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提交 027bfe06 编写于 作者: C Channingss
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paddle2onnx support opset:9,10,11

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x2paddle/convert.py
浏览文件 @ 027bfe06
......@@ -178,7 +178,7 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
return
print("Now translating model from onnx to paddle.")
from x2paddle.op_mapper.onnx_op_mapper import ONNXOpMapper
from x2paddle.op_mapper.onnx2paddle.onnx_op_mapper import ONNXOpMapper
from x2paddle.decoder.onnx_decoder import ONNXDecoder
from x2paddle.optimizer.onnx_optimizer import ONNXOptimizer
model = ONNXDecoder(model_path)
......@@ -192,12 +192,12 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
print("Paddle model and code generated.")
def paddle2onnx(model_path, save_dir, opset):
def paddle2onnx(model_path, save_dir, opset_number):
from x2paddle.decoder.paddle_decoder import PaddleDecoder
from x2paddle.op_mapper.paddle_op_mapper import PaddleOpMapper
from x2paddle.op_mapper.paddle2onnx.paddle_op_mapper import PaddleOpMapper
model = PaddleDecoder(model_path, '__model__', '__params__')
mapper = PaddleOpMapper()
mapper.convert(model.program, save_dir, opset)
mapper.convert(model.program, save_dir, opset_number=opset_number)
def main():
......
x2paddle/op_mapper/onnx_op_mapper.py 已删除 100644 → 0
浏览文件 @ bbc964e8
# 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.
from x2paddle.op_mapper.onnx_opsets.opset9 import OpSet9
from x2paddle.core.op_mapper import OpMapper
from x2paddle.op_mapper.onnx_opsets.custom_layer import *
from x2paddle.decoder.onnx_decoder import ONNXGraph, ONNXGraphNode, ONNXGraphDataNode
class ONNXOpMapper(OpMapper):
def __init__(self, decoder):
super(ONNXOpMapper, self).__init__()
self.support_op_sets = [9, ]
self.default_op_set = 9
self.graph = decoder.graph
self.opset = self.create_opset(decoder)
if not self.op_checker():
raise Exception("Model are not supported yet.")
#mapping op
print("Total nodes: {}".format(
sum([
isinstance(node, ONNXGraphNode)
for name, node in self.graph.node_map.items()
])))
print("Nodes converting ...")
for node_name in self.graph.topo_sort:
node = self.graph.get_node(node_name)
op = node.layer_type
if hasattr(self.opset, op):
func = getattr(self.opset, op)
func(node)
elif op in self.opset.default_op_mapping:
self.opset.directly_map(node)
elif op in custom_layers:
self.opset.deal_custom_layer(node)
elif op in self.opset.elementwise_ops:
self.opset.elementwise_map(node)
print("Nodes converted.")
self.weights = self.opset.weights
self.omit_nodes = self.opset.omit_nodes
self.used_custom_layers = self.opset.used_custom_layers
def op_checker(self):
unsupported_ops = set()
for node_name in self.graph.topo_sort:
node = self.graph.get_node(node_name)
op = node.layer_type
if not hasattr(self.opset, op) and \
op not in self.opset.default_op_mapping and \
op not in custom_layers and \
op not in self.opset.elementwise_ops:
unsupported_ops.add(op)
if len(unsupported_ops) == 0:
return True
else:
print("There are {} ops not supported yet, list as below".format(
len(unsupported_ops)))
for op in unsupported_ops:
print(op)
return False
def create_opset(self, decoder):
run_op_set = self.default_op_set
opset = ''
if decoder.op_set in self.support_op_sets:
opset = 'OpSet' + str(decoder.op_set)
elif decoder.op_set < self.default_op_set:
opset = 'OpSet' + str(self.default_op_set)
else:
for op_set in self.support_op_sets:
if decoder.op_set > op_set:
run_op_set = op_set
else:
break
opset = 'OpSet' + str(run_op_set)
print(
'Now, onnx2paddle support convert onnx model opset_verison {},'
'opset_verison of your onnx model is {}, automatically treated as op_set: {}.'
.format(self.support_op_sets, decoder.op_set, run_op_set))
return eval(opset)(decoder)
x2paddle/op_mapper/onnx_opsets/custom_layer/InstanceNormalization.py 已删除 100644 → 0
浏览文件 @ bbc964e8
# 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.
from .register import register
def InstanceNormalization_shape(input_shape):
return input_shape
def InstanceNormalization_layer(inputs, name=None):
# TODO(lvmengsi@baidu.com): Check the accuracy when using fluid.layers.layer_norm.
epsilon = 1e-5
input_ = inputs[0]
mean = fluid.layers.reduce_mean(input_, dim=[2, 3], keep_dim=True)
var = fluid.layers.reduce_mean(
fluid.layers.square(input_ - mean), dim=[2, 3], keep_dim=True)
if name is not None:
scale_name = name + "_scale"
offset_name = name + "_offset"
scale_param = inputs[1]
offset_param = inputs[2]
scale = fluid.layers.create_parameter(
name=scale_param.name, shape=input_.shape[1:2], dtype="float32")
offset = fluid.layers.create_parameter(
name=offset_param.name, shape=input_.shape[1:2], dtype="float32")
tmp = fluid.layers.elementwise_mul(x=(input_ - mean), y=scale, axis=1)
tmp = tmp / fluid.layers.sqrt(var + epsilon)
tmp = fluid.layers.elementwise_add(tmp, offset, axis=1)
return tmp
def InstanceNormalization_weights(name, data=None):
weights_name = [name + '_scale']
return weights_name
register(
kind='InstanceNormalization',
shape=InstanceNormalization_shape,
layer=InstanceNormalization_layer,
child_func=None,
weights=InstanceNormalization_weights)
x2paddle/op_mapper/onnx_opsets/custom_layer/__init__.py 已删除 100644 → 0
浏览文件 @ bbc964e8
# 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.
from .register import get_registered_layers
#custom layer import begins
from . import InstanceNormalization
#custom layer import ends
custom_layers = get_registered_layers()
def set_args(f, params):
""" set args for function 'f' using the parameters in node.layer.param
Args:
f (function): a python function object
params (object): a object contains attributes needed by f's arguments
Returns:
arg_names (list): a list of argument names
kwargs (dict): a dict contains needed arguments
"""
argc = f.__code__.co_argcount
arg_list = f.__code__.co_varnames[0:argc]
kwargs = {}
for arg_name in arg_list:
if hasattr(params, arg_name) and params is not None:
kwargs[arg_name] = getattr(params, arg_name)
return arg_list, kwargs
def has_layer(layer_type):
""" test whether this layer exists in custom layer
"""
return layer_type in custom_layers
def get_params(layer, layer_type):
import re
if layer_type.lower() == "deconvolution" or layer_type.lower(
) == "convolutiondepthwise":
param_name = '_'.join(('convolution', 'param'))
elif layer_type.lower() == "normalize":
param_name = '_'.join(('norm', 'param'))
elif len(layer_type) - len(re.sub("[A-Z]", "", layer_type)) >= 2:
s = ''
tmp_name = ''
for i, ch in enumerate(layer_type):
if i == 0:
s += ch.lower()
continue
elif ch.isupper() and layer_type[i - 1].islower():
tmp_name += (s + '_')
s = ''
s += ch.lower()
tmp_name += s
param_name = '_'.join((tmp_name, 'param'))
else:
param_name = '_'.join((layer_type.lower(), 'param'))
return getattr(layer, param_name, None)
def compute_output_shape(node):
""" compute the output shape of custom layer
"""
layer_type = node.layer_type
assert layer_type in custom_layers, "layer[%s] not exist in custom layers" % (
layer_type)
shape_func = custom_layers[layer_type]['shape']
layer = node.layer
params = get_params(layer, layer_type)
arg_names, kwargs = set_args(shape_func, params)
input_shape = node.input_shape
return shape_func(input_shape, **kwargs)
def make_custom_layer(node):
""" get the code which implement the custom layer function
"""
layer_type = node.layer_type
assert layer_type in custom_layers, "layer[%s] not exist in custom layers" % (
layer_type)
layer_func = custom_layers[layer_type]['layer']
import inspect
return inspect.getsource(layer_func), layer_func
def make_custom_child_func(node):
""" get the code which implement the custom layer function
"""
layer_type = node.layer_type
child_func = custom_layers[layer_type]['child_func']
if child_func is None:
return None, child_func
import inspect
return inspect.getsource(child_func), child_func
def deal_weights(node, data=None):
""" deal the weights of the custom layer
"""
layer_type = node.layer_type
weights_func = custom_layers[layer_type]['weights']
name = node.layer_name
return weights_func(name, data)
x2paddle/op_mapper/onnx_opsets/custom_layer/register.py 已删除 100644 → 0
浏览文件 @ bbc964e8
# 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.
""" this module provides 'register' for registering customized layers
"""
g_custom_layers = {}
def register(kind, shape, layer, child_func, weights):
""" register a custom layer or a list of custom layers
Args:
@kind (str or list): type name of the layer
@shape (function): a function to generate the shape of layer's output
@layer (function): a function to generate the paddle code of layer
@weights (function): a function to deal with weights data
Returns:
None
"""
assert type(shape).__name__ == 'function', 'shape should be a function'
assert type(layer).__name__ == 'function', 'layer should be a function'
if type(kind) is str:
kind = [kind]
else:
assert type(
kind) is list, 'invalid param "kind" for register, not a list or str'
for k in kind:
assert type(
k) is str, 'invalid param "kind" for register, not a list of str'
assert k not in g_custom_layers, 'this type[%s] has already been registered' % (
k)
g_custom_layers[k] = {
'shape': shape,
'layer': layer,
'child_func': child_func,
'weights': weights
}
def get_registered_layers():
return g_custom_layers
x2paddle/op_mapper/onnx_opsets/opset9.py 已删除 100644 → 0
浏览文件 @ bbc964e8
此差异已折叠。
x2paddle/op_mapper/paddle_custom_layer/im2sequence.py 已删除 100644 → 0
浏览文件 @ bbc964e8
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/paddle_custom_layer/multiclass_nms.py 已删除 100644 → 0
浏览文件 @ bbc964e8
# 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 warnings
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:
warnings.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')
#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'])]))
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=[np.int64(attrs['keep_top_k'])]))
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=[np.int64(attrs['keep_top_k'])]))
# 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)
# Ine 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_concat_final_results = outputs['Out']
node_concat_final_results = onnx.helper.make_node(
'Concat',
inputs=inputs_concat_final_results,
outputs=outputs_concat_final_results,
axis=2)
node_list.append(node_concat_final_results)
return node_list
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