fix onnx

x2paddle/core/program.py

@@ -210,9 +210,12 @@ class PaddleGraph(object):
                 if self.edges_in.get(layer_id, 0) == 0 and self.edges_out.get(
                         layer_id, 0) == 0 and layer.kernel != "prim.assert" \
                         and layer.kernel != "prim.exception" \
-                        and layer.kernel != "prim.warnings" and layer.outputs[0] not in self.outputs:
+                        and layer.kernel != "prim.warnings" \
+                        and layer.outputs[0] not in self.outputs:
                     if layer.kernel == "paddle.to_tensor" and layer.outputs[0] in self.inputs_info:
                         self.inputs_info.pop(layer.outputs[0])
+                    if layer.outputs[0] in self.inputs:
+                        self.inputs.pop(self.inputs.index(layer.outputs[0]))
                     invalid_list.append(layer_id)
         for layer_id in invalid_list:
             self.layers.pop(layer_id)
@@ -355,6 +358,8 @@ class PaddleGraph(object):
             edges_in = self.edges_in.get(layer_id, [])
             edges_out = self.edges_out.get(layer_id, [])
             if len(edges_in) == 0 and len(edges_out) == 0 and layer.outputs[0] not in self.outputs:
+                if layer.outputs[0] in self.inputs:
+                    self.inputs.pop(self.inputs.index(layer.outputs[0]))
                 continue
 
             line = ""

x2paddle/decoder/onnx_decoder.py

@@ -31,6 +31,7 @@ import numpy as np
 from copy import deepcopy
 import logging as _logging
 import os
+import copy
 
 default_op_domain = 'ai.onnx'
 _logger = _logging.getLogger(__name__)
@@ -125,6 +126,17 @@ class ONNXGraphDataNode(GraphNode):
                     shape.append(dim.dim_value)
             out_shapes.append(shape)
             return out_shapes
+        elif isinstance(self.layer, TensorProto):
+            values = self.layer.dims
+            out_shapes = list()
+            shape = list()
+            for dim in values:
+                if dim == 0:
+                    shape.append(-1)
+                else:
+                    shape.append(dim)
+            out_shapes.append(shape)
+            return out_shapes
         else:
             values = self.layer.dims
             out_shapes = list()
@@ -227,8 +239,6 @@ class ONNXGraph(Graph):
         inner_nodes = self.get_inner_nodes()
         for ipt_vi in self.graph.input:
             if ipt_vi.name not in inner_nodes:
-                if len(ipt_vi.type.tensor_type.shape.dim) == 0:
-                    continue
                 self.check_input_shape(ipt_vi)
                 self.place_holder_nodes.append(ipt_vi.name)
 
@@ -289,7 +299,7 @@ class ONNXGraph(Graph):
         #generate topo
         super(ONNXGraph, self).build()
 
-        self.input_nodes = self.place_holder_nodes
+        self.input_nodes = copy.deepcopy(self.place_holder_nodes)
 
     def build_connection(self, layer_name, node):
         """

x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py

@@ -299,6 +299,9 @@ class OpSet9():
         attrs.update({"align_corners": False,
                       "mode": string(mode),
                       "align_mode": 1})
+        val_x_shape = val_x.out_shapes[0]
+        if mode == "linear" and len(val_x_shape) == 4:
+            attrs["mode"] = string("bilinear")
         self.paddle_graph.add_layer(
             kernel="paddle.nn.functional.interpolate",
             inputs=inputs,
@@ -1386,9 +1389,7 @@ class OpSet9():
                 outputs=[output_name])
         else:
             if mode == 'channel' and len(shape_slope) == 1:
-                # paddle params shape need be [1, channel]
                 slope_data = _const_weight_or_none(val_slope)
-                slope_data = np.reshape(slope_data, [1] + shape_slope)
                 self.weights[val_slope.name] = slope_data
                 num_parameters = val_x.out_shapes[0][1]
             else:

x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py

@@ -289,6 +289,9 @@ class OpSet9():
         attrs.update({"align_corners": False,
                  "mode": string(mode),
                  "align_mode": 1})
+        val_x_shape = val_x.out_shapes[0]
+        if mode == "linear" and len(val_x_shape) == 4:
+            attrs["mode"] = string("bilinear")
         self.paddle_graph.add_layer(
             kernel="paddle.nn.functional.interpolate",
             inputs=inputs,
@@ -1323,9 +1326,6 @@ class OpSet9():
 
     @print_mapping_info
     def PRelu(self, node):
-        op_name = name_generator("prelu", self.nn_name2id)
-        output_name = node.name
-        layer_outputs = [op_name, output_name]
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         val_slope = self.graph.get_input_node(node, idx=1, copy=True)
 
@@ -1342,12 +1342,13 @@ class OpSet9():
                 outputs=[node.name],
                 mode="element")
         else:
-            if mode == 'channel' and len(shape_slope) == 1:
-                # paddle params shape need be [1, channel]
-                slope_data = _const_weight_or_none(val_slope)
-                slope_data = np.reshape(slope_data, [1] + shape_slope)
-                self.params[val_slope.name] = slope_data
-
+            if mode == 'channel':
+                if len(shape_slope) > 1:
+                    self.paddle_graph.add_layer(
+                        "paddle.reshape", 
+                        inputs={"x": val_slope.name}, 
+                        outputs=[val_slope.name],
+                        shape=[shape_slope[0]])
             self.paddle_graph.add_layer(
                 "paddle.nn.functional.prelu", 
                 inputs={"x": val_x.name,