Merge pull request #488 from SunAhong1993/pytorch

fix the pytorch interpolate

x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py

@@ -1310,7 +1310,6 @@ def aten_dim(mapper, graph, node):
     """
     scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
-    layer_outputs = [output_name]
     layer_inputs = {}
     inputs_name, inputs_node = mapper._get_inputs_name(node)
     # 获取当前节点输出的list
@@ -1322,9 +1321,9 @@ def aten_dim(mapper, graph, node):
     current_inputs = list(layer_inputs.values())
 
     graph.add_layer(
-        "prim.shape", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
+        "prim.shape", inputs=layer_inputs, outputs=[output_name], scope_name=scope_name)
     graph.add_layer(
-        "prim.len", inputs={"input": output_name}, outputs=layer_outputs, scope_name=scope_name)
+        "prim.len", inputs={"input": output_name}, outputs=[output_name], scope_name=scope_name)
     return current_inputs, current_outputs
 
 
@@ -4512,21 +4511,10 @@ def aten_upsample_bilinear2d(mapper, graph, node):
                             current_outputs, scope_name)
         layer_inputs["align_corners"] = inputs_name[2]
         current_inputs.append(inputs_name[2])
-#     # 处理输入3和4，构造assert
-#     list_layer_inputs = {}
-#     mapper._check_input(graph, inputs_node[3], inputs_name[3], current_outputs, scope_name)
-#     list_layer_inputs["key"] = inputs_name[3]
-#     current_inputs.append(inputs_name[3])
-#     mapper._check_input(graph, inputs_node[4], inputs_name[4], current_outputs, scope_name)
-#     list_layer_inputs["value"] = inputs_name[4]
-#     current_inputs.append(inputs_name[4])
-#     graph.add_layer(
-#         "prim.assert",
-#         inputs=list_layer_inputs,
-#         outputs=[output_name + "_assert"],
-#         scope_name=scope_name,
-#         type="eq")
-    layer_inputs["scale_factor"] = inputs_name[3]
+    if "size" in layer_attrs and layer_attrs["size"] is None:
+        mapper._check_input(graph, inputs_node[3], inputs_name[3],
+                            current_outputs, scope_name)
+        layer_inputs["scale_factor"] = inputs_name[3]
     layer_attrs["align_mode"] = 0
     layer_attrs["mode"] = string("bilinear")
     graph.add_layer(
@@ -4592,7 +4580,10 @@ def aten_upsample_nearest2d(mapper, graph, node):
         block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
         if_layer.add_block(block)
         if_layer.inputs["input-0"] = inputs_name[1]
-    layer_inputs["scale_factor"] = inputs_name[3]
+    if "size" in layer_attrs and layer_attrs["size"] is None:
+        mapper._check_input(graph, inputs_node[3], inputs_name[3],
+                            current_outputs, scope_name)
+        layer_inputs["scale_factor"] = inputs_name[3]
     layer_attrs["align_mode"] = 0
     layer_attrs["mode"] = string("nearest")
     graph.add_layer(

x2paddle/op_mapper/dygraph/pytorch2paddle/prim2code.py

@@ -182,7 +182,7 @@ def prim_equal(layer, indent=1, init_func=[], forward_func=[], layer_id=None, di
 
 
 def prim_exception(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
-    line = "raise RaiseException({})".format(get_value(layer, "input", different_attrs))
+    line = "raise Exception({})".format(get_value(layer, "input", different_attrs))
     forward_func.extend(gen_codes([line], indent=indent))
 
 
@@ -458,10 +458,12 @@ def prim_slice(layer, indent=1, init_func=[], forward_func=[], layer_id=None, di
     forward_func.extend(gen_codes([line], indent=indent))
     
     
-def prim_startswith(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+def prim_startswith(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None, is_return_line=False):
     line = "{} = {}.startswith({})".format(layer.outputs[0],
                                            get_value(layer, "input", different_attrs),
                                            get_value(layer, "start_str", different_attrs))
+    if is_return_line:
+        return line.split(" = ")[1]
     forward_func.extend(gen_codes([line], indent=indent))
 
 
@@ -471,7 +473,7 @@ def prim_str(layer, indent=1, init_func=[], forward_func=[], layer_id=None, diff
 
 
 def prim_sub(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
-    if int(get_value(layer, "alpha", different_attrs)) == 1:
+    if int(float(get_value(layer, "alpha", different_attrs))) == 1:
         line = "{} = {} - {}".format(layer.outputs[0],
                                      get_value(layer, "x", different_attrs), 
                                      get_value(layer, "y", different_attrs))

x2paddle/optimizer/fusion/dygraph/if_fuser.py

@@ -41,13 +41,21 @@ class DygraphIfFuser(FuseBase):
             return 
         for id in graph.edges_in[layer_id]:
             input_layer = graph.layers[id]
+            input_layer_id = id
             if input_layer.outputs == [layer.inputs["input"]]:
                 if input_layer.kernel == "prim.if":
                     matches.pop(layer_id)
                     return
                 input_id = id
                 break
+        if list(layer.inputs.values()).count(input_layer.outputs[0]) > 1 or \
+               (input_layer_id in graph.edges_out and len(graph.edges_out[input_layer_id]) > 1):
+            matches.pop(layer_id)
+            return
         func_name = input_layer.kernel.replace(".", "_")
+        if func_name in ["prim_if", "prim_loop"]:
+            matches.pop(layer_id)
+            return
         from x2paddle.op_mapper.dygraph.pytorch2paddle import prim2code
         func = getattr(prim2code, func_name)
         line = func(input_layer, is_return_line=True)

x2paddle/optimizer/fusion/dygraph/interpolate_bilinear_fuser.py

@@ -186,7 +186,6 @@ class DygraphInterpolateBilinearFuser(FuseBase):
             inputs={
                 "input": "interpolate-input-0",
                 "size": "interpolate-input-3",
-                "scale_factor": gen_name(21)
             },
             outputs=[gen_name(23)])
         pattern_block_block.add_layer(
@@ -295,6 +294,5 @@ class DygraphInterpolateBilinearFuser(FuseBase):
         layer = matches[layers_id[9]]
         new_layer.outputs[0] = layer.outputs[0]
         new_layer.layer_id = layers_id[7]
-        new_layer.inputs.pop("scale_factor")
         new_layer.inputs["size"] = size
         return new_layer