Merge pull request #274 from PaddlePaddle/2onnx

2onnx

x2paddle/op_mapper/paddle_op_mapper.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import math
+import sys
 import x2paddle
 import os
 import numpy as np
@@ -77,6 +78,11 @@ class PaddleOpMapper(object):
             'Relu', inputs=op.input('X'), outputs=op.output('Out'))
         return node
 
+    def sigmoid(self, op, block):
+        node = helper.make_node(
+            'Sigmoid', inputs=op.input('X'), outputs=op.output('Out'))
+        return node
+
     def elementwise_add(self, op, block):
         axis = op.attr('axis')
         x_shape = block.var(op.input('X')[0]).shape
@@ -128,12 +134,40 @@ class PaddleOpMapper(object):
         return node
 
     def softmax(self, op, block):
-        node = helper.make_node(
-            'Softmax',
-            inputs=op.input('X'),
-            outputs=op.output('Out'),
-            axis=op.attr('axis'))
-        return node
+        axis = op.attr('axis')
+        shape = block.var(op.output('Out')[0]).shape
+        if axis < 0:
+            axis += len(shape)
+        if axis == len(shape) - 1:
+            node = helper.make_node(
+                'Softmax',
+                inputs=op.input('X'),
+                outputs=op.output('Out'),
+                axis=op.attr('axis'))
+            return node
+        else:
+            perm = [i for i in range(len(shape))]
+            perm[-1] = axis
+            perm[axis] = len(shape) - 1
+            transpose_name0 = self.get_name(op.type, 'transpose')
+            transpose_node0 = helper.make_node(
+                'Transpose',
+                inputs=op.input('X'),
+                outputs=[transpose_name0],
+                perm=perm)
+            softmax_name = self.get_name(op.type, 'softmax')
+            softmax_node = helper.make_node(
+                'Softmax',
+                inputs=[transpose_name0],
+                outputs=[softmax_name],
+                axis=-1)
+            transpose_name1 = self.get_name(op.type, 'transpose')
+            transpose_node1 = helper.make_node(
+                'Transpose',
+                inputs=[softmax_name],
+                outputs=op.output('Out'),
+                perm=perm)
+            return [transpose_node0, softmax_node, transpose_node1]
 
     def scale(self, op, block):
         scale = op.attr('scale')
@@ -261,21 +295,22 @@ class PaddleOpMapper(object):
                 inputs=op.input('X'),
                 outputs=op.output('Out'),
                 axis=op.attr('axis'))
+        return node
 
     def slice(self, op, block):
         axes = op.attr('axes')
         starts = op.attr('starts')
         ends = op.attr('ends')
-        axes_name = get_name(op.type, 'axes')
-        starts_name = get_name(op.type, 'starts')
-        ends_name = get_name(op.type, 'ends')
-
-        axes_node = make_constant_node(axes_name, onnx_pb.TensorProto.INT64,
-                                       axes)
-        starts_node = make_constant_node(starts_name, onnx_pb.TensorProto.INT64,
-                                         starts)
-        ends_node = make_constant_node(ends_name, onnx_pb.TensorProto.INT64,
-                                       ends)
+        axes_name = self.get_name(op.type, 'axes')
+        starts_name = self.get_name(op.type, 'starts')
+        ends_name = self.get_name(op.type, 'ends')
+
+        axes_node = self.make_constant_node(axes_name,
+                                            onnx_pb.TensorProto.INT64, axes)
+        starts_node = self.make_constant_node(starts_name,
+                                              onnx_pb.TensorProto.INT64, starts)
+        ends_node = self.make_constant_node(ends_name,
+                                            onnx_pb.TensorProto.INT64, ends)
         node = helper.make_node(
             "Slice",
             inputs=[op.input('Input')[0], starts_name, ends_name, axes_name],
@@ -288,12 +323,14 @@ class PaddleOpMapper(object):
         dtype = op.attr('dtype')
         shape = op.attr('shape')
         value = np.ones(shape) * value
+        if dtype == 2:
+            value = value.astype('int32')
         node = helper.make_node(
             'Constant',
             inputs=[],
-            outputs=op.attr('Out'),
+            outputs=op.output('Out'),
             value=helper.make_tensor(
-                name=op.attr('Out'),
+                name=op.output('Out')[0],
                 data_type=self.paddle_onnx_dtype_map[dtype],
                 dims=shape,
                 vals=value.tolist()))
@@ -304,28 +341,45 @@ class PaddleOpMapper(object):
             'Transpose',
             inputs=op.input('X'),
             outputs=op.output('Out'),
-            perm=op.attr('perm'))
+            perm=op.attr('axis'))
         return node
 
     def reshape2(self, op, block):
         input_names = op.input_names
-        if 'Shape' in input_names and len(op.input('Shape')) > 0:
+        if len(op.input('ShapeTensor')) > 1:
+            cast_shape_nodes = list()
+            cast_shape_names = list()
+            for i in range(len(op.input('ShapeTensor'))):
+                dim = op.input('ShapeTensor')[i]
+                temp_name = self.get_name(op.type, 'shape.cast')
+                node = helper.make_node(
+                    'Cast',
+                    inputs=[dim],
+                    outputs=[temp_name],
+                    to=onnx_pb.TensorProto.INT64)
+                cast_shape_nodes.append(node)
+                cast_shape_names.append(temp_name)
+
+            temp_name = self.get_name(op.type, 'shape.concat')
+            shape_node = helper.make_node(
+                'Concat', inputs=cast_shape_names, outputs=[temp_name], axis=-1)
             node = helper.make_node(
                 'Reshape',
-                inputs=[op.input('X')[0],
-                        op.input('Shape')[0]],
+                inputs=[op.input('X')[0], temp_name],
                 outputs=op.output('Out'))
+            return cast_shape_nodes + [shape_node, node]
         else:
-            shape = op.attr('shape')
-            shape_name = get_name(op.type, 'shape')
-            shape_node = make_constant_node(shape_name,
-                                            onnxpb.TensorProto.INT64, shape)
+            temp_name = self.get_name(op.type, 'shape.cast')
+            cast_shape_node = helper.make_node(
+                'Cast',
+                inputs=op.input('ShapeTensor'),
+                outputs=[temp_name],
+                to=onnx_pb.TensorProto.INT64)
             node = helper.make_node(
                 'Reshape',
-                inputs=[op.input('X')[0], shape_name],
+                inputs=[op.input('X')[0], temp_name],
                 outputs=op.output('Out'))
-            return [shape_node, node]
-        return node
+            return [cast_shape_node, node]
 
     def dropout(self, op, block):
         dropout_mode = op.attr('dropout_implementation')
@@ -351,24 +405,119 @@ class PaddleOpMapper(object):
             'ReduceMean',
             inputs=op.input('X'),
             outputs=op.output('Out'),
-            axes=op.attr('axes'),
+            axes=op.attr('dim'),
             keepdims=op.attr('keep_dim'))
         return node
 
+    def bilinear_interp(self, op, block):
+        input_names = op.input_names
+        coordinate_transformation_mode = 'half_pixel'
+        shape_dtype = block.var(op.input('OutSize')[0]).dtype
+        if op.attr('align_corners'):
+            coordinate_transformation_mode = 'align_corners'
+        if 'OutSize' in input_names and len(op.input('OutSize')) > 0:
+            roi_node = self.make_constant_node(
+                self.get_name(op.type, 'roi'), onnx_pb.TensorProto.FLOAT,
+                [1, 1, 1, 1, 1, 1, 1, 1])
+            roi_name = self.get_name(op.type, 'roi')
+            roi_node = self.make_constant_node(
+                roi_name, onnx_pb.TensorProto.FLOAT, [1, 1, 1, 1, 1, 1, 1, 1])
+            empty_name = self.get_name(op.type, 'empty')
+            empty_tensor = helper.make_tensor(
+                empty_name,
+                onnx_pb.TensorProto.FLOAT, (0, ),
+                np.array([]).astype('float32'),
+                raw=False)
+            empty_node = helper.make_node(
+                'Constant', [], outputs=[empty_name], value=empty_tensor)
+            shape_name0 = self.get_name(op.type, 'shape')
+            shape_node0 = helper.make_node(
+                'Shape', inputs=op.input('X'), outputs=[shape_name0])
+            starts_name = self.get_name(op.type, 'slice.starts')
+            starts_node = self.make_constant_node(
+                starts_name, onnx_pb.TensorProto.INT64, [0])
+            ends_name = self.get_name(op.type, 'slice.ends')
+            ends_node = self.make_constant_node(ends_name,
+                                                onnx_pb.TensorProto.INT64, [2])
+            shape_name1 = self.get_name(op.type, 'shape')
+            shape_node1 = helper.make_node(
+                'Slice',
+                inputs=[shape_name0, starts_name, ends_name],
+                outputs=[shape_name1])
+            shape_name2 = self.get_name(op.type, "shape.cast")
+            shape_node2 = helper.make_node(
+                'Cast',
+                inputs=op.input('OutSize'),
+                outputs=[shape_name2],
+                to=onnx_pb.TensorProto.INT64)
+            shape_name3 = self.get_name(op.type, "shape.concat")
+            shape_node3 = helper.make_node(
+                'Concat',
+                inputs=[shape_name1, shape_name2],
+                outputs=[shape_name3],
+                axis=0)
+            result_node = helper.make_node(
+                'Resize',
+                inputs=[op.input('X')[0], roi_name, empty_name, shape_name3],
+                outputs=op.output('Out'),
+                mode='linear',
+                coordinate_transformation_mode=coordinate_transformation_mode)
+            return [
+                roi_node, empty_node, shape_node0, starts_node, ends_node,
+                shape_node1, shape_node2, shape_node3, result_node
+            ]
+        elif 'Scale' in input_names and len(op.input('Scale')) > 0:
+            node = helper.make_node(
+                'Resize',
+                inputs=[op.input('X')[0],
+                        op.input('Scale')[0]],
+                outputs=op.output('Out'),
+                mode='linear',
+                coordinate_transformation_mode=coordinate_transformation_mode)
+        else:
+            out_shape = [op.attr('out_h'), op.attr('out_w')]
+            scale = op.attr('scale')
+            if out_shape.count(-1) > 0:
+                scale_name = self.get_name(op.type, 'scale')
+                scale_node = self.make_constant_node(
+                    scale_name, onnx_pb.TensorProto.FLOAT, [1, 1, scale, scale])
+                roi_name = self.get_name(op.type, 'roi')
+                roi_node = self.make_constant_node(roi_name,
+                                                   onnx_pb.TensorProto.FLOAT,
+                                                   [1, 1, 1, 1, 1, 1, 1, 1])
+                node = helper.make_node(
+                    'Resize',
+                    inputs=[op.input('X')[0], roi_name, scale_name],
+                    outputs=op.output('Out'),
+                    mode='nearest',
+                    coordinate_transformation_mode=coordinate_transformation_mode
+                )
+                return [scale_node, roi_node, node]
+            else:
+                raise Exception("Unexpected situation happend")
+        return node
+
     def nearest_interp(self, op, block):
         input_names = op.input_names
+        coordinate_transformation_mode = 'half_pixel'
+        if op.attr('align_corners'):
+            coordinate_transformation_mode = 'align_corners'
         if 'OutSize' in input_names and len(op.input('OutSize')) > 0:
             node = helper.make_node(
                 'Resize',
                 inputs=[op.input('X')[0], '',
                         op.input('OutSize')[0]],
-                outputs=op.output('Out'))
+                outputs=op.output('Out'),
+                mode='nearest',
+                coordinate_transformation_mode=coordinate_transformation_mode)
         elif 'Scale' in input_names and len(op.input('Scale')) > 0:
             node = helper.make_node(
                 'Resize',
                 inputs=[op.input('X')[0],
                         op.input('Scale')[0]],
-                outputs=op.output('Out'))
+                outputs=op.output('Out'),
+                mode='nearest',
+                coordinate_transformation_mode=coordinate_transformation_mode)
         else:
             out_shape = [op.attr('out_h'), op.attr('out_w')]
             scale = op.attr('scale')
@@ -384,7 +533,9 @@ class PaddleOpMapper(object):
                     'Resize',
                     inputs=[op.input('X')[0], roi_name, scale_name],
                     outputs=op.output('Out'),
-                    mode='nearest')
+                    mode='nearest',
+                    coordinate_transformation_mode=coordinate_transformation_mode
+                )
                 return [scale_node, roi_node, node]
             else:
                 raise Exception("Unexpected situation happend")
@@ -449,6 +600,23 @@ class PaddleOpMapper(object):
             elem_type=self.paddle_onnx_dtype_map[var.dtype])
         return tensor_info
 
+    def unsqueeze2(self, op, block):
+        node = helper.make_node(
+            'Unsqueeze',
+            inputs=op.input('X'),
+            outputs=op.output('Out'),
+            axes=op.attr('axes'))
+        return node
+
+    def arg_max(self, op, block):
+        node = helper.make_node(
+            'ArgMax',
+            inputs=op.input('X'),
+            outputs=op.output('Out'),
+            axis=op.attr('axis'),
+            keepdims=0)
+        return node
+
     def convert_weights(self, program):
         var_names = program.global_block().vars
         nodes = list()
@@ -477,9 +645,12 @@ class PaddleOpMapper(object):
 
         unsupported_ops = set()
 
+        print("Translating PaddlePaddle to ONNX...\n")
         for block in program.blocks:
-            for op in block.ops:
-                print('Translating op: {}'.format(op.type))
+            for i, op in enumerate(block.ops):
+                sys.stdout.write(
+                    "\rTotal:{}, Current:{} : {}                   ".format(
+                        len(block.ops), i + 1, op.type))
                 if not hasattr(self, op.type):
                     unsupported_ops.add(op.type)
                     continue
@@ -497,7 +668,7 @@ class PaddleOpMapper(object):
                         op_nodes.append(node)
 
         if len(unsupported_ops) > 0:
-            print("There's {} ops are not supported yet".format(
+            print("\nThere's {} ops are not supported yet".format(
                 len(unsupported_ops)))
             for op in unsupported_ops:
                 print("=========== {} ===========".format(op))
@@ -516,5 +687,5 @@ class PaddleOpMapper(object):
             os.makedirs(save_dir)
         with open(os.path.join(save_dir, 'x2paddle_model.onnx'), 'wb') as f:
             f.write(model.SerializeToString())
-        print("Translated model saved in {}".format(
+        print("\nTranslated model saved in {}".format(
             os.path.join(save_dir, 'x2paddle_model.onnx')))