fix

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

@@ -1588,9 +1588,6 @@ class OpSet9():
 
     @print_mapping_info
     def ConvTranspose(self, node):
-        op_name = name_generator("conv", 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_w = self.graph.get_input_node(node, idx=1, copy=True)
         val_b = None
@@ -1604,7 +1601,7 @@ class OpSet9():
         assert 2 <= convnd <= 3, 'only Conv2DTranspose and Conv3DTranspose supported'
         num_in_channels = val_w.out_shapes[0][0]
         num_out_channels = val_w.out_shapes[0][1]
-        paddle_op = 'paddle.nn.Conv{}DTranspose'.format(convnd)
+        paddle_op = 'paddle.nn.functional.conv{}d_transpose'.format(convnd)
 
         num_groups = node.get_attr('group', 1)
         strides = node.get_attr('strides', [1] * convnd)
@@ -1622,37 +1619,21 @@ class OpSet9():
         output_size[1] = (val_x.out_shapes[0][3] - 1
                           ) * strides[1] - 2 * paddings[1] + dilations[1] * (
                               kernel_shape[1] - 1) + 1 + out_padding[1]
+        # Conv2DTranspose缺少output_size，只能在forward里头传进output_size
+        inputs_dict = {'x': val_x if isinstance(val_x, str) else val_x.name,
+                       "weight": val_w.name}
         layer_attrs = {
-            'in_channels': num_in_channels,
-            'out_channels': num_out_channels,
-            'output_size': output_size or None,
-            'kernel_size': kernel_shape,
-            'padding': paddings,
-            'stride': strides,
-            'dilation': dilations,
-            'groups': num_groups,
-            'weight_attr': string(val_w.name),
-            'bias_attr': None if val_b is None else string(val_b.name),
-        }
+            "stride": strides,
+            "dilation": dilations,
+            "padding": paddings,
+            "groups": num_groups,
+            "output_size": node.out_shapes[0][2:]}
+        if val_b is not None:
+            inputs_dict["bias"] = val_b.name
+        else:
+            layer_attrs["bias"] = None
         self.paddle_graph.add_layer(
-            paddle_op, 
-            inputs={"x": val_x.name}, 
-            outputs=layer_outputs, 
+            kernel="paddle.nn.functional.conv2d_transpose",
+            inputs=inputs_dict,
+            outputs=[node.name],
             **layer_attrs)
-#         inputs_dict = {'x': val_x if isinstance(val_x, str) else val_x.name,
-#                        "weight": val_w.name}
-#         layer_attrs = {
-#             "stride": strides,
-#             "dilation": dilations,
-#             "padding": paddings,
-#             "groups": num_groups,
-#             "output_size": node.out_shapes[0][2:]}
-#         if val_b is not None:
-#             inputs_dict["bias"] = val_b.name
-#         else:
-#             layer_attrs["bias"] = None
-#         self.paddle_graph.add_layer(
-#             kernel="paddle.nn.functional.conv2d_transpose",
-#             inputs=inputs_dict,
-#             outputs=[node.name],
-#             **layer_attrs)

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

 from .opset import OpSet9
-from .custom_layer import custom_layers

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

@@ -17,7 +17,6 @@ from x2paddle.core.graph import GraphNode
 from x2paddle.core.fluid_code import Layer
 from x2paddle.core.fluid_code import FluidCode
 from x2paddle.core.util import string
-from x2paddle.op_mapper.static.onnx2paddle.opset9.custom_layer import *
 from functools import reduce
 import numpy as np
 import onnx
@@ -508,33 +507,30 @@ class OpSet9():
 
     @print_mapping_info
     def InstanceNormalization(self, node):
-        op_name = name_generator("instanse_norm", 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_scale = self.graph.get_input_node(node, idx=1, copy=True)
         val_b = self.graph.get_input_node(node, idx=2, copy=True)
         epsilon = node.get_attr('epsilon', 1e-5)
         layer_attrs = {
-            'epsilon': epsilon,
+            'eps': epsilon,
         }
         dim = len(val_x.out_shapes[0])
         if dim ==2 :
-            layer_attrs["data_format"] = "NC"
+            layer_attrs["data_format"] = string("NC")
         elif dim == 3:
-            layer_attrs["data_format"] = "NCL"
+            layer_attrs["data_format"] = string("NCL")
         elif dim == 4:
-            layer_attrs["data_format"] = "NCHW"
+            layer_attrs["data_format"] = string("NCHW")
         elif dim == 5:
-            layer_attrs["data_format"] = "NCDHW"
+            layer_attrs["data_format"] = string("NCDHW")
         else:
             raise Exception("The paddle only support 2D, 3D, 4D or 5D input in InstanceNormalization.")
         self.paddle_graph.add_layer(
-            paddle_op, 
+            "paddle.nn.functional.instance_norm", 
             inputs={"x": val_x.name,
                     "weight": val_scale.name,
                     "bias": val_b.name}, 
-            outputs=layer_outputs, 
+            outputs=[node.name], 
             **layer_attrs)
 
     @print_mapping_info
@@ -1577,7 +1573,7 @@ class OpSet9():
         if val_b is not None:
             layer_inputs["bias"] = val_b.name
         self.paddle_graph.add_layer(
-            kernel="paddle.nn.functional.conv2d_transpose",
+            kernel=paddle_op,
             inputs=layer_inputs,
             outputs=[node.name],
             **layer_attrs)
\ No newline at end of file