fix

x2paddle/op_mapper/static/caffe2paddle/caffe_op_mapper.py

@@ -89,7 +89,6 @@ class CaffeOpMapper(OpMapper):
             input_shape.append(last_node.output_shape[idx])
 
         node.input_shape = input_shape
-
         func_name = 'shape_' + node.layer_type.lower()
         if is_fluid_op:
             node.output_shape = getattr(caffe_shape, func_name)(node.layer,
@@ -458,7 +457,6 @@ class CaffeOpMapper(OpMapper):
 
     def ReLU(self, node):
         """
-
         :param node:
         :return:
         """
@@ -974,5 +972,4 @@ class CaffeOpMapper(OpMapper):
         self.paddle_graph.add_layer(
             kernel=op_info,
             inputs={"x": self.get_input_name(input)},
-            outputs=[node.layer_name])
-        
\ No newline at end of file
+            outputs=[node.layer_name])
\ No newline at end of file

x2paddle/optimizer/code_optimizer/hierachical_tree.py

@@ -201,7 +201,6 @@ class HierarchicalTree(Tree):
         code_str = gen_layer_code(self.pd_graph, sub_layers, module_name, 
                                                      different_attrs=diff_attrs_column)
         
-#         print(code_str)
         self.codes.append(code_str)
         for sub_layers in sub_layers_list:
             inputs, outputs = get_inputs_outputs(self.pd_graph, sub_layers)
@@ -359,7 +358,7 @@ class HierarchicalTree(Tree):
                 run_func_list.append("    # {}: 形状为{}，类型为{}。".format(k, v[0], v[1]))
             run_func_list.extend(
                 ["    paddle.disable_static()",
-                 "    params, _ = fluid.load_dygraph('{}/model')".format(save_dir),
+                 "    params = paddle.load('{}/model.pdparams')".format(osp.abspath(save_dir)),
                  "    model = {}()".format(self.pd_graph.name),
                  "    model.set_dict(params)",
                  "    model.eval()",
@@ -371,7 +370,12 @@ class HierarchicalTree(Tree):
         self.update_parameters()
         import_list = ["import paddle",
                        "import paddle.fluid as fluid",
-                       "",]
+                       "from paddle.fluid.initializer import Constant",
+                       "from paddle.fluid.param_attr import ParamAttr",
+                       "import math",
+                       "from x2paddle.op_mapper.dygraph.pytorch2paddle " + \
+                                 "import pytorch_custom_layer as x2paddle_nn"
+                       "\n",]
         import_str = "\n".join(import_list)
         if not osp.exists(save_dir):
             os.makedirs(save_dir)

x2paddle/optimizer/code_optimizer/layer_code_generator.py

@@ -29,9 +29,9 @@ NN_KERNEL_NAME = {"paddle.nn.BatchNorm": "bn",
                   "paddle.nn.Tanh": "tanh",
                   "paddle.nn.AvgPool2D": "pool",
                   "paddle.nn.MaxPool2D": "pool",
-                  "paddle.nn.Pad1d": "pad",
-                  "paddle.nn.Pad2d": "pad",
-                  "paddle.nn.Pad3d": "pad",
+                  "paddle.nn.Pad1D": "pad",
+                  "paddle.nn.Pad2D": "pad",
+                  "paddle.nn.Pad3D": "pad",
                   "paddle.nn.Dropout": "dropout",
                   "paddle.nn.GELU": "gelu",
                   "paddle.nn.Hardtanh": "tanh",
@@ -175,9 +175,11 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
                 if layer.kernel.startswith("paddle.nn") and index == 0:
                     continue
                 if not output_name.startswith("x") or output_name in outputs \
-                        or layer.kernel == "prim.assert" or \
-                        layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                        or layer.kernel == "prim.assert":
                     continue
+                elif layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                    if index != 0:
+                        outputs.append(output_name)
                 elif output_name not in outputs:
                     outputs.append(output_name)
             continue
@@ -187,15 +189,22 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
                     if layer.kernel.startswith("paddle.nn") and index == 0 and "functional" not in layer.kernel:
                         continue
                     if not output_name.startswith("x") or output_name in outputs \
-                            or layer.kernel == "prim.assert" or \
-                            layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                            or layer.kernel == "prim.assert":
                         continue
+                    elif layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                        if index != 0:
+                            outputs.append(output_name)
                     else:
                         outputs.append(output_name)
     no_output_count = 0
     for i, (layer_id, layer) in enumerate(sub_layers.items()):
-        if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel):
-            line = "self.{} = {}(".format(layer.outputs[0], layer.kernel)
+        if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel) or \
+                layer.kernel.startswith("custom_layer"):
+            line = "self.{}".format(layer.outputs[0])
+            if layer.kernel.startswith("custom_layer"):
+                line += "= x2paddle_nn.{}(".format(layer.kernel.split(":")[-1])
+            else:
+                line += " = {}(".format(layer.kernel)
             for k, v in layer.attrs.items():
                 key_name = "{}_{}".format(layer.outputs[0], k)
                 if key_name in different_attrs:
@@ -289,7 +298,10 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
                 else:
                     if v not in cur_outputs and v not in inputs:
                         inputs.append(v)
-                    line += "{}={}, ".format(k, v)
+                    if k == "args":
+                        line += v
+                    else:
+                        line += "{}={}, ".format(k, v)
             for k, v in layer.attrs.items():
                 key_name = "{}_{}".format(layer.outputs[0], k)
                 if key_name in different_attrs:

x2paddle/optimizer/code_optimizer/subgraphs_union.py

@@ -50,21 +50,25 @@ def get_inputs_outputs(pd_graph, layers):
     for layer_id, layer in layers.items():
         # 获取输出节点名字
         if layer_id not in pd_graph.edges_out:
-            for output_name in layer.outputs:
+            for index, output_name in enumerate(layer.outputs):
                 if not output_name.startswith("x") or output_name in outputs \
-                        or layer.kernel == "prim.assert" or \
-                        layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                        or layer.kernel == "prim.assert":
                     continue
+                elif layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                        if index != 0:
+                            outputs.append(output_name)
                 elif output_name not in outputs:
                     outputs.append(output_name)
         else:
             for out_layer_id in pd_graph.edges_out[layer_id]:
                 if out_layer_id not in layer_ids:
-                    for output_name in layer.outputs:
+                    for index, output_name in enumerate(layer.outputs):
                         if not output_name.startswith("x") or output_name in outputs \
-                                or layer.kernel == "prim.assert" or \
-                                layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                                or layer.kernel == "prim.assert":
                             continue
+                        elif layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                            if index != 0:
+                                outputs.append(output_name)
                         else:
                             outputs.append(output_name)
         # 获取输入节点名字