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提交 7d8c8b1a 编写于 作者: C Channingss
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rm paddle2onnx code

上级 56697812
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Showing with 12 addition and 4692 deletion
x2paddle/convert.py
浏览文件 @ 7d8c8b1a
......@@ -95,6 +95,7 @@ def arg_parser():
help="define the inputs' shape")
return parser
def tf2paddle(model_path,
save_dir,
without_data_format_optimization,
......@@ -194,8 +195,8 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
print("Paddle model and code generating ...")
mapper.save_inference_model(save_dir, params_merge)
print("Paddle model and code generated.")
def pytorch2paddle(model_path, save_dir, input_shapes):
# check pytorch installation and version
try:
......@@ -236,13 +237,14 @@ def pytorch2paddle(model_path, save_dir, input_shapes):
def paddle2onnx(model_path, save_dir, opset_version=10):
from x2paddle.decoder.paddle_decoder import PaddleDecoder
from x2paddle.op_mapper.paddle2onnx.paddle_op_mapper import PaddleOpMapper
import paddle.fluid as fluid
model = PaddleDecoder(model_path, '__model__', '__params__')
mapper = PaddleOpMapper()
mapper.convert(
model.program,
try:
import paddle2onnx as p2o
except:
print(
"[ERROR] paddle2onnx not installed, use \"pip install paddle2onnx\"")
p2o.convert(
model_path,
save_dir,
scope=fluid.global_scope(),
opset_version=opset_version)
......@@ -310,7 +312,7 @@ def main():
if args.params_merge:
params_merge = True
onnx2paddle(args.model, args.save_dir, params_merge)
elif args.framework == "pytorch":
assert args.model is not None, "--model should be defined while translating pytorch model"
pytorch2paddle(args.model, args.save_dir, args.input_shapes)
......@@ -325,4 +327,4 @@ def main():
if __name__ == "__main__":
main()
\ No newline at end of file
main()
x2paddle/decoder/paddle_decoder.py 已删除 100644 → 0
浏览文件 @ 56697812
# 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 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/paddle2onnx/opset10/opset.py 已删除 100644 → 0
浏览文件 @ 56697812
# 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.opset9.opset import OpSet9
class OpSet10(OpSet9):
def __init__(self):
super(OpSet10, self).__init__()
def slice(self, op, block):
axes = op.attr('axes')
starts = op.attr('starts')
ends = op.attr('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],
outputs=op.output('Out'), )
return [starts_node, ends_node, axes_node, node]
x2paddle/op_mapper/paddle2onnx/opset11/opset.py 已删除 100644 → 0
浏览文件 @ 56697812
# 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'
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])
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()
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], roi_name, 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])
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 [roi_node, 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 = 'half_pixel'
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])
if 'OutSize' in input_names and len(op.input('OutSize')) > 0:
node = helper.make_node(
'Resize',
inputs=[op.input('X')[0], roi_name, 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], roi_name, 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])
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 [roi_node, 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 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)
x2paddle/op_mapper/paddle2onnx/opset11/paddle_custom_layer/multiclass_nms.py 已删除 100644 → 0
浏览文件 @ 56697812
# 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 os
import numpy as np
import paddle.fluid.core as core
import paddle.fluid as fluid
import onnx
import logging
from onnx import helper, onnx_pb
def multiclass_nms(op, block):
"""
Convert the paddle multiclass_nms to onnx op.
This op is get the select boxes from origin boxes.
"""
inputs = dict()
outputs = dict()
attrs = dict()
for name in op.input_names:
inputs[name] = op.input(name)
for name in op.output_names:
outputs[name] = op.output(name)
for name in op.attr_names:
attrs[name] = op.attr(name)
result_name = outputs['Out'][0]
background = attrs['background_label']
normalized = attrs['normalized']
if normalized == False:
logging.warn(
"The parameter normalized of multiclass_nms OP of Paddle is False, which has diff with ONNX." \
" Please set normalized=True in multiclass_nms of Paddle, see doc Q4 in https://github.com/PaddlePaddle/X2Paddle/blob/develop/FAQ.md")
#convert the paddle attribute to onnx tensor
name_score_threshold = [outputs['Out'][0] + "@score_threshold"]
name_iou_threshold = [outputs['Out'][0] + "@iou_threshold"]
name_keep_top_k = [outputs['Out'][0] + '@keep_top_k']
name_keep_top_k_2D = [outputs['Out'][0] + '@keep_top_k_1D']
node_score_threshold = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=name_score_threshold,
value=onnx.helper.make_tensor(
name=name_score_threshold[0] + "@const",
data_type=onnx.TensorProto.FLOAT,
dims=(),
vals=[float(attrs['score_threshold'])]))
node_iou_threshold = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=name_iou_threshold,
value=onnx.helper.make_tensor(
name=name_iou_threshold[0] + "@const",
data_type=onnx.TensorProto.FLOAT,
dims=(),
vals=[float(attrs['nms_threshold'])]))
boxes_num = block.var(outputs['Out'][0]).shape[0]
top_k_value = np.int64(boxes_num if attrs['keep_top_k'] == -1 else attrs['keep_top_k'])
node_keep_top_k = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=name_keep_top_k,
value=onnx.helper.make_tensor(
name=name_keep_top_k[0] + "@const",
data_type=onnx.TensorProto.INT64,
dims=(),
vals=[top_k_value]))
node_keep_top_k_2D = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=name_keep_top_k_2D,
value=onnx.helper.make_tensor(
name=name_keep_top_k_2D[0] + "@const",
data_type=onnx.TensorProto.INT64,
dims=[1, 1],
vals=[top_k_value]))
# the paddle data format is x1,y1,x2,y2
kwargs = {'center_point_box': 0}
name_select_nms = [outputs['Out'][0] + "@select_index"]
node_select_nms= onnx.helper.make_node(
'NonMaxSuppression',
inputs=inputs['BBoxes'] + inputs['Scores'] + name_keep_top_k +\
name_iou_threshold + name_score_threshold,
outputs=name_select_nms)
# step 1 nodes select the nms class
node_list = [
node_score_threshold, node_iou_threshold, node_keep_top_k,
node_keep_top_k_2D, node_select_nms
]
# create some const value to use
name_const_value = [result_name+"@const_0",
result_name+"@const_1",\
result_name+"@const_2",\
result_name+"@const_-1"]
value_const_value = [0, 1, 2, -1]
for name, value in zip(name_const_value, value_const_value):
node = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=[name],
value=onnx.helper.make_tensor(
name=name + "@const",
data_type=onnx.TensorProto.INT64,
dims=[1],
vals=[value]))
node_list.append(node)
# In this code block, we will deocde the raw score data, reshape N * C * M to 1 * N*C*M
# and the same time, decode the select indices to 1 * D, gather the select_indices
outputs_gather_1 = [result_name + "@gather_1"]
node_gather_1 = onnx.helper.make_node(
'Gather',
inputs=name_select_nms + [result_name + "@const_1"],
outputs=outputs_gather_1,
axis=1)
node_list.append(node_gather_1)
outputs_squeeze_gather_1 = [result_name + "@sequeeze_gather_1"]
node_squeeze_gather_1 = onnx.helper.make_node(
'Squeeze',
inputs=outputs_gather_1,
outputs=outputs_squeeze_gather_1,
axes=[1])
node_list.append(node_squeeze_gather_1)
outputs_gather_2 = [result_name + "@gather_2"]
node_gather_2 = onnx.helper.make_node(
'Gather',
inputs=name_select_nms + [result_name + "@const_2"],
outputs=outputs_gather_2,
axis=1)
node_list.append(node_gather_2)
#slice the class is not 0
if background == 0:
outputs_nonzero = [result_name + "@nonzero"]
node_nonzero = onnx.helper.make_node(
'NonZero', inputs=outputs_squeeze_gather_1, outputs=outputs_nonzero)
node_list.append(node_nonzero)
else:
name_thresh = [result_name + "@thresh"]
node_thresh = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=name_thresh,
value=onnx.helper.make_tensor(
name=name_thresh[0] + "@const",
data_type=onnx.TensorProto.INT32,
dims=[1],
vals=[-1]))
node_list.append(node_thresh)
outputs_cast = [result_name + "@cast"]
node_cast = onnx.helper.make_node(
'Cast', inputs=outputs_squeeze_gather_1, outputs=outputs_cast, to=6)
node_list.append(node_cast)
outputs_greater = [result_name + "@greater"]
node_greater = onnx.helper.make_node(
'Greater',
inputs=outputs_cast + name_thresh,
outputs=outputs_greater)
node_list.append(node_greater)
outputs_nonzero = [result_name + "@nonzero"]
node_nonzero = onnx.helper.make_node(
'NonZero', inputs=outputs_greater, outputs=outputs_nonzero)
node_list.append(node_nonzero)
outputs_gather_1_nonzero = [result_name + "@gather_1_nonzero"]
node_gather_1_nonzero = onnx.helper.make_node(
'Gather',
inputs=outputs_gather_1 + outputs_nonzero,
outputs=outputs_gather_1_nonzero,
axis=0)
node_list.append(node_gather_1_nonzero)
outputs_gather_2_nonzero = [result_name + "@gather_2_nonzero"]
node_gather_2_nonzero = onnx.helper.make_node(
'Gather',
inputs=outputs_gather_2 + outputs_nonzero,
outputs=outputs_gather_2_nonzero,
axis=0)
node_list.append(node_gather_2_nonzero)
# reshape scores N * C * M to (N*C*M) * 1
outputs_reshape_scores_rank1 = [result_name + "@reshape_scores_rank1"]
node_reshape_scores_rank1 = onnx.helper.make_node(
"Reshape",
inputs=inputs['Scores'] + [result_name + "@const_-1"],
outputs=outputs_reshape_scores_rank1)
node_list.append(node_reshape_scores_rank1)
# get the shape of scores
outputs_shape_scores = [result_name + "@shape_scores"]
node_shape_scores = onnx.helper.make_node(
'Shape', inputs=inputs['Scores'], outputs=outputs_shape_scores)
node_list.append(node_shape_scores)
# gather the index: 2 shape of scores
outputs_gather_scores_dim1 = [result_name + "@gather_scores_dim1"]
node_gather_scores_dim1 = onnx.helper.make_node(
'Gather',
inputs=outputs_shape_scores + [result_name + "@const_2"],
outputs=outputs_gather_scores_dim1,
axis=0)
node_list.append(node_gather_scores_dim1)
# mul class * M
outputs_mul_classnum_boxnum = [result_name + "@mul_classnum_boxnum"]
node_mul_classnum_boxnum = onnx.helper.make_node(
'Mul',
inputs=outputs_gather_1_nonzero + outputs_gather_scores_dim1,
outputs=outputs_mul_classnum_boxnum)
node_list.append(node_mul_classnum_boxnum)
# add class * M * index
outputs_add_class_M_index = [result_name + "@add_class_M_index"]
node_add_class_M_index = onnx.helper.make_node(
'Add',
inputs=outputs_mul_classnum_boxnum + outputs_gather_2_nonzero,
outputs=outputs_add_class_M_index)
node_list.append(node_add_class_M_index)
# Squeeze the indices to 1 dim
outputs_squeeze_select_index = [result_name + "@squeeze_select_index"]
node_squeeze_select_index = onnx.helper.make_node(
'Squeeze',
inputs=outputs_add_class_M_index,
outputs=outputs_squeeze_select_index,
axes=[0, 2])
node_list.append(node_squeeze_select_index)
# gather the data from flatten scores
outputs_gather_select_scores = [result_name + "@gather_select_scores"]
node_gather_select_scores = onnx.helper.make_node('Gather',
inputs=outputs_reshape_scores_rank1 + \
outputs_squeeze_select_index,
outputs=outputs_gather_select_scores,
axis=0)
node_list.append(node_gather_select_scores)
# get nums to input TopK
outputs_shape_select_num = [result_name + "@shape_select_num"]
node_shape_select_num = onnx.helper.make_node(
'Shape',
inputs=outputs_gather_select_scores,
outputs=outputs_shape_select_num)
node_list.append(node_shape_select_num)
outputs_gather_select_num = [result_name + "@gather_select_num"]
node_gather_select_num = onnx.helper.make_node(
'Gather',
inputs=outputs_shape_select_num + [result_name + "@const_0"],
outputs=outputs_gather_select_num,
axis=0)
node_list.append(node_gather_select_num)
outputs_unsqueeze_select_num = [result_name + "@unsqueeze_select_num"]
node_unsqueeze_select_num = onnx.helper.make_node(
'Unsqueeze',
inputs=outputs_gather_select_num,
outputs=outputs_unsqueeze_select_num,
axes=[0])
node_list.append(node_unsqueeze_select_num)
outputs_concat_topK_select_num = [result_name + "@conat_topK_select_num"]
node_conat_topK_select_num = onnx.helper.make_node(
'Concat',
inputs=outputs_unsqueeze_select_num + name_keep_top_k_2D,
outputs=outputs_concat_topK_select_num,
axis=0)
node_list.append(node_conat_topK_select_num)
outputs_cast_concat_topK_select_num = [
result_name + "@concat_topK_select_num"
]
node_outputs_cast_concat_topK_select_num = onnx.helper.make_node(
'Cast',
inputs=outputs_concat_topK_select_num,
outputs=outputs_cast_concat_topK_select_num,
to=6)
node_list.append(node_outputs_cast_concat_topK_select_num)
# get min(topK, num_select)
outputs_compare_topk_num_select = [result_name + "@compare_topk_num_select"]
node_compare_topk_num_select = onnx.helper.make_node(
'ReduceMin',
inputs=outputs_cast_concat_topK_select_num,
outputs=outputs_compare_topk_num_select,
keepdims=0)
node_list.append(node_compare_topk_num_select)
# unsqueeze the indices to 1D tensor
outputs_unsqueeze_topk_select_indices = [
result_name + "@unsqueeze_topk_select_indices"
]
node_unsqueeze_topk_select_indices = onnx.helper.make_node(
'Unsqueeze',
inputs=outputs_compare_topk_num_select,
outputs=outputs_unsqueeze_topk_select_indices,
axes=[0])
node_list.append(node_unsqueeze_topk_select_indices)
# cast the indices to INT64
outputs_cast_topk_indices = [result_name + "@cast_topk_indices"]
node_cast_topk_indices = onnx.helper.make_node(
'Cast',
inputs=outputs_unsqueeze_topk_select_indices,
outputs=outputs_cast_topk_indices,
to=7)
node_list.append(node_cast_topk_indices)
# select topk scores indices
outputs_topk_select_topk_indices = [result_name + "@topk_select_topk_values",\
result_name + "@topk_select_topk_indices"]
node_topk_select_topk_indices = onnx.helper.make_node(
'TopK',
inputs=outputs_gather_select_scores + outputs_cast_topk_indices,
outputs=outputs_topk_select_topk_indices)
node_list.append(node_topk_select_topk_indices)
# gather topk label, scores, boxes
outputs_gather_topk_scores = [result_name + "@gather_topk_scores"]
node_gather_topk_scores = onnx.helper.make_node(
'Gather',
inputs=outputs_gather_select_scores +
[outputs_topk_select_topk_indices[1]],
outputs=outputs_gather_topk_scores,
axis=0)
node_list.append(node_gather_topk_scores)
outputs_gather_topk_class = [result_name + "@gather_topk_class"]
node_gather_topk_class = onnx.helper.make_node(
'Gather',
inputs=outputs_gather_1_nonzero +
[outputs_topk_select_topk_indices[1]],
outputs=outputs_gather_topk_class,
axis=1)
node_list.append(node_gather_topk_class)
# gather the boxes need to gather the boxes id, then get boxes
outputs_gather_topk_boxes_id = [result_name + "@gather_topk_boxes_id"]
node_gather_topk_boxes_id = onnx.helper.make_node(
'Gather',
inputs=outputs_gather_2_nonzero +
[outputs_topk_select_topk_indices[1]],
outputs=outputs_gather_topk_boxes_id,
axis=1)
node_list.append(node_gather_topk_boxes_id)
# squeeze the gather_topk_boxes_id to 1 dim
outputs_squeeze_topk_boxes_id = [result_name + "@squeeze_topk_boxes_id"]
node_squeeze_topk_boxes_id = onnx.helper.make_node(
'Squeeze',
inputs=outputs_gather_topk_boxes_id,
outputs=outputs_squeeze_topk_boxes_id,
axes=[0, 2])
node_list.append(node_squeeze_topk_boxes_id)
outputs_gather_select_boxes = [result_name + "@gather_select_boxes"]
node_gather_select_boxes = onnx.helper.make_node(
'Gather',
inputs=inputs['BBoxes'] + outputs_squeeze_topk_boxes_id,
outputs=outputs_gather_select_boxes,
axis=1)
node_list.append(node_gather_select_boxes)
# concat the final result
# before concat need to cast the class to float
outputs_cast_topk_class = [result_name + "@cast_topk_class"]
node_cast_topk_class = onnx.helper.make_node(
'Cast',
inputs=outputs_gather_topk_class,
outputs=outputs_cast_topk_class,
to=1)
node_list.append(node_cast_topk_class)
outputs_unsqueeze_topk_scores = [result_name + "@unsqueeze_topk_scores"]
node_unsqueeze_topk_scores = onnx.helper.make_node(
'Unsqueeze',
inputs=outputs_gather_topk_scores,
outputs=outputs_unsqueeze_topk_scores,
axes=[0, 2])
node_list.append(node_unsqueeze_topk_scores)
inputs_concat_final_results = outputs_cast_topk_class + outputs_unsqueeze_topk_scores +\
outputs_gather_select_boxes
outputs_sort_by_socre_results = [result_name + "@concat_topk_scores"]
node_sort_by_socre_results = onnx.helper.make_node(
'Concat',
inputs=inputs_concat_final_results,
outputs=outputs_sort_by_socre_results,
axis=2)
node_list.append(node_sort_by_socre_results)
# select topk classes indices
outputs_squeeze_cast_topk_class = [result_name + "@squeeze_cast_topk_class"]
node_squeeze_cast_topk_class = onnx.helper.make_node(
'Squeeze',
inputs=outputs_cast_topk_class,
outputs=outputs_squeeze_cast_topk_class,
axes=[0, 2])
node_list.append(node_squeeze_cast_topk_class)
outputs_topk_select_classes_indices = [result_name + "@topk_select_topk_classes_scores",\
result_name + "@topk_select_topk_classes_indices"]
node_topk_select_topk_indices = onnx.helper.make_node(
'TopK',
inputs=outputs_squeeze_cast_topk_class + outputs_cast_topk_indices,
outputs=outputs_topk_select_classes_indices,
largest=0)
node_list.append(node_topk_select_topk_indices)
outputs_concat_final_results = outputs['Out']
node_concat_final_results = onnx.helper.make_node(
'Gather',
inputs=outputs_sort_by_socre_results +
[outputs_topk_select_classes_indices[1]],
outputs=outputs_concat_final_results,
axis=1)
node_list.append(node_concat_final_results)
return node_list
x2paddle/op_mapper/paddle2onnx/opset9/paddle_custom_layer/box_coder.py 已删除 100644 → 0
浏览文件 @ 56697812
# 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 sys
import math
import onnx
import warnings
import numpy as np
from functools import partial
from onnx import TensorProto
from onnx.helper import make_node, make_tensor
from onnx import onnx_pb
from paddle.fluid.executor import _fetch_var as fetch_var
from onnx import helper
import paddle.fluid as fluid
import paddle.fluid.core as core
def box_coder(op, block):
"""
In this function, we will use the decode the prior box to target box,
we just use the decode mode to transform this op.
"""
node_list = []
input_names = op.input_names
prior_var = block.var(op.input('PriorBox')[0])
t_size = block.var(op.input('TargetBox')[0]).shape
p_size = prior_var.shape
# get the outout_name
result_name = op.output('OutputBox')[0]
# n is size of batch, m is boxes num of targe_boxes
n = t_size[0]
m = t_size[0]
axis = int(op.attr('axis'))
#norm
norm = bool(op.attr('box_normalized'))
name_slice_x1 = op.output('OutputBox')[0] + "@x1"
name_slice_y1 = op.output('OutputBox')[0] + "@y1"
name_slice_x2 = op.output('OutputBox')[0] + "@x2"
name_slice_y2 = op.output('OutputBox')[0] + "@y2"
#make onnx tensor to save the intermeidate reslut
name_slice_indices = [[op.output('OutputBox')[0] + "@slice_" + str(i)]
for i in range(1, 3)]
node_slice_indices = [None for i in range(1, 3)]
# create the range(0, 4) const data to slice
for i in range(1, 3):
node = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=name_slice_indices[i - 1],
value=onnx.helper.make_tensor(
name=name_slice_indices[i - 1][0] + "@const",
data_type=onnx.TensorProto.FLOAT,
dims=(),
vals=[i]))
node_list.append(node)
# make node split data
name_box_split = [
name_slice_x1, name_slice_y1, name_slice_x2, name_slice_y2
]
split_shape = list(p_size)
split_shape[-1] = 1
node_split_prior_node = onnx.helper.make_node(
'Split', inputs=op.input('PriorBox'), outputs=name_box_split, axis=1)
node_list.append(node_split_prior_node)
# make node get centor node for decode
final_outputs_vars = []
if not norm:
name_centor_w_tmp = [op.output('OutputBox')[0] + "@centor_w_tmp"]
name_centor_h_tmp = [op.output('OutputBox')[0] + "@centor_h_tmp"]
node_centor_w_tmp = None
node_centor_h_tmp = None
name_centor_tmp_list = [name_centor_w_tmp, name_centor_h_tmp]
node_centor_tmp_list = [node_centor_w_tmp, node_centor_h_tmp]
count = 2
for (name, node) in zip(name_centor_tmp_list, node_centor_tmp_list):
node = onnx.helper.make_node('Add',
inputs=[op.output('OutputBox')[0] + "@slice_" + str(1)]\
+ [name_box_split[count]],
outputs=name)
node_list.append(node)
count = count + 1
if not norm:
inputs_sub = [[name_centor_w_tmp[0], name_box_split[0]],
[name_centor_h_tmp[0], name_box_split[1]]]
else:
inputs_sub = [[name_box_split[2], name_box_split[0]],
[name_box_split[3], name_box_split[1]]]
outputs_sub = [result_name + "@pb_w", result_name + "@pb_h"]
for i in range(0, 2):
node = onnx.helper.make_node(
'Sub', inputs=inputs_sub[i], outputs=[outputs_sub[i]])
node_list.append(node)
# according to prior_box height and weight to get centor x, y
name_half_value = [result_name + "@half_value"]
node_half_value = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=name_half_value,
value=onnx.helper.make_tensor(
name=name_slice_indices[i][0] + "@const",
data_type=onnx.TensorProto.FLOAT,
dims=(),
vals=[0.5]))
node_list.append(node_half_value)
outputs_half_wh = [[result_name + "@pb_w_half"],
[result_name + "@pb_h_half"]]
inputs_half_wh = [[result_name + "@pb_w", name_half_value[0]],
[result_name + "@pb_h", name_half_value[0]]]
for i in range(0, 2):
node = onnx.helper.make_node(
'Mul', inputs=inputs_half_wh[i], outputs=outputs_half_wh[i])
node_list.append(node)
inputs_centor_xy = [[outputs_half_wh[0][0], name_slice_x1],
[outputs_half_wh[1][0], name_slice_y1]]
outputs_centor_xy = [[result_name + "@pb_x"], [result_name + "@pb_y"]]
# final calc the centor x ,y
for i in range(0, 2):
node = onnx.helper.make_node(
'Add', inputs=inputs_centor_xy[i], outputs=outputs_centor_xy[i])
node_list.append(node)
# reshape the data
shape = (1, split_shape[0]) if axis == 0 else (split_shape[0], 1)
# need to reshape the data
inputs_transpose_pb = [
[result_name + "@pb_w"],
[result_name + "@pb_h"],
[result_name + "@pb_x"],
[result_name + "@pb_y"],
]
outputs_transpose_pb = [
[result_name + "@pb_w_transpose"],
[result_name + "@pb_h_transpose"],
[result_name + "@pb_x_transpose"],
[result_name + "@pb_y_transpose"],
]
if axis == 0:
name_reshape_pb = [result_name + "@pb_transpose"]
# reshape the data
for i in range(0, 4):
node = onnx.helper.make_node(
'Transpose',
inputs=inputs_transpose_pb[i],
outputs=outputs_transpose_pb[i])
node_list.append(node)
# decoder the box according to the target_box and variacne
name_variance_raw = [result_name + "@variance_raw"]
name_variance_unsqueeze = [result_name + "@variance_unsqueeze"]
shape = []
# make node to extend the data
var_split_axis = 0
var_split_inputs_name = []
if 'PriorBoxVar' in input_names and len(op.input('PriorBoxVar')) > 0:
if axis == 1:
raise Exception(
"The op box_coder has variable do not support aixs broadcast")
prior_variance_var = block.var(op.input('PriorBoxVar')[0])
axes = []
var_split_inputs_name = [result_name + "@variance_split"]
node = onnx.helper.make_node(
'Transpose',
inputs=op.input('PriorBoxVar'),
outputs=var_split_inputs_name)
node_list.append(node)
var_split_axis = 0
else:
variances = [1.0, 1.0, 1.0, 1.0]
if 'variance' in op.attr and len(op.attr('variance')) > 0:
variances = [float(var) for var in op.attr('variance')]
node_variance_create = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=name_variance_raw,
value=onnx.helper.make_tensor(
name=name_variance_raw[0] + "@const",
data_type=onnx.TensorProto.FLOAT,
dims=[len(variances)],
vals=variances))
node_list.append(node_variance_create)
var_split_axis = 0
var_split_inputs_name = name_variance_raw
# decode the result
outputs_split_variance = [
result_name + "@variance_split" + str(i) for i in range(0, 4)
]
outputs_split_targebox = [
result_name + "@targebox_split" + str(i) for i in range(0, 4)
]
node_split_var = onnx.helper.make_node(
'Split',
inputs=var_split_inputs_name,
outputs=outputs_split_variance,
axis=var_split_axis)
node_split_target = onnx.helper.make_node(
'Split',
inputs=op.input('TargetBox'),
outputs=outputs_split_targebox,
axis=2)
node_list.extend([node_split_var, node_split_target])
outputs_squeeze_targebox = [
result_name + "@targebox_squeeze" + str(i) for i in range(0, 4)
]
for (input_name, output_name) in zip(outputs_split_targebox,
outputs_squeeze_targebox):
node = onnx.helper.make_node(
'Squeeze', inputs=[input_name], outputs=[output_name], axes=[2])
node_list.append(node)
output_shape_step1 = list(t_size)[:-1]
inputs_tb_step1 = [
[outputs_squeeze_targebox[0], outputs_split_variance[0]],
[outputs_squeeze_targebox[1], outputs_split_variance[1]],
[outputs_squeeze_targebox[2], outputs_split_variance[2]],
[outputs_squeeze_targebox[3], outputs_split_variance[3]]
]
outputs_tb_step1 = [[result_name + "@decode_x_step1"],
[result_name + "@decode_y_step1"],
[result_name + "@decode_w_step1"],
[result_name + "@decode_h_step1"]]
for input_step1, output_step_1 in zip(inputs_tb_step1, outputs_tb_step1):
node = onnx.helper.make_node(
'Mul', inputs=input_step1, outputs=output_step_1)
node_list.append(node)
if axis == 0:
inputs_tbxy_step2 = [
[outputs_tb_step1[0][0], outputs_transpose_pb[0][0]],
[outputs_tb_step1[1][0], outputs_transpose_pb[1][0]]
]
else:
inputs_tbxy_step2 = [
[outputs_tb_step1[0][0], inputs_transpose_pb[0][0]],
[outputs_tb_step1[1][0], inputs_transpose_pb[1][0]]
]
outputs_tbxy_step2 = [[result_name + "@decode_x_step2"],
[result_name + "@decode_y_step2"]]
for input_step2, output_step_2 in zip(inputs_tbxy_step2,
outputs_tbxy_step2):
node = onnx.helper.make_node(
'Mul', inputs=input_step2, outputs=output_step_2)
node_list.append(node)
if axis == 0:
inputs_tbxy_step3 = [
[outputs_tbxy_step2[0][0], outputs_transpose_pb[2][0]],
[outputs_tbxy_step2[1][0], outputs_transpose_pb[3][0]]
]
else:
inputs_tbxy_step3 = [
[outputs_tbxy_step2[0][0], inputs_transpose_pb[2][0]],
[outputs_tbxy_step2[1][0], inputs_transpose_pb[3][0]]
]
outputs_tbxy_step3 = [[result_name + "@decode_x_step3"],
[result_name + "@decode_y_step3"]]
for input_step3, output_step_3 in zip(inputs_tbxy_step3,
outputs_tbxy_step3):
node = onnx.helper.make_node(
'Add', inputs=input_step3, outputs=output_step_3)
node_list.append(node)
# deal with width & height
inputs_tbwh_step2 = [outputs_tb_step1[2], outputs_tb_step1[3]]
outputs_tbwh_step2 = [[result_name + "@decode_w_step2"],
[result_name + "@decode_h_step2"]]
for input_name, output_name in zip(inputs_tbwh_step2, outputs_tbwh_step2):
node = onnx.helper.make_node(
'Exp', inputs=input_name, outputs=output_name)
node_list.append(node)
if axis == 0:
inputs_tbwh_step3 = [
[outputs_tbwh_step2[0][0], outputs_transpose_pb[0][0]],
[outputs_tbwh_step2[1][0], outputs_transpose_pb[1][0]]
]
else:
inputs_tbwh_step3 = [
[outputs_tbwh_step2[0][0], inputs_transpose_pb[0][0]],
[outputs_tbwh_step2[1][0], inputs_transpose_pb[1][0]]
]
outputs_tbwh_step3 = [[result_name + "@decode_w_step3"],
[result_name + "@decode_h_step3"]]
for input_name, output_name in zip(inputs_tbwh_step3, outputs_tbwh_step3):
node = onnx.helper.make_node(
'Mul', inputs=input_name, outputs=output_name)
node_list.append(node)
# final step to calc the result, and concat the result to output
# return the output box, [(x1, y1), (x2, y2)]
inputs_half_tbwh_step4 = [
[outputs_tbwh_step3[0][0], result_name + "@slice_2"],
[outputs_tbwh_step3[1][0], result_name + "@slice_2"]
]
outputs_half_tbwh_step4 = [[result_name + "@decode_half_w_step4"],
[result_name + "@decode_half_h_step4"]]
for inputs_name, outputs_name in zip(inputs_half_tbwh_step4,
outputs_half_tbwh_step4):
node = onnx.helper.make_node(
'Div', inputs=inputs_name, outputs=outputs_name)
node_list.append(node)
inputs_output_point1 = [
[outputs_tbxy_step3[0][0], outputs_half_tbwh_step4[0][0]],
[outputs_tbxy_step3[1][0], outputs_half_tbwh_step4[1][0]]
]
outputs_output_point1 = [[result_name + "@ouput_x1"],
[result_name + "@output_y1"]]
for input_name, output_name in zip(inputs_output_point1,
outputs_output_point1):
node = onnx.helper.make_node(
'Sub', inputs=input_name, outputs=output_name)
node_list.append(node)
inputs_output_point2 = [
[outputs_tbxy_step3[0][0], outputs_half_tbwh_step4[0][0]],
[outputs_tbxy_step3[1][0], outputs_half_tbwh_step4[1][0]]
]
outputs_output_point2 = [[result_name + "@ouput_x2"],
[result_name + "@output_y2"]]
for input_name, output_name in zip(inputs_output_point2,
outputs_output_point2):
node = onnx.helper.make_node(
'Add', inputs=input_name, outputs=output_name)
node_list.append(node)
if not norm:
inputs_unnorm_point2 = [
[outputs_output_point2[0][0], result_name + "@slice_1"],
[outputs_output_point2[1][0], result_name + "@slice_1"]
]
outputs_unnorm_point2 = [[result_name + "@ouput_unnorm_x2"],
[result_name + "@ouput_unnorm_y2"]]
for input_name, output_name in zip(inputs_unnorm_point2,
outputs_unnorm_point2):
node = onnx.helper.make_node(
'Sub', inputs=input_name, outputs=output_name)
node_list.append(node)
outputs_output_point2 = outputs_unnorm_point2
outputs_output_point1.extend(outputs_output_point2)
ouputs_points_unsqueeze = [[result_name + "@points_unsqueeze_x1"],
[result_name + "points_unsqueeze_y1"],
[result_name + "points_unsqueeze_x2"],
[result_name + "points_unsqueeze_y2"]]
for input_name, output_name in zip(outputs_output_point1,
ouputs_points_unsqueeze):
node = onnx.helper.make_node(
'Unsqueeze',
inputs=input_name,
outputs=output_name,
axes=[len(output_shape_step1)])
node_list.append(node)
outputs_points_unsqueeze_list = [
output[0] for output in ouputs_points_unsqueeze
]
node_point_final = onnx.helper.make_node(
'Concat',
inputs=outputs_points_unsqueeze_list,
outputs=op.output('OutputBox'),
axis=len(output_shape_step1))
node_list.append(node_point_final)
return node_list
x2paddle/op_mapper/paddle2onnx/opset9/paddle_custom_layer/im2sequence.py 已删除 100644 → 0
浏览文件 @ 56697812
# Copyright (c) 2020 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 onnx
import numpy as np
from onnx import onnx_pb, helper
im2seq_counter = 0
def im2sequence(op, block):
global im2sequence_counter
n, c, h, w = block.var(op.input('X')[0]).shape
assert h > 0 and w > 0, "Only supported fixed input shape for im2sequence operator."
stride_h, stride_w = op.attr('strides')
paddings = op.attr('paddings')
assert op.attr(
'out_stride'
) != 1, "Only out_stride==1 is supported for im2sequence operator."
h = h + paddings[0] + paddings[1]
w = w + paddings[1] + paddings[2]
kernel_h, kernel_w = op.attr('kernels')
out_h = 1 + (h - kernel_h + stride_h - 1) // stride_h
out_w = 1 + (w - kernel_w + stride_w - 1) // stride_w
h_steps = list()
for i in range(out_h):
h_steps.append([i * stride_h, i * stride_h + kernel_h])
w_steps = list()
for i in range(out_w):
w_steps.append([i * stride_w, i * stride_w + kernel_w])
nodes = list()
slice_blocks = list()
for i in range(out_h):
for j in range(out_w):
starts_name = "im2sequence.starts.{}.{}.{}".format(im2seq_counter,
i, j)
starts_tensor = helper.make_tensor(
name=starts_name,
data_type=onnx_pb.TensorProto.INT64,
dims=[4],
vals=[0, 0, h_steps[i][0], w_steps[j][0]])
ends_name = "im2sequence.ends.{}.{}.{}".format(im2seq_counter, i, j)
ends_tensor = helper.make_tensor(
name=ends_name,
data_type=onnx_pb.TensorProto.INT64,
dims=[4],
vals=[999999, 999999, h_steps[i][1], w_steps[j][1]])
starts_node = helper.make_node(
'Constant',
inputs=[],
outputs=[starts_name],
value=starts_tensor)
ends_node = helper.make_node(
'Constant', inputs=[], outputs=[ends_name], value=ends_tensor)
nodes.extend([starts_node, ends_node])
slice_block_name = "im2sequence.slice.{}.{}.{}".format(
im2seq_counter, i, j)
slice_block_node = helper.make_node(
'Slice',
inputs=[op.input('X')[0], starts_name, ends_name],
outputs=[slice_block_name])
flatten_block_name = "im2sequence.flatten.{}.{}.{}".format(
im2seq_counter, i, j)
flatten_block_node = helper.make_node(
"Flatten",
inputs=[slice_block_name],
outputs=[flatten_block_name],
axis=0)
nodes.extend([slice_block_node, flatten_block_node])
slice_blocks.append(flatten_block_name)
concat_block_name = "im2sequence.concat_block.{}".format(im2seq_counter)
# concat_block_node = helper.make_node("Concat", inputs=slice_blocks, outputs=[concat_block_name], axis=0)
concat_block_node = helper.make_node(
"Concat", inputs=slice_blocks, outputs=op.output('Out'), axis=0)
nodes.append(concat_block_node)
print("\n\n==========Importance Notice===========")
print(
"Since im2sequence operator is used in your paddlepaddle model, the translated onnx model only support input data with batch_size=1."
)
print("======================================\n")
return nodes
x2paddle/op_mapper/paddle2onnx/opset9/paddle_custom_layer/multiclass_nms.py 已删除 100644 → 0
浏览文件 @ 56697812
# 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 os
import numpy as np
import paddle.fluid.core as core
import paddle.fluid as fluid
import onnx
import logging
from onnx import helper, onnx_pb
def multiclass_nms(op, block):
"""
Convert the paddle multiclass_nms to onnx op.
This op is get the select boxes from origin boxes.
"""
inputs = dict()
outputs = dict()
attrs = dict()
for name in op.input_names:
inputs[name] = op.input(name)
for name in op.output_names:
outputs[name] = op.output(name)
for name in op.attr_names:
attrs[name] = op.attr(name)
result_name = outputs['Out'][0]
background = attrs['background_label']
normalized = attrs['normalized']
if normalized == False:
logging.warn(
"The parameter normalized of multiclass_nms OP of Paddle is False, which has diff with ONNX." \
" Please set normalized=True in multiclass_nms of Paddle, see doc Q4 in https://github.com/PaddlePaddle/X2Paddle/blob/develop/FAQ.md")
#convert the paddle attribute to onnx tensor
name_score_threshold = [outputs['Out'][0] + "@score_threshold"]
name_iou_threshold = [outputs['Out'][0] + "@iou_threshold"]
name_keep_top_k = [outputs['Out'][0] + '@keep_top_k']
name_keep_top_k_2D = [outputs['Out'][0] + '@keep_top_k_1D']
node_score_threshold = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=name_score_threshold,
value=onnx.helper.make_tensor(
name=name_score_threshold[0] + "@const",
data_type=onnx.TensorProto.FLOAT,
dims=(),
vals=[float(attrs['score_threshold'])]))
node_iou_threshold = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=name_iou_threshold,
value=onnx.helper.make_tensor(
name=name_iou_threshold[0] + "@const",
data_type=onnx.TensorProto.FLOAT,
dims=(),
vals=[float(attrs['nms_threshold'])]))
boxes_num = block.var(outputs['Out'][0]).shape[0]
top_k_value = np.int64(boxes_num if attrs['keep_top_k'] == -1 else attrs['keep_top_k'])
node_keep_top_k = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=name_keep_top_k,
value=onnx.helper.make_tensor(
name=name_keep_top_k[0] + "@const",
data_type=onnx.TensorProto.INT64,
dims=(),
vals=[top_k_value]))
node_keep_top_k_2D = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=name_keep_top_k_2D,
value=onnx.helper.make_tensor(
name=name_keep_top_k_2D[0] + "@const",
data_type=onnx.TensorProto.INT64,
dims=[1, 1],
vals=[top_k_value]))
# the paddle data format is x1,y1,x2,y2
kwargs = {'center_point_box': 0}
name_select_nms = [outputs['Out'][0] + "@select_index"]
node_select_nms= onnx.helper.make_node(
'NonMaxSuppression',
inputs=inputs['BBoxes'] + inputs['Scores'] + name_keep_top_k +\
name_iou_threshold + name_score_threshold,
outputs=name_select_nms)
# step 1 nodes select the nms class
node_list = [
node_score_threshold, node_iou_threshold, node_keep_top_k,
node_keep_top_k_2D, node_select_nms
]
# create some const value to use
name_const_value = [result_name+"@const_0",
result_name+"@const_1",\
result_name+"@const_2",\
result_name+"@const_-1"]
value_const_value = [0, 1, 2, -1]
for name, value in zip(name_const_value, value_const_value):
node = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=[name],
value=onnx.helper.make_tensor(
name=name + "@const",
data_type=onnx.TensorProto.INT64,
dims=[1],
vals=[value]))
node_list.append(node)
# In this code block, we will deocde the raw score data, reshape N * C * M to 1 * N*C*M
# and the same time, decode the select indices to 1 * D, gather the select_indices
outputs_gather_1 = [result_name + "@gather_1"]
node_gather_1 = onnx.helper.make_node(
'Gather',
inputs=name_select_nms + [result_name + "@const_1"],
outputs=outputs_gather_1,
axis=1)
node_list.append(node_gather_1)
outputs_squeeze_gather_1 = [result_name + "@sequeeze_gather_1"]
node_squeeze_gather_1 = onnx.helper.make_node(
'Squeeze',
inputs=outputs_gather_1,
outputs=outputs_squeeze_gather_1,
axes=[1])
node_list.append(node_squeeze_gather_1)
outputs_gather_2 = [result_name + "@gather_2"]
node_gather_2 = onnx.helper.make_node(
'Gather',
inputs=name_select_nms + [result_name + "@const_2"],
outputs=outputs_gather_2,
axis=1)
node_list.append(node_gather_2)
#slice the class is not 0
if background == 0:
outputs_nonzero = [result_name + "@nonzero"]
node_nonzero = onnx.helper.make_node(
'NonZero', inputs=outputs_squeeze_gather_1, outputs=outputs_nonzero)
node_list.append(node_nonzero)
else:
name_thresh = [result_name + "@thresh"]
node_thresh = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=name_thresh,
value=onnx.helper.make_tensor(
name=name_thresh[0] + "@const",
data_type=onnx.TensorProto.INT32,
dims=[1],
vals=[-1]))
node_list.append(node_thresh)
outputs_cast = [result_name + "@cast"]
node_cast = onnx.helper.make_node(
'Cast', inputs=outputs_squeeze_gather_1, outputs=outputs_cast, to=6)
node_list.append(node_cast)
outputs_greater = [result_name + "@greater"]
node_greater = onnx.helper.make_node(
'Greater',
inputs=outputs_cast + name_thresh,
outputs=outputs_greater)
node_list.append(node_greater)
outputs_nonzero = [result_name + "@nonzero"]
node_nonzero = onnx.helper.make_node(
'NonZero', inputs=outputs_greater, outputs=outputs_nonzero)
node_list.append(node_nonzero)
outputs_gather_1_nonzero = [result_name + "@gather_1_nonzero"]
node_gather_1_nonzero = onnx.helper.make_node(
'Gather',
inputs=outputs_gather_1 + outputs_nonzero,
outputs=outputs_gather_1_nonzero,
axis=0)
node_list.append(node_gather_1_nonzero)
outputs_gather_2_nonzero = [result_name + "@gather_2_nonzero"]
node_gather_2_nonzero = onnx.helper.make_node(
'Gather',
inputs=outputs_gather_2 + outputs_nonzero,
outputs=outputs_gather_2_nonzero,
axis=0)
node_list.append(node_gather_2_nonzero)
# reshape scores N * C * M to (N*C*M) * 1
outputs_reshape_scores_rank1 = [result_name + "@reshape_scores_rank1"]
node_reshape_scores_rank1 = onnx.helper.make_node(
"Reshape",
inputs=inputs['Scores'] + [result_name + "@const_-1"],
outputs=outputs_reshape_scores_rank1)
node_list.append(node_reshape_scores_rank1)
# get the shape of scores
outputs_shape_scores = [result_name + "@shape_scores"]
node_shape_scores = onnx.helper.make_node(
'Shape', inputs=inputs['Scores'], outputs=outputs_shape_scores)
node_list.append(node_shape_scores)
# gather the index: 2 shape of scores
outputs_gather_scores_dim1 = [result_name + "@gather_scores_dim1"]
node_gather_scores_dim1 = onnx.helper.make_node(
'Gather',
inputs=outputs_shape_scores + [result_name + "@const_2"],
outputs=outputs_gather_scores_dim1,
axis=0)
node_list.append(node_gather_scores_dim1)
# mul class * M
outputs_mul_classnum_boxnum = [result_name + "@mul_classnum_boxnum"]
node_mul_classnum_boxnum = onnx.helper.make_node(
'Mul',
inputs=outputs_gather_1_nonzero + outputs_gather_scores_dim1,
outputs=outputs_mul_classnum_boxnum)
node_list.append(node_mul_classnum_boxnum)
# add class * M * index
outputs_add_class_M_index = [result_name + "@add_class_M_index"]
node_add_class_M_index = onnx.helper.make_node(
'Add',
inputs=outputs_mul_classnum_boxnum + outputs_gather_2_nonzero,
outputs=outputs_add_class_M_index)
node_list.append(node_add_class_M_index)
# Squeeze the indices to 1 dim
outputs_squeeze_select_index = [result_name + "@squeeze_select_index"]
node_squeeze_select_index = onnx.helper.make_node(
'Squeeze',
inputs=outputs_add_class_M_index,
outputs=outputs_squeeze_select_index,
axes=[0, 2])
node_list.append(node_squeeze_select_index)
# gather the data from flatten scores
outputs_gather_select_scores = [result_name + "@gather_select_scores"]
node_gather_select_scores = onnx.helper.make_node('Gather',
inputs=outputs_reshape_scores_rank1 + \
outputs_squeeze_select_index,
outputs=outputs_gather_select_scores,
axis=0)
node_list.append(node_gather_select_scores)
# get nums to input TopK
outputs_shape_select_num = [result_name + "@shape_select_num"]
node_shape_select_num = onnx.helper.make_node(
'Shape',
inputs=outputs_gather_select_scores,
outputs=outputs_shape_select_num)
node_list.append(node_shape_select_num)
outputs_gather_select_num = [result_name + "@gather_select_num"]
node_gather_select_num = onnx.helper.make_node(
'Gather',
inputs=outputs_shape_select_num + [result_name + "@const_0"],
outputs=outputs_gather_select_num,
axis=0)
node_list.append(node_gather_select_num)
outputs_unsqueeze_select_num = [result_name + "@unsqueeze_select_num"]
node_unsqueeze_select_num = onnx.helper.make_node(
'Unsqueeze',
inputs=outputs_gather_select_num,
outputs=outputs_unsqueeze_select_num,
axes=[0])
node_list.append(node_unsqueeze_select_num)
outputs_concat_topK_select_num = [result_name + "@conat_topK_select_num"]
node_conat_topK_select_num = onnx.helper.make_node(
'Concat',
inputs=outputs_unsqueeze_select_num + name_keep_top_k_2D,
outputs=outputs_concat_topK_select_num,
axis=0)
node_list.append(node_conat_topK_select_num)
outputs_cast_concat_topK_select_num = [
result_name + "@concat_topK_select_num"
]
node_outputs_cast_concat_topK_select_num = onnx.helper.make_node(
'Cast',
inputs=outputs_concat_topK_select_num,
outputs=outputs_cast_concat_topK_select_num,
to=6)
node_list.append(node_outputs_cast_concat_topK_select_num)
# get min(topK, num_select)
outputs_compare_topk_num_select = [result_name + "@compare_topk_num_select"]
node_compare_topk_num_select = onnx.helper.make_node(
'ReduceMin',
inputs=outputs_cast_concat_topK_select_num,
outputs=outputs_compare_topk_num_select,
keepdims=0)
node_list.append(node_compare_topk_num_select)
# unsqueeze the indices to 1D tensor
outputs_unsqueeze_topk_select_indices = [
result_name + "@unsqueeze_topk_select_indices"
]
node_unsqueeze_topk_select_indices = onnx.helper.make_node(
'Unsqueeze',
inputs=outputs_compare_topk_num_select,
outputs=outputs_unsqueeze_topk_select_indices,
axes=[0])
node_list.append(node_unsqueeze_topk_select_indices)
# cast the indices to INT64
outputs_cast_topk_indices = [result_name + "@cast_topk_indices"]
node_cast_topk_indices = onnx.helper.make_node(
'Cast',
inputs=outputs_unsqueeze_topk_select_indices,
outputs=outputs_cast_topk_indices,
to=7)
node_list.append(node_cast_topk_indices)
# select topk scores indices
outputs_topk_select_topk_indices = [result_name + "@topk_select_topk_values",\
result_name + "@topk_select_topk_indices"]
node_topk_select_topk_indices = onnx.helper.make_node(
'TopK',
inputs=outputs_gather_select_scores + outputs_cast_topk_indices,
outputs=outputs_topk_select_topk_indices)
node_list.append(node_topk_select_topk_indices)
# gather topk label, scores, boxes
outputs_gather_topk_scores = [result_name + "@gather_topk_scores"]
node_gather_topk_scores = onnx.helper.make_node(
'Gather',
inputs=outputs_gather_select_scores +
[outputs_topk_select_topk_indices[1]],
outputs=outputs_gather_topk_scores,
axis=0)
node_list.append(node_gather_topk_scores)
outputs_gather_topk_class = [result_name + "@gather_topk_class"]
node_gather_topk_class = onnx.helper.make_node(
'Gather',
inputs=outputs_gather_1_nonzero +
[outputs_topk_select_topk_indices[1]],
outputs=outputs_gather_topk_class,
axis=1)
node_list.append(node_gather_topk_class)
# gather the boxes need to gather the boxes id, then get boxes
outputs_gather_topk_boxes_id = [result_name + "@gather_topk_boxes_id"]
node_gather_topk_boxes_id = onnx.helper.make_node(
'Gather',
inputs=outputs_gather_2_nonzero +
[outputs_topk_select_topk_indices[1]],
outputs=outputs_gather_topk_boxes_id,
axis=1)
node_list.append(node_gather_topk_boxes_id)
# squeeze the gather_topk_boxes_id to 1 dim
outputs_squeeze_topk_boxes_id = [result_name + "@squeeze_topk_boxes_id"]
node_squeeze_topk_boxes_id = onnx.helper.make_node(
'Squeeze',
inputs=outputs_gather_topk_boxes_id,
outputs=outputs_squeeze_topk_boxes_id,
axes=[0, 2])
node_list.append(node_squeeze_topk_boxes_id)
outputs_gather_select_boxes = [result_name + "@gather_select_boxes"]
node_gather_select_boxes = onnx.helper.make_node(
'Gather',
inputs=inputs['BBoxes'] + outputs_squeeze_topk_boxes_id,
outputs=outputs_gather_select_boxes,
axis=1)
node_list.append(node_gather_select_boxes)
# concat the final result
# before concat need to cast the class to float
outputs_cast_topk_class = [result_name + "@cast_topk_class"]
node_cast_topk_class = onnx.helper.make_node(
'Cast',
inputs=outputs_gather_topk_class,
outputs=outputs_cast_topk_class,
to=1)
node_list.append(node_cast_topk_class)
outputs_unsqueeze_topk_scores = [result_name + "@unsqueeze_topk_scores"]
node_unsqueeze_topk_scores = onnx.helper.make_node(
'Unsqueeze',
inputs=outputs_gather_topk_scores,
outputs=outputs_unsqueeze_topk_scores,
axes=[0, 2])
node_list.append(node_unsqueeze_topk_scores)
inputs_concat_final_results = outputs_cast_topk_class + outputs_unsqueeze_topk_scores +\
outputs_gather_select_boxes
outputs_sort_by_socre_results = [result_name + "@concat_topk_scores"]
node_sort_by_socre_results = onnx.helper.make_node(
'Concat',
inputs=inputs_concat_final_results,
outputs=outputs_sort_by_socre_results,
axis=2)
node_list.append(node_sort_by_socre_results)
# select topk classes indices
outputs_squeeze_cast_topk_class = [result_name + "@squeeze_cast_topk_class"]
node_squeeze_cast_topk_class = onnx.helper.make_node(
'Squeeze',
inputs=outputs_cast_topk_class,
outputs=outputs_squeeze_cast_topk_class,
axes=[0, 2])
node_list.append(node_squeeze_cast_topk_class)
outputs_neg_squeeze_cast_topk_class = [
result_name + "@neg_squeeze_cast_topk_class"
]
node_neg_squeeze_cast_topk_class = onnx.helper.make_node(
'Neg',
inputs=outputs_squeeze_cast_topk_class,
outputs=outputs_neg_squeeze_cast_topk_class)
node_list.append(node_neg_squeeze_cast_topk_class)
outputs_topk_select_classes_indices = [result_name + "@topk_select_topk_classes_scores",\
result_name + "@topk_select_topk_classes_indices"]
node_topk_select_topk_indices = onnx.helper.make_node(
'TopK',
inputs=outputs_neg_squeeze_cast_topk_class + outputs_cast_topk_indices,
outputs=outputs_topk_select_classes_indices)
node_list.append(node_topk_select_topk_indices)
outputs_concat_final_results = outputs['Out']
node_concat_final_results = onnx.helper.make_node(
'Gather',
inputs=outputs_sort_by_socre_results +
[outputs_topk_select_classes_indices[1]],
outputs=outputs_concat_final_results,
axis=1)
node_list.append(node_concat_final_results)
return node_list
x2paddle/op_mapper/paddle2onnx/opset9/paddle_custom_layer/prior_box.py 已删除 100644 → 0
浏览文件 @ 56697812
# 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 sys
import math
import onnx
import warnings
import numpy as np
from functools import partial
from onnx import TensorProto
from onnx.helper import make_node, make_tensor
from onnx import onnx_pb
from paddle.fluid.executor import _fetch_var as fetch_var
from onnx import helper
import paddle.fluid as fluid
import paddle.fluid.core as core
def ExpandAspectRations(input_aspect_ratior, flip):
expsilon = 1e-6
output_ratios = [1.0]
for input_ratio in input_aspect_ratior:
already_exis = False
for output_ratio in output_ratios:
if abs(input_ratio - output_ratio) < expsilon:
already_exis = True
break
if already_exis == False:
output_ratios.append(input_ratio)
if flip:
output_ratios.append(1.0 / input_ratio)
return output_ratios
def prior_box(op, block):
"""
In this function, use the attribute to get the prior box, because we do not use
the image data and feature map, wo could the python code to create the varaible,
and to create the onnx tensor as output.
"""
flip = bool(op.attr('flip'))
clip = bool(op.attr('clip'))
min_max_aspect_ratios_order = bool(op.attr('min_max_aspect_ratios_order'))
min_sizes = [float(size) for size in op.attr('min_sizes')]
max_sizes = [float(size) for size in op.attr('max_sizes')]
if isinstance(op.attr('aspect_ratios'), list):
aspect_ratios = [float(ratio) for ratio in op.attr('aspect_ratios')]
else:
aspect_ratios = [float(op.attr('aspect_ratios'))]
variances = [float(var) for var in op.attr('variances')]
# set min_max_aspect_ratios_order = false
output_ratios = ExpandAspectRations(aspect_ratios, flip)
step_w = float(op.attr('step_w'))
step_h = float(op.attr('step_h'))
offset = float(op.attr('offset'))
input_shape = block.var(op.input('Input')[0]).shape
image_shape = block.var(op.input('Image')[0]).shape
img_width = image_shape[3]
img_height = image_shape[2]
feature_width = input_shape[3]
feature_height = input_shape[2]
step_width = 1.0
step_height = 1.0
if step_w == 0.0 or step_h == 0.0:
step_w = float(img_width / feature_width)
step_h = float(img_height / feature_height)
num_priors = len(output_ratios) * len(min_sizes)
if len(max_sizes) > 0:
num_priors += len(max_sizes)
out_dim = (feature_height, feature_width, num_priors, 4)
out_boxes = np.zeros(out_dim).astype('float32')
out_var = np.zeros(out_dim).astype('float32')
idx = 0
for h in range(feature_height):
for w in range(feature_width):
c_x = (w + offset) * step_w
c_y = (h + offset) * step_h
idx = 0
for s in range(len(min_sizes)):
min_size = min_sizes[s]
if not min_max_aspect_ratios_order:
# rest of priors
for r in range(len(output_ratios)):
ar = output_ratios[r]
c_w = min_size * math.sqrt(ar) / 2
c_h = (min_size / math.sqrt(ar)) / 2
out_boxes[h, w, idx, :] = [
(c_x - c_w) / img_width, (c_y - c_h) / img_height,
(c_x + c_w) / img_width, (c_y + c_h) / img_height
]
idx += 1
if len(max_sizes) > 0:
max_size = max_sizes[s]
# second prior: aspect_ratio = 1,
c_w = c_h = math.sqrt(min_size * max_size) / 2
out_boxes[h, w, idx, :] = [
(c_x - c_w) / img_width, (c_y - c_h) / img_height,
(c_x + c_w) / img_width, (c_y + c_h) / img_height
]
idx += 1
else:
c_w = c_h = min_size / 2.
out_boxes[h, w, idx, :] = [
(c_x - c_w) / img_width, (c_y - c_h) / img_height,
(c_x + c_w) / img_width, (c_y + c_h) / img_height
]
idx += 1
if len(max_sizes) > 0:
max_size = max_sizes[s]
# second prior: aspect_ratio = 1,
c_w = c_h = math.sqrt(min_size * max_size) / 2
out_boxes[h, w, idx, :] = [
(c_x - c_w) / img_width, (c_y - c_h) / img_height,
(c_x + c_w) / img_width, (c_y + c_h) / img_height
]
idx += 1
# rest of priors
for r in range(len(output_ratios)):
ar = output_ratios[r]
if abs(ar - 1.) < 1e-6:
continue
c_w = min_size * math.sqrt(ar) / 2
c_h = (min_size / math.sqrt(ar)) / 2
out_boxes[h, w, idx, :] = [
(c_x - c_w) / img_width, (c_y - c_h) / img_height,
(c_x + c_w) / img_width, (c_y + c_h) / img_height
]
idx += 1
if clip:
out_boxes = np.clip(out_boxes, 0.0, 1.0)
# set the variance.
out_var = np.tile(variances, (feature_height, feature_width, num_priors, 1))
#make node that
node_boxes = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=op.output('Boxes'),
value=onnx.helper.make_tensor(
name=op.output('Boxes')[0] + "@const",
data_type=onnx.TensorProto.FLOAT,
dims=out_boxes.shape,
vals=out_boxes.flatten()))
node_vars = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=op.output('Variances'),
value=onnx.helper.make_tensor(
name=op.output('Variances')[0] + "@const",
data_type=onnx.TensorProto.FLOAT,
dims=out_var.shape,
vals=out_var.flatten()))
return [node_boxes, node_vars]
x2paddle/op_mapper/paddle2onnx/paddle_op_mapper.py 已删除 100644 → 0
浏览文件 @ 56697812
# 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.opset9.opset import OpSet9
from x2paddle.op_mapper.paddle2onnx.opset10.opset import OpSet10
from x2paddle.op_mapper.paddle2onnx.opset11.opset import OpSet11
class PaddleOpMapper(object):
def __init__(self):
self.support_opsets = [9, 10, 11]
self.default_opset = 10
self.name_counter = dict()
self.op_set = None
def convert(self, program, save_dir, scope=None, opset_version=10):
self.op_set = self.create_opset(opset_version)
weight_nodes = self.op_set.convert_weights(program, scope=scope)
op_nodes = list()
input_nodes = list()
output_nodes = list()
unsupported_ops = set()
print("Translating PaddlePaddle to ONNX...\n")
for block in program.blocks:
for i, op in enumerate(block.ops):
sys.stdout.write("\rTotal:{}, Current:{} : {} ".format(
len(block.ops), i + 1, op.type))
sys.stdout.flush()
if not hasattr(self.op_set, op.type):
unsupported_ops.add(op.type)
continue
if len(unsupported_ops) > 0:
continue
node = getattr(self.op_set, op.type)(op, block)
if op.type == 'feed':
print(node.name)
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("\nThere'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)
opset_imports = [helper.make_opsetid("", opset_version)]
model = helper.make_model(
graph, producer_name='X2Paddle', opset_imports=opset_imports)
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("\nTranslated model saved in {}".format(
os.path.join(save_dir, 'x2paddle_model.onnx')))
def create_opset(self, opset_version=10):
run_opset = self.default_opset
opset = ''
if opset_version in self.support_opsets:
run_opset = opset_version
else:
for support_opset_version in self.support_opsets:
if support_opset_version < opset_version:
run_opset = support_opset_version
else:
break
print(
'Now, onnx2paddle support convert onnx model opset_verison {},'
'opset_verison of your onnx model is {}, automatically treated as op_set: {}.'
.format(self.support_opsets, opset_version, run_opset))
opset = 'OpSet' + str(run_opset)
return eval(opset)()
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