# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License" # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import sys 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 from x2paddle.op_mapper.paddle2onnx.opset10.opset import OpSet10 class OpSet11(OpSet10): def __init__(self): super(OpSet11, self).__init__() 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 pad2d(self, op, block): x_shape = block.var(op.input('X')[0]).shape paddings = op.attr('paddings') onnx_pads = [] #TODO support pads is Variable if op.attr('data_format') == 'NCHW': pads = [ 0, 0, paddings[0], paddings[2], 0, 0, paddings[1], paddings[3] ] else: pads = [ 0, paddings[0], paddings[2], 0, 0, paddings[1], paddings[3], 0 ] pads_name = self.get_name(op.type, 'pads') pads_node = self.make_constant_node(pads_name, onnx_pb.TensorProto.INT64, pads) constant_value_name = self.get_name(op.type, 'constant_value') constant_value_node = self.make_constant_node(constant_value_name, onnx_pb.TensorProto.FLOAT, op.attr('pad_value')) node = helper.make_node( 'Pad', inputs=op.input('X') + [pads_name, constant_value_name], outputs=op.output('Out'), mode=op.attr('mode')) return [pads_node, constant_value_node, node] def clip(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, op.attr('min')) max_node = self.make_constant_node(max_name, onnx_pb.TensorProto.FLOAT, op.attr('max')) 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 bilinear_interp(self, op, block): input_names = op.input_names coordinate_transformation_mode = '' align_corners = op.attr('align_corners') align_mode = op.attr('align_mode') if align_corners: coordinate_transformation_mode = 'align_corners' elif align_mode == 1: coordinate_transformation_mode = 'asymmetric' else: coordinate_transformation_mode = 'half_pixel' if ('OutSize' in input_names and len(op.input('OutSize')) > 0) or ( 'SizeTensor' in input_names and len(op.input('SizeTensor')) > 0): node_list = list() 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]) node_list.extend([ roi_node, empty_node, shape_node0, starts_node, ends_node, shape_node1 ]) if 'OutSize' in input_names and len(op.input('OutSize')) > 0: cast_shape_name = self.get_name(op.type, "shape.cast") cast_shape_node = helper.make_node( 'Cast', inputs=op.input('OutSize'), outputs=[cast_shape_name], to=onnx_pb.TensorProto.INT64) node_list.append(cast_shape_node) else: concat_shape_name = self.get_name(op.type, "shape.concat") concat_shape_node = helper.make_node( "Concat", inputs=op.input('SizeTensor'), outputs=[concat_shape_name], axis=0) cast_shape_name = self.get_name(op.type, "shape.cast") cast_shape_node = helper.make_node( 'Cast', inputs=[concat_shape_name], outputs=[cast_shape_name], to=onnx_pb.TensorProto.INT64) node_list.extend([concat_shape_node, cast_shape_node]) shape_name3 = self.get_name(op.type, "shape.concat") shape_node3 = helper.make_node( 'Concat', inputs=[shape_name1, cast_shape_name], 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) node_list.extend([shape_node3, result_node]) return node_list 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 = '' align_corners = op.attr('align_corners') if align_corners: coordinate_transformation_mode = 'align_corners' else: coordinate_transformation_mode = 'asymmetric' 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'), 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'), mode='nearest', 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 hard_swish(self, op, block): min_name = self.get_name(op.type, 'min') max_name = self.get_name(op.type, 'max') scale_name = self.get_name(op.type, 'scale') offset_name = self.get_name(op.type, 'offset') 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')) scale_node = self.make_constant_node(scale_name, onnx_pb.TensorProto.FLOAT, op.attr('scale')) offset_node = self.make_constant_node(offset_name, onnx_pb.TensorProto.FLOAT, op.attr('offset')) name0 = self.get_name(op.type, 'add') node0 = helper.make_node( 'Add', inputs=[op.input('X')[0], offset_name], outputs=[name0]) name1 = self.get_name(op.type, 'relu') node1 = helper.make_node( 'Clip', inputs=[name0, min_name, max_name], outputs=[name1], ) name2 = self.get_name(op.type, 'mul') node2 = helper.make_node( 'Mul', inputs=[op.input('X')[0], name1], outputs=[name2]) node3 = helper.make_node( 'Div', inputs=[name2, scale_name], outputs=op.output('Out')) return [ min_node, max_node, scale_node, offset_node, node0, node1, node2, node3 ] def im2sequence(self, op, block): from .paddle_custom_layer.im2sequence import im2sequence return im2sequence(op, block) def yolo_box(self, op, block): from .paddle_custom_layer.yolo_box import yolo_box return yolo_box(op, block) def multiclass_nms(self, op, block): from .paddle_custom_layer.multiclass_nms import multiclass_nms return multiclass_nms(op, block)