0:
                     shape[shape.index(-1)] = b
                 feed[input_tensor] = numpy.random.random_sample(shape)
             output_tensor = self.sess.graph.get_tensor_by_name(tensor_name)
-            results.append(self.sess.run([output_tensor], feed)[0].flatten())
+            if use_diff_inputs:
+                results.append(self.sess.run([output_tensor], feed)[0].flatten())
+            else:
+                return self.sess.run([output_tensor], feed)[0]
 
         compare01 = (results[0] == results[1])
         compare12 = (results[1] == results[2])
@@ -494,38 +488,4 @@ class TFDecoder(object):
             return results[0].tolist()
         else:
             raise Exception("Couldn't infer a stable shape shape tensor value")
-
-    def infer_tensor_shape(self, graph_node):
+            
\ No newline at end of file
-        if hasattr(graph_node, "index"):
-            tensor_name = graph_node.layer.name + ":{}".format(graph_node.index)
-        else:
-            tensor_name = graph_node.layer.name + ":0"
-        feed = dict()
-        batch_size = [2, 3, 5]
-        shapes = list()
-        for b in batch_size:
-            for input_name, info in self.input_info.items():
-                (shape, dtype) = cp.deepcopy(info)
-                input_tensor = self.sess.graph.get_tensor_by_name(input_name +
-                                                                  ":0")
-                if shape.count(-1) > 0:
-                    shape[shape.index(-1)] = b
-                feed[input_tensor] = numpy.random.random_sample(shape)
-            output_tensor = self.sess.graph.get_tensor_by_name(tensor_name)
-            shape = self.sess.run([output_tensor], feed)[0].shape
-            shapes.append(numpy.array(shape))
-
-        compare01 = (shapes[0] == shapes[1])
-        compare12 = (shapes[1] == shapes[2])
-
-        if compare01.all() and compare12.all():
-            return shape[0].tolist()
-
-        if (compare01 == compare12).all():
-            index = numpy.argwhere(compare01 == False).flatten()
-            if index.shape[0] != 1:
-                raise Exception("There's not only one unstable dimension")
-            if index[0] != 0:
-                raise Exception("Batch size not in the first dimension")
-            shapes[0][0] = -1
-            return shapes[0].tolist()

x2paddle/op_mapper/dygraph/onnx2paddle/onnx_op_mapper.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import sys
 from x2paddle.op_mapper.dygraph.onnx2paddle.opset9 import OpSet9
 from x2paddle.core.op_mapper import OpMapper
 from x2paddle.decoder.onnx_decoder import ONNXGraphNode
@@ -25,34 +26,33 @@ class ONNXOpMapper(OpMapper):
         self.default_op_set = 9
         self.graph = decoder.graph
         self.paddle_graph = PaddleGraph(parent_layer=None, graph_type="dygraph", source_type="onnx")
+        self.paddle_graph.outputs = self.graph.output_nodes
         self.opset = self.create_opset(decoder)
         if not self.op_checker():
-            raise Exception("Model are not supported yet.")
-        #mapping op
+            raise Exception("Model is not supported yet.")
+        
         print("Total nodes: {}".format(
             sum([
                 isinstance(node, ONNXGraphNode)
                 for name, node in self.graph.node_map.items()
             ])))
-
         print("Nodes converting ...")
-        for node_name in self.graph.topo_sort:
+        for i, node_name in enumerate(self.graph.topo_sort):
+            sys.stderr.write("\rConverting node {} ...     ".format(i + 1))
             node = self.graph.get_node(node_name)
             op = node.layer_type
             if hasattr(self.opset, op):
                 func = getattr(self.opset, op)
                 func(node)
-            elif op in self.opset.default_op_mapping:
+            elif op in self.opset.directly_map_ops:
                 self.opset.directly_map(node)
             elif op in self.opset.elementwise_ops:
                 self.opset.elementwise_map(node)
-        print("Nodes converted.")
-        self.weights = self.opset.weights
-        self.inputs_info = self.opset.inputs_info
+        print("\nNodes converted.")
         self.paddle_graph.set_name(self.graph.graph_name)
-        self.paddle_graph.set_parameters(self.weights)
-        self.paddle_graph.set_inputs_info(self.inputs_info)
-        self.paddle_graph.outputs = self.graph.output_nodes
+        self.paddle_graph.set_parameters(self.opset.weights)
+        self.paddle_graph.set_inputs_info(self.opset.inputs_info)
+        
 
     def op_checker(self):
         unsupported_ops = set()
@@ -60,16 +60,17 @@ class ONNXOpMapper(OpMapper):
             node = self.graph.get_node(node_name)
             op = node.layer_type
             if not hasattr(self.opset, op) and \
-                op not in self.opset.default_op_mapping and \
+                op not in self.opset.directly_map_ops and \
                 op not in self.opset.elementwise_ops:
                 unsupported_ops.add(op)
         if len(unsupported_ops) == 0:
             return True
         else:
-            print("There are {} ops not supported yet, list as below".format(
-                len(unsupported_ops)))
+            if len(unsupported_ops) > 0:
+                print("\n========= {} OPs are not supported yet ===========".format(
+                    len(unsupported_ops)))
             for op in unsupported_ops:
-                print(op)
+                print("========== {} ============".format(op))
             return False
 
     def create_opset(self, decoder):

x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_op_mapper.py

@@ -38,28 +38,34 @@ class PyTorchOpMapper(OpMapper):
         self.scope_name2id = dict()
         self.inputs_info = dict()
         # 转换
-        self.check_op(decoder.graph)
+        if not self.op_checker(decoder.graph):
+            raise Exception("Model is not supported yet.")
         self.paddle_graph, _ = self.traverse(decoder.graph)
         self.paddle_graph.set_inputs_info(self.inputs_info)
 
-    def check_op(self, script_graph):
+    def op_checker(self, script_graph):
         def _update_op_list(graph):
             for node in graph.nodes():
                 op_list.append(node.kind())
                 for block in node.blocks():
                     _update_op_list(block)
-
         op_list = list()
         _update_op_list(script_graph)
         op_list = list(set(op_list))
-        unsupported_op_list = []
+        unsupported_ops = []
         for op in op_list:
             func_name = op.replace('::', '_')
             if not (hasattr(prim, func_name) or hasattr(aten, func_name)):
-                unsupported_op_list.append(op)
-        if len(unsupported_op_list) > 0:
-            raise Exception("The kind {} in model is not supported yet.".format(
-                unsupported_op_list))
+                unsupported_ops.append(op)
+        if len(unsupported_ops) == 0:
+            return True
+        else:
+            if len(unsupported_ops) > 0:
+                print("\n========= {} OPs are not supported yet ===========".format(
+                    len(unsupported_ops)))
+            for op in unsupported_ops:
+                print("========== {} ============".format(op))
+            return False 
 
     def traverse(self, script_graph, parent_layer=None):
         # 用于获取graph的输入

x2paddle/op_mapper/static/caffe2paddle/caffe_op_mapper.py

@@ -231,7 +231,7 @@ class CaffeOpMapper(OpMapper):
             self.weights[node.layer_name + '_bias'] = data[1]
         assert len(node.inputs
                    ) == 1, 'The count of Convolution node\'s input is not 1.'
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         layer_attrs = {
             'filter_size': kernel,
             'num_filters': channel,
@@ -273,7 +273,7 @@ class CaffeOpMapper(OpMapper):
             self.weights[node.layer_name + '_bias'] = data[1]
         assert len(node.inputs
                    ) == 1, 'The count of Deconvolution node\'s input is not 1.'
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         layer_attrs = {
             'output_size': None,
             'filter_size': kernel,
@@ -306,7 +306,7 @@ class CaffeOpMapper(OpMapper):
             pool_type = 'avg'
         assert len(
             node.inputs) == 1, 'The count of Pooling node\'s input is not 1.'
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         layer_attrs = {
             'pool_size': kernel,
             'pool_stride': stride,
@@ -333,7 +333,7 @@ class CaffeOpMapper(OpMapper):
         # just scales by alpha (as does Krizhevsky's paper).
         # We'll account for that here.
         alpha = params.alpha / float(params.local_size)
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         layer_attrs = {
             'n': params.local_size,
             'k': params.k,
@@ -381,7 +381,7 @@ class CaffeOpMapper(OpMapper):
         #params = node.layer.inner_product_param
         assert params.axis == 1
         assert params.bias_term == True
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         layer_attrs = {
             'size': params.num_output,
             'name': string(node.layer_name),
@@ -399,7 +399,7 @@ class CaffeOpMapper(OpMapper):
     def Softmax(self, node):
         assert len(
             node.inputs) == 1, 'The count of Softmax node\'s input is not 1.'
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         params = node.layer.softmax_param
         axis = params.axis
         shape = node.input_shape[0]
@@ -415,7 +415,7 @@ class CaffeOpMapper(OpMapper):
     def Slice(self, node):
         assert len(
             node.inputs) == 1, 'The count of Slice node\'s input is not 1.'
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         top_len = len(node.layer.top)
         params = node.layer.slice_param
         axis = params.axis
@@ -445,7 +445,7 @@ class CaffeOpMapper(OpMapper):
         ) >= 1, 'The count of Concat node\'s input is not more than 1.'
         inputs_list = []
         for i in range(len(node.inputs)):
-            input = self.graph.get_bottom_node(node, idx=i, copy=True)
+            input = self.graph.get_input_node(node, idx=i, copy=True)
             inputs_list.append(self.get_input_name(input))
         params = node.layer.concat_param
         axis = params.axis
@@ -464,7 +464,7 @@ class CaffeOpMapper(OpMapper):
         """
         assert len(
             node.inputs) == 1, 'The count of ReLU node\'s input is not 1.'
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
 
         params = node.layer.relu_param
         if params.HasField('negative_slope') and params.negative_slope != 0:
@@ -483,7 +483,7 @@ class CaffeOpMapper(OpMapper):
     def PReLU(self, node):
         assert len(
             node.inputs) == 1, 'The count of PReLU node\'s input is not 1.'
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         params = node.layer.prelu_param
         mode_bool = params.channel_shared
         if mode_bool:
@@ -511,10 +511,10 @@ class CaffeOpMapper(OpMapper):
         inputs_dict = dict()
         for i, shape in enumerate(node.input_shape):
             if shape[1] == 1:
-                input = self.graph.get_bottom_node(node, idx=i, copy=True)
+                input = self.graph.get_input_node(node, idx=i, copy=True)
                 inputs_dict["label"] = self.get_input_name(input)
             else:
-                input = self.graph.get_bottom_node(node, idx=i, copy=True)
+                input = self.graph.get_input_node(node, idx=i, copy=True)
                 inputs_dict["input"] = self.get_input_name(input)
         params = node.layer.accuracy_param
         top_k = params.top_k
@@ -534,9 +534,9 @@ class CaffeOpMapper(OpMapper):
         params = node.layer.eltwise_param
         mode = params.operation
         inputs = []
-        input0 = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input0 = self.graph.get_input_node(node, idx=0, copy=True)
         inputs.append(input0)
-        input1 = self.graph.get_bottom_node(node, idx=1, copy=True)
+        input1 = self.graph.get_input_node(node, idx=1, copy=True)
         inputs.append(input1)
         if mode == 0:
             inputs_dict = {}
@@ -606,7 +606,7 @@ class CaffeOpMapper(OpMapper):
     def BatchNorm(self, node):
         assert len(
             node.inputs) == 1, 'The count of BatchNorm node\'s input is not 1.'
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         params = node.layer.batch_norm_param
         if hasattr(params, 'eps'):
             eps = params.eps
@@ -670,8 +670,8 @@ class CaffeOpMapper(OpMapper):
             # for two tensor, here resets axis to 1. Maybe there is a bug for unkown case.
             axis = 1
             bias_shape = node.input_shape[0][axis:axis + num_axes]
-            input0 = self.graph.get_bottom_node(node, idx=0, copy=True)
-            input1 = self.graph.get_bottom_node(node, idx=1, copy=True)
+            input0 = self.graph.get_input_node(node, idx=0, copy=True)
+            input1 = self.graph.get_input_node(node, idx=1, copy=True)
             inputs_dict = {}
             inputs_dict['x'] = self.get_input_name(input0)
             inputs_dict['y'] = self.get_input_name(input1)
@@ -682,7 +682,7 @@ class CaffeOpMapper(OpMapper):
                 axis=axis)
         else:
             bias_shape = node.input_shape[0][axis:axis + num_axes]
-            input0 = self.graph.get_bottom_node(node, idx=0, copy=True)
+            input0 = self.graph.get_input_node(node, idx=0, copy=True)
             input0_name = self.get_input_name(input0)
             self.paddle_graph.add_layer(
                 kernel="fluid.ParamAttr",
@@ -739,7 +739,7 @@ class CaffeOpMapper(OpMapper):
         
 
     def Reshape(self, node):
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         top_count = len(input.layer.top)
         is_inplace = False if top_count == 1 else True
         output_shape = node.output_shape[0]
@@ -759,7 +759,7 @@ class CaffeOpMapper(OpMapper):
         assert len(node.inputs) == 1 and len(
             node.outputs
         ) == 1, 'The count of ArgMax node\'s input and output is not 1.'
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         input_shape = node.input_shape[0]
         params = node.layer.argmax_param
         out_max_val = params.out_max_val if hasattr(params,
@@ -796,8 +796,8 @@ class CaffeOpMapper(OpMapper):
     def Crop(self, node):
         assert len(
             node.inputs) == 2, 'The count of Crop node\'s input is not 2.'
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
-        example = self.graph.get_bottom_node(node, idx=1, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
+        example = self.graph.get_input_node(node, idx=1, copy=True)
         params = node.layer.crop_param
         axis = params.axis
         input_shape = node.input_shape[0]
@@ -822,7 +822,7 @@ class CaffeOpMapper(OpMapper):
         assert len(
             node.
             inputs) == 1, 'The count of DetectionOutput node\'s input is not 1.'
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         self.paddle_graph.add_layer(
             kernel="fluid.layers.reshape",
             inputs={"x": self.get_input_name(input)},
@@ -832,7 +832,7 @@ class CaffeOpMapper(OpMapper):
     def Power(self, node):
         assert len(
             node.inputs) == 1, 'The count of Permute node\'s input is not 1.'
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         params = node.layer.power_param
         power = params.power
         scale = params.scale
@@ -857,7 +857,7 @@ class CaffeOpMapper(OpMapper):
     def Reduction(self, node):
         assert len(
             node.inputs) == 1, 'The count of Reduction node\'s input is not 1.'
-        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         params = node.layer.reduction_param
         operation = params.operation
         axis = params.axis
@@ -942,15 +942,15 @@ class CaffeOpMapper(OpMapper):
                 self.weights[weights_name[i]] = data[i]
         inputs_list = []
         for i in range(len(node.inputs)):
-            input = self.graph.get_bottom_node(node, idx=i, copy=True)
+            input = self.graph.get_input_node(node, idx=i, copy=True)
             if i == 1 and op == 'DetectionOutput':
-                input = self.graph.get_bottom_node(node, idx=i, copy=True)
+                input = self.graph.get_input_node(node, idx=i, copy=True)
                 while input is not None \
                       and input.layer_type != 'Softmax' \
                       and input.layer_type != 'Sigmoid':
-                    input = self.graph.get_bottom_node(input, idx=0, copy=True)
+                    input = self.graph.get_input_node(input, idx=0, copy=True)
                 assert input is not None, 'This kind of DetectionOutput is not supported!'
-                input = self.graph.get_bottom_node(input, idx=0, copy=True)
+                input = self.graph.get_input_node(input, idx=0, copy=True)
             inputs_list.append(self.get_input_name(input))
         kwargs_tmp = copy.deepcopy(kwargs)
         for k, v in kwargs_tmp.items():
@@ -970,7 +970,7 @@ class CaffeOpMapper(OpMapper):
     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_bottom_node(node, idx=0, copy=True)
+        input = self.graph.get_input_node(node, idx=0, copy=True)
         self.paddle_graph.add_layer(
             kernel=op_info,
             inputs={"x": self.get_input_name(input)},

x2paddle/op_mapper/static/tf2paddle/tf_op_mapper.py

@@ -359,7 +359,7 @@ class TFOpMapper(OpMapper):
             kernel_value = kernel.value
             kernel_weight_name = kernel.name.replace('/', '_')
         else:
-            kernel_value = self.decoder.infer_tensor(kernel)
+            kernel_value = self.decoder.infer_tensor(kernel, use_diff_inputs=False)
             if kernel.layer_type == 'Split':
                 kernel_weight_name = "{}_{}_kernel".format(node.name,
                                                            kernel.name)
@@ -781,15 +781,15 @@ class TFOpMapper(OpMapper):
         if strides.layer_type == "Const":
             strides = strides.value.tolist()
         else:
-            strides = self.decoder.infer_shape_tensor(strides)
+            strides = self.decoder.infer_tensor(strides)
         if begin.layer_type == "Const":
             begin = begin.value.tolist()
         else:
-            begin = self.decoder.infer_shape_tensor(begin)
+            begin = self.decoder.infer_tensor(begin)
         if end.layer_type == "Const":
             end = end.value.tolist()
         else:
-            end = self.decoder.infer_shape_tensor(end)
+            end = self.decoder.infer_tensor(end)
 
         assert len(set(strides)) == 1 and strides[
             0] == 1, "Only support strides be 1 in StridedSlice OP"
@@ -897,7 +897,7 @@ class TFOpMapper(OpMapper):
             #                 outputs=[reshape_name],
             #                 shape=shape)
             #             inputs['offsets'] = reshape_name
-            begin = self.decoder.infer_tensor(begin).tolist()
+            begin = self.decoder.infer_tensor(begin, use_diff_inputs=False).tolist()
             attrs['offsets'] = begin
         if size.layer_type == "Const":
             size = size.value.tolist()
@@ -1066,15 +1066,15 @@ class TFOpMapper(OpMapper):
         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])
+            out_shape = self.decoder.infer_tensor(out_shape,
+                                                  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
+            in_shape = self.decoder.infer_tensor(input, use_diff_inputs=False).shape
         k_size = kernel.out_shapes[0]
         if k_size.count(-1) > 2:
-            k_size = self.decoder.infer_tensor(kernel).shape
+            k_size = self.decoder.infer_tensor(input, use_diff_inputs=False).shape
 
         pad_mode = node.get_attr("padding").decode()
         strides = node.get_attr("strides")

x2paddle/optimizer/code_optimizer/hierachical_tree.py

+# -*- coding:UTF-8 -*-
 #   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"

x2paddle/optimizer/code_optimizer/layer_code_generator.py

+# -*- coding:UTF-8 -*-
 #   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"

x2paddle/optimizer/code_optimizer/parameter_tree.py

+# -*- coding:UTF-8 -*-
 #   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"

x2paddle/optimizer/code_optimizer/subgraphs_union.py

+# -*- coding:UTF-8 -*-
 #   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"

x2paddle/optimizer/pass_manager.py

+# -*- coding:UTF-8 -*-
 #   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"

x2paddle/optimizer/pattern_matcher.py

+# -*- coding:UTF-8 -*-
 #   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"