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X2Paddle
未验证 提交 f1999b34 编写于 作者: S SunAhong1993 提交者： GitHub
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Merge pull request #8 from PaddlePaddle/develop 

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README.md
浏览文件 @ f1999b34
......@@ -44,24 +44,22 @@ x2paddle --framework=caffe --prototxt=deploy.prototxt --weight=deploy.caffemodel
```
x2paddle --framework=onnx --model=onnx_model.onnx --save_dir=pd_model
```
### Paddle2ONNX
```
# 注意：paddle_infer_model_dir下需包含__model__和__params__两个文件
x2paddle --framework=paddle2onnx --model=paddle_infer_model_dir --save_dir=onnx_model
Paddle2ONNX功能已迁移至新的github: https://github.com/PaddlePaddle/paddle2onnx, 欢迎大家去新的代码仓库查看详细介绍以及新功能。
```
### 参数选项
| 参数 | |
|----------|--------------|
|--framework | 源模型类型 (tensorflow、caffe、onnx、paddle2onnx) |
|--framework | 源模型类型 (tensorflow、caffe、onnx) |
|--prototxt | 当framework为caffe时，该参数指定caffe模型的proto文件路径 |
|--weight | 当framework为caffe时，该参数指定caffe模型的参数文件路径 |
|--save_dir | 指定转换后的模型保存目录路径 |
|--model | 当framework为tensorflow/onnx时，该参数指定tensorflow的pb模型文件或onnx模型路径 |
|--caffe_proto | **[可选]** 由caffe.proto编译成caffe_pb2.py文件的存放路径，当存在自定义Layer时使用，默认为None |
|--without_data_format_optimization | **[可选]** For TensorFlow, 当指定该参数为False时，打开NHWC->NCHW的优化，见[文档Q2](FAQ.md)，默认为True|
|--define_input_shape | **[可选]** For TensorFlow, 当指定该参数时，强制用户输入每个Placeholder的shape，见[文档Q2](FAQ.md) |
|--params_merge | **[可选]** 当指定该参数时，转换完成后，inference_model中的所有模型参数将合并保存为一个文件__params__ |
|--onnx_opset | **[可选]** 当framework为paddle2onnx时，该参数可设置转换为ONNX的OpSet版本，目前支持9、10、11，默认为10 |
......
x2paddle/__init__.py
浏览文件 @ f1999b34
__version__ = "0.8.4"
__version__ = "0.8.8"
from .core.program import PaddleProgram
from .core.program import PaddleGraph
program = PaddleProgram()
name_counter = dict()
def gen_name(op_name, var_name):
name = "{}.{}".format(op_name, var_name)
if name not in name_counter:
name_counter[name] = 0
else:
name_counter[name] += 1
name = name + "." + str(name_counter[name])
return name
program = PaddleGraph()
x2paddle/convert.py
浏览文件 @ f1999b34
......@@ -75,25 +75,24 @@ def arg_parser():
action="store_true",
default=False,
help="define input shape for tf model")
parser.add_argument(
"--onnx_opset",
"-oo",
type=int,
default=10,
help="when paddle2onnx set onnx opset version to export")
parser.add_argument(
"--params_merge",
"-pm",
action="store_true",
default=False,
help="define whether merge the params")
parser.add_argument(
"--input_shapes",
"-is",
action='append',
default=None,
help="define the inputs' shape")
return parser
def tf2paddle(model_path,
save_dir,
without_data_format_optimization,
without_data_format_optimization=False,
define_input_shape=False,
params_merge=False):
# check tensorflow installation and version
......@@ -112,37 +111,24 @@ def tf2paddle(model_path,
"[ERROR] Tensorflow is not installed, use \"pip install tensorflow\"."
)
return
from x2paddle import program
from x2paddle.decoder.tf_decoder import TFDecoder
from x2paddle.op_mapper.tf_op_mapper import TFOpMapper
from x2paddle.op_mapper.tf_op_mapper_nhwc import TFOpMapperNHWC
from x2paddle.optimizer.tf_optimizer import TFOptimizer
from x2paddle.optimizer.tensorflow.bias import BiasOpt
from x2paddle.optimizer.tensorflow.transpose import TransposeOpt
from x2paddle.optimizer.tensorflow.batch_norm import BatchNormOpt
print("Now translating model from tensorflow to paddle.")
model = TFDecoder(model_path, define_input_shape=define_input_shape)
if not without_data_format_optimization:
mapper = TFOpMapper(model)
optimizer = TFOptimizer(mapper)
# neccesary optimization
optimizer.delete_redundance_code()
# optimizer below is experimental
optimizer.optimize_elementwise_op()
optimizer.merge_activation()
optimizer.merge_bias()
optimizer.optimize_sub_graph()
# optimizer.merge_batch_norm()
# optimizer.merge_prelu()
else:
mapper = TFOpMapperNHWC(model)
optimizer = TFOptimizer(mapper)
optimizer.delete_redundance_code()
optimizer.strip_graph()
optimizer.merge_activation()
optimizer.merge_bias()
optimizer.make_nchw_input_output()
optimizer.remove_transpose()
mapper.save_inference_model(save_dir, params_merge)
program.build()
bias_opt = BiasOpt()
transpose_opt = TransposeOpt()
batch_norm_opt = BatchNormOpt()
bias_opt.run(program)
batch_norm_opt.run(program)
transpose_opt.run(program)
program.gen_model(save_dir)
def caffe2paddle(proto, weight, save_dir, caffe_proto, params_merge=False):
......@@ -185,6 +171,7 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
mapper = ONNXOpMapper(model)
print("Model optimizing ...")
optimizer = ONNXOptimizer(mapper)
optimizer.delete_redundance_code()
print("Model optimized.")
print("Paddle model and code generating ...")
......@@ -192,17 +179,43 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
print("Paddle model and code generated.")
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,
save_dir,
scope=fluid.global_scope(),
opset_version=opset_version)
def pytorch2paddle(model_path, save_dir, input_shapes):
# check pytorch installation and version
try:
import torch
version = torch.__version__
ver_part = version.split('.')
print(ver_part)
if int(ver_part[1]) < 5:
print("[ERROR] pytorch>=1.5.0 is required")
return
except:
print(
"[ERROR] Pytorch is not installed, use \"pip install torch==1.5.0 torchvision\"."
)
return
print("Now translating model from pytorch to paddle.")
from x2paddle.decoder.pytorch_decoder import PyTorchDecoder
from x2paddle.op_mapper.pytorch2paddle import pytorch_op_mapper
model = PyTorchDecoder(model_path)
mapper = pytorch_op_mapper.PyTorchOpMapper(model)
mapper.graph.build()
print("Model optimizing ...")
from x2paddle.optimizer.pytorch_optimizer.optimizer import GraphOptimizer
graph_opt = GraphOptimizer()
graph_opt.optimize(mapper.graph)
print("Model optimized.")
if input_shapes is not None:
real_input_shapes = list()
for shape in input_shapes:
sp = shape[1:-1].split(",")
for i, s in enumerate(sp):
sp[i] = int(s)
real_input_shapes.append(sp)
else:
real_input_shapes = None
mapper.graph.gen_model(save_dir, real_input_shapes)
def main():
......@@ -269,12 +282,11 @@ def main():
onnx2paddle(args.model, args.save_dir, params_merge)
elif args.framework == "paddle2onnx":
assert args.model is not None, "--model should be defined while translating paddle model to onnx"
paddle2onnx(args.model, args.save_dir, opset_version=args.onnx_opset)
print("Paddle to ONNX tool has been migrated to the new github: https://github.com/PaddlePaddle/paddle2onnx")
else:
raise Exception(
"--framework only support tensorflow/caffe/onnx/paddle2onnx now")
"--framework only support tensorflow/caffe/onnx/ now")
if __name__ == "__main__":
......
x2paddle/core/program.py
浏览文件 @ f1999b34
此差异已折叠。
x2paddle/decoder/onnx_decoder.py
浏览文件 @ f1999b34
......@@ -18,7 +18,7 @@ from x2paddle.decoder.onnx_shape_inference import SymbolicShapeInference
from onnx.checker import ValidationError
from onnx.checker import check_model
from onnx.utils import polish_model
from onnx import helper
from onnx import helper, shape_inference
from onnx.helper import get_attribute_value, make_attribute
from onnx.shape_inference import infer_shapes
from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
......@@ -141,6 +141,10 @@ class ONNXGraph(Graph):
print("shape inferencing ...")
self.graph = SymbolicShapeInference.infer_shapes(
onnx_model, fixed_input_shape=self.fixed_input_shape)
if self.graph is None:
print('[WARNING] Shape inference by ONNX offical interface.')
onnx_model = shape_inference.infer_shapes(onnx_model)
self.graph = onnx_model.graph
print("shape inferenced.")
self.build()
self.collect_value_infos()
......@@ -346,8 +350,12 @@ class ONNXGraph(Graph):
#if len(value_info['shape']) == 0 or value_info[
# 'dtype'] is None or 0 in value_info['shape']:
# #TODO add node shape inference
shape = value_info['shape']
for idx in range(len(shape)):
if shape[idx] == 0:
shape[idx] = -1
node.out_shapes.append(shape)
node.dtype = value_info['dtype']
node.out_shapes.append(value_info['shape'])
else:
node.out_shapes.append([])
......
x2paddle/decoder/pytorch_decoder.py 0 → 100644
浏览文件 @ f1999b34
# 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 torch
class PyTorchDecoder(object):
def __init__(self, script_path):
self.script = torch.jit.load(script_path)
self.graph = self._optimize_graph(self.script.inlined_graph)
def _optimize_graph(self, graph):
torch._C._jit_pass_constant_propagation(graph)
torch._C._jit_pass_dce(graph)
torch._C._jit_pass_lint(graph)
torch._C._jit_pass_peephole(graph)
torch._C._jit_pass_lint(graph)
torch._C._jit_pass_dce(graph)
torch._C._jit_pass_lint(graph)
torch._C._jit_pass_canonicalize(graph)
torch._C._jit_pass_lint(graph)
torch._C._jit_pass_constant_propagation(graph)
return graph
x2paddle/decoder/tf_decoder.py
浏览文件 @ f1999b34
......@@ -60,7 +60,7 @@ class TFGraphNode(GraphNode):
@property
def dtype(self):
keys = ['dtype', 'Tidx', 'T', 'DstT']
keys = ['dtype', 'T', 'DstT']
for k in keys:
dtype = self.layer.attr[k].type
if dtype > 0:
......@@ -72,6 +72,15 @@ class TFGraphNode(GraphNode):
dtype, self.layer.name))
return self.dtype_map[dtype]
def set_dtype(self, dtype):
dtype_idx = 0
for k, v in self.dtype_map.items():
if v == dtype:
dtype_idx = k
if dtype_idx == 0:
raise Exception("Cannot set dtype of node to '{}'".format(dtype))
self.layer.attr['dtype'].type = dtype_idx
@property
def raw_dtype(self):
keys = ['dtype', 'Tidx', 'T', 'DstT']
......@@ -89,6 +98,12 @@ class TFGraphNode(GraphNode):
field = getattr(attr, attr.WhichOneof('value'))
return tensor_util.MakeNdarray(field)
@property
def name(self):
if hasattr(self, 'index'):
return self.layer_name + "_p{}".format(self.index)
return self.layer_name
def get_attr(self, name):
if name not in self.layer.attr:
return None
......
x2paddle/op_mapper/caffe_op_mapper.py
浏览文件 @ f1999b34
......@@ -467,6 +467,11 @@ class CaffeOpMapper(OpMapper):
data = node.data
assert data is not None, 'The parameter of {} (type is {}) is not set. You need to use python package of caffe to set the default value.'.format(
node.layer_name, node.layer_type)
import paddle
pd_version = paddle.__version__
if pd_version.startswith("1.8.4") or pd_version.startswith("1.8.3"):
self.weights[node.layer_name + '_weights'] = data[0].reshape(1, -1)
else:
self.weights[node.layer_name + '_weights'] = data[0]
attr = {
'mode': string(mode),
......
x2paddle/op_mapper/onnx2paddle/opset9/opset.py
浏览文件 @ f1999b34
......@@ -104,14 +104,6 @@ class OpSet9():
default_op_mapping = {
'Shape': ['shape', ['X'], ['Out']],
'Clip': [
'clip', ['X'], ['Out'], dict(), dict(
min=(np.asarray(
[255, 255, 127, 255], dtype=np.uint8).view(np.float32)[0]),
max=(np.asarray(
[255, 255, 127, 127], dtype=np.uint8).view(np.float32)[0]),
)
],
'Erf': ['erf', ['X'], ['Out']],
'Ceil': ['ceil', ['X'], ['Out']],
'ReduceMean': [
......@@ -357,6 +349,7 @@ class OpSet9():
'Warnning: paddle not support op:resize wiht mode: linear, we use bilinear replace linear'
)
fluid_op = 'resize_bilinear'
attr['align_corners'] = False
node.fluid_code.add_layer(
fluid_op, inputs=inputs, output=node, param_attr=attr)
......@@ -557,8 +550,6 @@ class OpSet9():
def Expand(self, node):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
val_shape = self.graph.get_input_node(node, idx=1, copy=True)
if len(val_shape.outputs) == 1:
self.omit_nodes.append(val_shape.layer_name)
val_x_dtype = val_x.dtype
name_ones = node.layer_name + '_ones'
attr_ones = {
......@@ -745,53 +736,59 @@ class OpSet9():
param_attr=None)
else:
input_inner_indices = node.layer_name + '_input_inner_indices'
shape = val_x.out_shapes[0]
node.fluid_code.add_layer(
'reshape',
inputs=indices.layer_name,
output=indices.layer_name,
param_attr={'shape': indices.out_shapes[0]})
zeros_like_val_x = val_x.layer_name + '_zeros'
node.fluid_code.add_layer(
'zeros_like',
inputs=val_x,
output=zeros_like_val_x,
param_attr=None)
node.fluid_code.add_layer(
'scatter_nd',
'scatter_nd_add',
inputs={
'shape': val_x.out_shapes[0],
'ref': zeros_like_val_x,
'index': indices,
'updates': updates
},
output=input_inner_indices,
param_attr=None)
indices_mask = node.layer_name + '_indices_mask'
constant_minus_one = node.layer_name + '_constant_minus_one'
# full_like support create tensor shape like input tensor
node.fluid_code.add_layer(
'fill_constant',
inputs=None,
'full_like',
inputs=updates,
output=constant_minus_one,
param_attr={
'shape': updates.out_shapes[0],
'dtype': string(updates.dtype),
'value': -1
})
indices_mask = node.layer_name + '_indices_mask'
param_attr={'dtype': string(updates.dtype),
'fill_value': -1})
node.fluid_code.add_layer(
'scatter_nd',
'scatter_nd_add',
inputs={
'shape': val_x.out_shapes[0],
'ref': zeros_like_val_x,
'index': indices,
'updates': constant_minus_one
},
output=indices_mask,
param_attr=None)
constant_1 = node.layer_name + '_constant_1'
constant_one = node.layer_name + '_constant_1'
# full_like support create tensor shape like input tensor
node.fluid_code.add_layer(
'fill_constant',
inputs=None,
output=constant_1,
param_attr={
'shape': val_x.out_shapes[0],
'dtype': string(val_x.dtype),
'value': 1
})
'full_like',
inputs=val_x,
output=constant_one,
param_attr={'dtype': string(val_x.dtype),
'fill_value': 1})
input_out_indices_mask = node.layer_name + '_input_out_indices_mask'
node.fluid_code.add_layer(
"elementwise_add",
inputs={"x": indices_mask,
"y": constant_1},
"y": constant_one},
output=input_out_indices_mask,
param_attr=None)
......@@ -831,27 +828,35 @@ class OpSet9():
if len(node.inputs) > 1:
starts = self.graph.get_input_node(node, idx=1, copy=True)
ends = self.graph.get_input_node(node, idx=2, copy=True)
starts_value = _const_weight_or_none(starts)
ends_value = _const_weight_or_none(ends)
if len(node.inputs) > 3:
axes = self.graph.get_input_node(node, idx=3, copy=True)
axes = _const_weight_or_none(axes, necessary=True)
if len(node.inputs) > 4:
steps = self.graph.get_input_node(node, idx=4, copy=True)
steps = _const_weight_or_none(steps)
if steps is not None:
assert steps == 1, "Only support convert op:Slice, which attribute:steps == 1"
attr = {
"axes": axes,
"starts": starts.layer_name,
"ends": ends.layer_name
}
starts_value = _const_weight_or_none(starts)
ends_value = _const_weight_or_none(ends)
if starts_value is not None and ends_value is not None:
self.omit_nodes.append(starts.layer_name)
self.omit_nodes.append(ends.layer_name)
starts_value = starts_value.copy()
ends_value = ends_value.copy()
#for idx in range(len(ends_value)):
# if ends_value[idx] > 2**31 - 1:
# ends_value[idx] = 2**31 - 1
#print(val_x.out_shapes)
for idx in range(len(ends_value)):
if ends_value[idx] > 2**31 - 1:
if starts_value[idx] >= val_x.out_shapes[0][axes[idx]]:
starts_value[idx] = val_x.out_shapes[0][axes[idx]] - 1
ends_value[idx] = val_x.out_shapes[0][axes[idx]]
starts_value[idx] = val_x.out_shapes[0][axes[idx]] - 1
elif ends_value[idx] > 2**31 - 1:
ends_value[idx] = 2**31 - 1
attr = {
"axes": axes,
......@@ -884,6 +889,11 @@ class OpSet9():
ends[idx] = 2**31 - 1
attr = {"axes": axes, "starts": starts, "ends": ends}
if steps is not None:
attr['strides'] = steps
node.fluid_code.add_layer(
'strided_slice', inputs=val_x, output=node, param_attr=attr)
else:
node.fluid_code.add_layer(
'slice', inputs=val_x, output=node, param_attr=attr)
......@@ -907,6 +917,38 @@ class OpSet9():
node.fluid_code.add_layer(
'fill_constant', inputs=None, output=node, param_attr=attr)
@print_mapping_info
def Clip(self, node):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
val_y = self.graph.get_node(node.layer.output[0], copy=True)
max_value, min_value = None, None
if len(node.inputs) == 1:
max_value = node.get_attr('max')
min_value = node.get_attr('min')
attr = {
'max': max_value,
'min': min_value,
}
node.fluid_code.add_layer(
'clip', inputs=val_x, output=node, param_attr=attr)
else:
max_ipt = self.graph.get_input_node(node, idx=1, copy=True)
min_ipt = self.graph.get_input_node(node, idx=2, copy=True)
max_value = _const_weight_or_none(max_ipt)
min_value = _const_weight_or_none(min_ipt)
self.omit_nodes.append(max_ipt.layer_name)
self.omit_nodes.append(min_ipt.layer_name)
if max_value.shape == (1, ):
max_value = max_value[0]
if min_value.shape == (1, ):
min_value = min_value[0]
if max_value is not None and min_value is not None:
attr = {'max': max_value, 'min': min_value}
node.fluid_code.add_layer(
'clip', inputs=val_x, output=node, param_attr=attr)
else:
raise
@print_mapping_info
def Split(self, node):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
......@@ -1212,10 +1254,19 @@ class OpSet9():
mode = 'channel'
shape_slope = val_slope.out_shapes[0]
if len(shape_slope) == 1:
if shape_slope == [1]:
mode = 'all'
elif len(shape_slope) > 2:
mode = 'element'
if mode == 'channel' and len(shape_slope) == 1:
# paddle params shape need be [1, channel]
slope_data = _const_weight_or_none(val_slope)
slope_data = np.reshape(slope_data, [1] + shape_slope)
self.weights[val_slope.layer_name] = slope_data
self.omit_nodes.append(val_slope.layer_name)
attr = {
"param_attr": string(val_slope.layer_name),
'mode': string(mode)
......
x2paddle/op_mapper/paddle2onnx/opset11/opset.py 已删除 100644 → 0
浏览文件 @ ce6ffee2
# 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 @ ce6ffee2
# 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 @ ce6ffee2
# 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 @ ce6ffee2
# 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 @ ce6ffee2
# 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
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# 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.
from .adaptive_pool2d_fuser import AdaptivePool2dFuser
from .adaptive_pool2d_fuse_pass import AdaptivePool2dFusePass
from .batchnorm2d_fuser import BatchNorm2dFuser
from .batchnorm2d_fuse_pass import BatchNorm2dFusePass
from .constant_fuser import ConstantFuser
from .constant_fuse_pass import ConstantFusePass
from .dropout_fuser import DropoutFuser
from .dropout_fuse_pass import DropoutFusePass
from .fc_fuser import FcFuser
from .fc_fuse_pass import FcFusePass
from .interpolate_bilinear_fuser import InterpolateBilinearFuser
from .interpolate_bilinear_fuse_pass import InterpolateBilinearFusePass
from .reshape_fuser import ReshapeFuser
from .reshape_fuse_pass import ReshapeFusePass
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