add paddle2onnx

x2paddle/__init__.py

-__version__ = "0.7.1"
+__version__ = "0.7.2"

x2paddle/convert.py

@@ -19,22 +19,26 @@ import sys
 
 def arg_parser():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--model",
+    parser.add_argument(
+        "--model",
         "-m",
         type=_text_type,
         default=None,
         help="define model file path for tensorflow or onnx")
-    parser.add_argument("--prototxt",
+    parser.add_argument(
+        "--prototxt",
         "-p",
         type=_text_type,
         default=None,
         help="prototxt file of caffe model")
-    parser.add_argument("--weight",
+    parser.add_argument(
+        "--weight",
         "-w",
         type=_text_type,
         default=None,
         help="weight file of caffe model")
-    parser.add_argument("--save_dir",
+    parser.add_argument(
+        "--save_dir",
         "-s",
         type=_text_type,
         default=None,
@@ -44,7 +48,9 @@ def arg_parser():
         "-f",
         type=_text_type,
         default=None,
-        help="define which deeplearning framework(tensorflow/caffe/onnx)")
+        help=
+        "define which deeplearning framework(tensorflow/caffe/onnx/paddle2onnx)"
+    )
     parser.add_argument(
         "--caffe_proto",
         "-c",
@@ -52,7 +58,8 @@ def arg_parser():
         default=None,
         help="optional: the .py file compiled by caffe proto file of caffe model"
     )
-    parser.add_argument("--version",
+    parser.add_argument(
+        "--version",
         "-v",
         action="store_true",
         default=False,
@@ -63,12 +70,14 @@ def arg_parser():
         action="store_true",
         default=False,
         help="tf model conversion without data format optimization")
-    parser.add_argument("--define_input_shape",
+    parser.add_argument(
+        "--define_input_shape",
         "-d",
         action="store_true",
         default=False,
         help="define input shape for tf model")
-    parser.add_argument("--params_merge",
+    parser.add_argument(
+        "--params_merge",
         "-pm",
         action="store_true",
         default=False,
@@ -177,6 +186,14 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
     mapper.save_inference_model(save_dir, params_merge)
 
 
+def paddle2onnx(model_path, save_dir):
+    from x2paddle.decoder.paddle_decoder import PaddleDecoder
+    from x2paddle.op_mapper.paddle_op_mapper import PaddleOpMapper
+    model = PaddleDecoder(model_path, '__model__', '__params__')
+    mapper = PaddleOpMapper()
+    mapper.convert(model.program, save_dir)
+
+
 def main():
     if len(sys.argv) < 2:
         print("Use \"x2paddle -h\" to print the help information")
@@ -249,8 +266,14 @@ def main():
         if args.params_merge:
             params_merge = True
         onnx2paddle(args.model, args.save_dir, params_merge)
+
+    elif args.framework == "paddle2onnx":
+        assert args.model is not None, "--model should be defined while translating paddle model to onnx"
+        paddle2onnx(args.model, args.save_dir)
+
     else:
-        raise Exception("--framework only support tensorflow/caffe/onnx now")
+        raise Exception(
+            "--framework only support tensorflow/caffe/onnx/paddle2onnx now")
 
 
 if __name__ == "__main__":

x2paddle/decoder/paddle_decoder.py

+import paddle.fluid as fluid
+
+
+class PaddleDecoder(object):
+    def __init__(self,
+                 model_dir,
+                 model_filename='__model__',
+                 params_filename=None):
+        exe = fluid.Executor(fluid.CPUPlace())
+        [self.program, feed, fetchs] = fluid.io.load_inference_model(
+            model_dir,
+            exe,
+            model_filename=model_filename,
+            params_filename=params_filename)

x2paddle/op_mapper/paddle_op_mapper.py

+import math
+import x2paddle
+import os
+import numpy as np
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+import onnx
+from onnx import helper, onnx_pb
+
+
+class PaddleOpMapper(object):
+    def __init__(self):
+        self.paddle_onnx_dtype_map = {
+            core.VarDesc.VarType.FP32: onnx_pb.TensorProto.FLOAT,
+            core.VarDesc.VarType.FP64: onnx_pb.TensorProto.DOUBLE,
+            core.VarDesc.VarType.INT32: onnx_pb.TensorProto.INT32,
+            core.VarDesc.VarType.INT16: onnx_pb.TensorProto.INT16,
+            core.VarDesc.VarType.INT16: onnx_pb.TensorProto.UINT16,
+            core.VarDesc.VarType.INT64: onnx_pb.TensorProto.INT64,
+            core.VarDesc.VarType.BOOL: onnx_pb.TensorProto.BOOL
+        }
+
+        self.name_counter = dict()
+
+    def get_name(self, op_name, var_name):
+        name = 'p2o.{}.{}'.format(op_name, var_name)
+        if name not in self.name_counter:
+            self.name_counter[name] = 0
+        else:
+            self.name_counter[name] += 1
+        return name + '.{}'.format(self.name_counter[name])
+
+    def make_constant_node(self, name, dtype, value=None):
+        if isinstance(value, list):
+            dims = (len(value), )
+        elif value is None:
+            dims = ()
+            value = []
+        else:
+            dims = ()
+            value = [value]
+        tensor = helper.make_tensor(
+            name=name, data_type=dtype, dims=dims, vals=value)
+        node = helper.make_node(
+            'Constant', inputs=[], outputs=[name], value=tensor)
+        return node
+
+    def conv2d(self, op, block):
+        kernel_shape = block.var(op.input('Filter')[0]).shape
+        node = helper.make_node(
+            'Conv',
+            inputs=op.input('Input') + op.input('Filter'),
+            outputs=op.output('Output'),
+            dilations=op.attr('dilations'),
+            kernel_shape=kernel_shape[-2:],
+            strides=op.attr('strides'),
+            group=op.attr('groups'),
+            pads=op.attr('paddings') + op.attr('paddings'))
+        return node
+
+    def relu(self, op, block):
+        node = helper.make_node(
+            'Relu', 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
+        y_shape = block.var(op.input('Y')[0]).shape
+        if len(y_shape) == 1 and axis == 1:
+            shape_name = self.get_name(op.type, 'shape')
+            shape_value = [1] * len(x_shape)
+            shape_value[axis] = y_shape[0]
+            shape_node = self.make_constant_node(
+                shape_name, onnx_pb.TensorProto.INT64, shape_value)
+            temp_value = self.get_name(op.type, 'temp')
+            y_node = helper.make_node(
+                'Reshape',
+                inputs=[op.input('Y')[0], shape_name],
+                outputs=[temp_value])
+            node = helper.make_node(
+                'Add',
+                inputs=[op.input('X')[0], temp_value],
+                outputs=op.output('Out'))
+            return [shape_node, y_node, node]
+        elif len(x_shape) == len(y_shape):
+            node = helper.make_node(
+                'Add',
+                inputs=[op.input('X')[0], op.input('Y')[0]],
+                outputs=op.output('Out'))
+            return node
+        else:
+            raise Excpetion("Unexpected situation happend in elementwise_add")
+
+    def pool2d(self, op, block):
+        pool_type = {
+            'max': ('MaxPool', 'GlobalMaxPool'),
+            'avg': ('AveragePool', 'GlobalAveragePool')
+        }
+        if op.attr('global_pooling'):
+            node = helper.make_node(
+                pool_type[op.attr('pooling_type')][1],
+                inputs=op.input('X'),
+                outputs=op.output('Out'),
+            )
+        else:
+            node = helper.make_node(
+                pool_type[op.attr('pooling_type')][0],
+                inputs=op.input('X'),
+                outputs=op.output('Out'),
+                kernel_shape=op.attr('ksize'),
+                strides=op.attr('strides'),
+                pads=op.attr('paddings') + op.attr('paddings'))
+        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
+
+    def scale(self, op, block):
+        scale = op.attr('scale')
+        bias = op.attr('bias')
+        if math.fabs(scale - 1.0) < 1e-06 and math.fabs(bias - 0.0) < 1e-06:
+            node = helper.make_node(
+                'Identity', inputs=op.input('X'), outputs=op.output('Out'))
+            return node
+        else:
+            scale_name = self.get_name(op.type, 'scale')
+            bias_name = self.get_name(op.type, 'bias')
+            scale_node = self.make_constant_node(
+                scale_name, onnx_pb.TensorProto.FLOAT, scale)
+            bias_node = self.make_constant_node(bias_name,
+                                                onnx_pb.TensorProto.FLOAT, bias)
+            temp_tensor_name = self.get_name(op.type, 'temporary')
+            if op.attr('bias_after_scale'):
+                node1 = helper.make_node(
+                    'Mul',
+                    inputs=[scale_name, op.input('X')[0]],
+                    outputs=[temp_tensor_name])
+                node2 = helper.make_node(
+                    'Add',
+                    inputs=[bias_name, temp_tensor_name],
+                    outputs=op.output('Out'))
+            else:
+                node1 = helper.make_node(
+                    'Add',
+                    inputs=[bias_name, op.input('X')[0]],
+                    outputs=temp_tensor_name)
+                node2 = helper.make_node(
+                    'Mul',
+                    inputs=[scale_name, temp_tensor_name],
+                    outputs=[op.output('Out')])
+            return [scale_node, bias_node, node1, node2]
+
+    def mul(self, op, block):
+        x_shape = block.var(op.input('X')[0]).shape
+        y_shape = block.var(op.input('Y')[0]).shape
+        out_shape = list(block.var(op.output('Out')[0]).shape)
+        x_num_col_dims = op.attr('x_num_col_dims')
+        y_num_col_dims = op.attr('y_num_col_dims')
+        flatten_x_name = 'flatten_{}'.format(op.input('X')[0])
+        flatten_y_name = 'flatten_{}'.format(op.input('Y')[0])
+        shape_name = 'temp_shape_{}'.format(op.output('Out')[0])
+        temp_out_name = 'temp_{}'.format(op.output('Out')[0])
+        flatten_x = helper.make_node(
+            'Flatten',
+            inputs=op.input('X'),
+            outputs=[flatten_x_name],
+            axis=x_num_col_dims)
+        flatten_y = helper.make_node(
+            'Flatten',
+            inputs=op.input('Y'),
+            outputs=[flatten_y_name],
+            axis=y_num_col_dims)
+        shape_node = self.make_constant_node(
+            shape_name, onnx_pb.TensorProto.INT64, out_shape)
+        node = helper.make_node(
+            'MatMul',
+            inputs=[flatten_x_name, flatten_y_name],
+            outputs=[temp_out_name])
+        reshape_out = helper.make_node(
+            'Reshape',
+            inputs=[temp_out_name, shape_name],
+            outputs=op.output('Out'))
+        return [flatten_x, flatten_y, shape_node, node, reshape_out]
+
+    def batch_norm(self, op, block):
+        kwargs = {
+            'epsilon': op.attr('epsilon'),
+            'momentum': op.attr('momentum')
+        }
+        inputs = op.input('X') + op.input('Scale') + op.input(
+            'Bias') + op.input('Mean') + op.input('Variance')
+        node = helper.make_node(
+            'BatchNormalization',
+            inputs=inputs,
+            outputs=op.output('Y'),
+            **kwargs)
+        return node
+
+    def concat(self, op, block):
+        node = helper.make_node(
+            'Concat',
+            inputs=op.input('X'),
+            outputs=op.output('Out'),
+            axis=op.attr('axis'))
+        return node
+
+    def depthwise_conv2d(self, op, block):
+        return self.conv2d(op, block)
+
+    def relu6(self, op, block):
+        min_name = self.get_name(op.type, 'min')
+        max_name = self.get_name(op.type, 'max')
+        min_node = self.make_constant_node(min_name, onnx_pb.TensorProto.FLOAT,
+                                           0)
+        max_node = self.make_constant_node(max_name, onnx_pb.TensorProto.FLOAT,
+                                           op.attr('threshold'))
+        node = helper.make_node(
+            'Clip',
+            inputs=[op.input('X')[0], min_name, max_name],
+            outputs=op.output('Out'),
+        )
+        return [min_node, max_node, node]
+
+    def shape(self, op, block):
+        node = helper.make_node(
+            'Shape', inputs=op.input('Input'), outputs=op.output('Out'))
+        return node
+
+    def split(self, op, block):
+        sections = op.attr('sections')
+        if len(sections) > 0:
+            node = helper.make_node(
+                'Split',
+                inputs=op.input('X'),
+                outputs=op.output('Out'),
+                axis=op.attr('axis'),
+                split=sections)
+        else:
+            node = helper.make_node(
+                'Split',
+                inputs=op.input('X'),
+                outputs=op.output('Out'),
+                axis=op.attr('axis'))
+
+    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)
+        node = helper.make_node(
+            "Slice",
+            inputs=[op.input('Input')[0], starts_name, ends_name, axes_name],
+            outputs=op.output('Out'),
+        )
+        return [starts_node, ends_node, axes_node, node]
+
+    def fill_constant(self, op, block):
+        value = op.attr('value')
+        dtype = op.attr('dtype')
+        shape = op.attr('shape')
+        value = np.ones(shape) * value
+        node = helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=op.attr('Out'),
+            value=helper.make_tensor(
+                name=op.attr('Out'),
+                data_type=self.paddle_onnx_dtype_map[dtype],
+                dims=shape,
+                vals=value.tolist()))
+        return node
+
+    def transpose2(self, op, block):
+        node = helper.make_node(
+            'Transpose',
+            inputs=op.input('X'),
+            outputs=op.output('Out'),
+            perm=op.attr('perm'))
+        return node
+
+    def reshape2(self, op, block):
+        input_names = op.input_names
+        if 'Shape' in input_names and len(op.input('Shape')) > 0:
+            node = helper.make_node(
+                'Reshape',
+                inputs=[op.input('X')[0],
+                        op.input('Shape')[0]],
+                outputs=op.output('Out'))
+        else:
+            shape = op.attr('shape')
+            shape_name = get_name(op.type, 'shape')
+            shape_node = make_constant_node(shape_name,
+                                            onnxpb.TensorProto.INT64, shape)
+            node = helper.make_node(
+                'Reshape',
+                inputs=[op.input('X')[0], shape_name],
+                outputs=op.output('Out'))
+            return [shape_node, node]
+        return node
+
+    def dropout(self, op, block):
+        dropout_mode = op.attr('dropout_implementation')
+        dropout_prob = op.attr('dropout_prob')
+        if dropout_mode == 'upscale_in_train':
+            node = helper.make_node(
+                'Identity', inputs=op.input('X'), outputs=op.output('Out'))
+            return node
+        elif dropout_mode == 'downgrade_in_infer':
+            scale_name = self.get_name(op.type, 'scale')
+            scale_node = self.make_constant_node(
+                scale_name, onnx_pb.TensorProto.FLOAT, 1 - dropout_prob)
+            node = helper.make_node(
+                "Mul",
+                inputs=[op.input('X')[0], scale_name],
+                outputs=op.output('Out'))
+            return [scale_node, node]
+        else:
+            raise Exception("Unexpected situation happend")
+
+    def reduce_mean(self, op, block):
+        node = helper.make_node(
+            'ReduceMean',
+            inputs=op.input('X'),
+            outputs=op.output('Out'),
+            axes=op.attr('axes'),
+            keepdims=op.attr('keep_dim'))
+        return node
+
+    def nearest_interp(self, op, block):
+        input_names = op.input_names
+        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'))
+        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'))
+        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')
+                return [scale_node, roi_node, node]
+            else:
+                raise Exception("Unexpected situation happend")
+        return node
+
+    def hard_sigmoid(self, op, block):
+        slope = op.attr('slope')
+        offset = op.attr('offset')
+        node = helper.make_node(
+            'HardSigmoid',
+            inputs=op.input('X'),
+            outputs=op.output('Out'),
+            alpha=slope,
+            beta=offset)
+        return node
+
+    def elementwise_mul(self, op, block):
+        axis = op.attr('axis')
+        x_shape = block.var(op.input('X')[0]).shape
+        y_shape = block.var(op.input('Y')[0]).shape
+        if len(y_shape) == 1 and axis == 1:
+            shape_name = self.get_name(op.type, 'shape')
+            shape_value = [1] * len(x_shape)
+            shape_value[axis] = y_shape[0]
+            shape_node = self.make_constant_node(
+                shape_name, onnx_pb.TensorProto.INT64, shape_value)
+            temp_value = self.get_name(op.type, 'temp')
+            y_node = helper.make_node(
+                'Reshape',
+                inputs=[op.input('Y')[0], shape_name],
+                outputs=[temp_value])
+            node = helper.make_node(
+                'Mul',
+                inputs=[op.input('X')[0], temp_value],
+                outputs=op.output('Out'))
+            return [shape_node, y_node, node]
+        elif len(x_shape) == len(y_shape):
+            node = helper.make_node(
+                'Mul',
+                inputs=[op.input('X')[0], op.input('Y')[0]],
+                outputs=op.output('Out'))
+            return node
+        else:
+            raise Excpetion("Unexpected situation happend in elementwise_add")
+        return node
+
+    def feed(self, op, block):
+        name = op.output('Out')[0]
+        var = block.var(name)
+        tensor_info = helper.make_tensor_value_info(
+            name=name,
+            shape=var.shape,
+            elem_type=self.paddle_onnx_dtype_map[var.dtype])
+        return tensor_info
+
+    def fetch(self, op, block):
+        name = op.input('X')[0]
+        var = block.var(name)
+        tensor_info = helper.make_tensor_value_info(
+            name=name,
+            shape=var.shape,
+            elem_type=self.paddle_onnx_dtype_map[var.dtype])
+        return tensor_info
+
+    def convert_weights(self, program):
+        var_names = program.global_block().vars
+        nodes = list()
+        for name in var_names:
+            var = program.global_block().var(name)
+            if name.endswith('feed') or name.endswith('fetch'):
+                continue
+            if not var.persistable:
+                continue
+            weight = np.array(fluid.global_scope().find_var(name).get_tensor())
+            tensor = helper.make_tensor(
+                name=name,
+                dims=var.shape,
+                data_type=self.paddle_onnx_dtype_map[var.dtype],
+                vals=weight.flatten().tolist())
+            node = helper.make_node(
+                'Constant', inputs=[], outputs=[name], value=tensor)
+            nodes.append(node)
+        return nodes
+
+    def convert(self, program, save_dir):
+        weight_nodes = self.convert_weights(program)
+        op_nodes = list()
+        input_nodes = list()
+        output_nodes = list()
+
+        unsupported_ops = set()
+
+        for block in program.blocks:
+            for op in block.ops:
+                print('Translating op: {}'.format(op.type))
+                if not hasattr(self, op.type):
+                    unsupported_ops.add(op.type)
+                    continue
+                if len(unsupported_ops) > 0:
+                    continue
+                node = getattr(self, op.type)(op, block)
+                if op.type == 'feed':
+                    input_nodes.append(node)
+                elif op.type == 'fetch':
+                    output_nodes.append(node)
+                else:
+                    if isinstance(node, list):
+                        op_nodes = op_nodes + node
+                    else:
+                        op_nodes.append(node)
+
+        if len(unsupported_ops) > 0:
+            print("There's {} ops are not supported yet".format(
+                len(unsupported_ops)))
+            for op in unsupported_ops:
+                print("=========== {} ===========".format(op))
+            return
+
+        graph = helper.make_graph(
+            nodes=weight_nodes + op_nodes,
+            name='onnx_model_from_paddle',
+            initializer=[],
+            inputs=input_nodes,
+            outputs=output_nodes)
+        model = helper.make_model(graph, producer_name='X2Paddle')
+        onnx.checker.check_model(model)
+
+        if not os.path.isdir(save_dir):
+            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(
+            os.path.join(save_dir, 'x2paddle_model.onnx')))