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提交 2a82fdeb 编写于 作者: M Macrobull
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fix name and add ops

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onnx2fluid/README.md
浏览文件 @ 2a82fdeb
......@@ -17,13 +17,13 @@ onnx2fluid支持将ONNX模型转换为PaddlePaddle模型，并用于预测，用
在如下环境配置中测试成功:
* python 3.5+
* onnx == 1.4.0
* paddlepaddle == 1.3.0 (可选，仅用于验证)
* onnx == 1.4.1
* paddlepaddle == 1.5.0 (可选，仅用于验证)
使用[Anaconda](https://docs.anaconda.com/anaconda/install):
``` shell
conda install -c conda-forge onnx
pip install paddlepaddle==1.3.0
pip install paddlepaddle==1.5.0
```
## 动手玩
......@@ -49,6 +49,8 @@ onnx2fluid sample_1.onnx -t sample_1.npz
## 使用说明
目前支持 **ONNX opset 9+** 的部分算子，对应PyTorch版本 **1.0/1.1(stable opset)**，更多兼容信息请参考[ONNX文档](https://github.com/onnx/onnx/blob/master/docs/Operators.md)
onnx2fluid:
```shell
......@@ -79,5 +81,5 @@ onnx2fluid.validate [-d] [-t test_data.npz] [-p 1e-3] /path/to/onnx/model.onnx
## 参考
* PaddlePaddle [算子](http://www.paddlepaddle.org/documentation/docs/zh/1.4/api_cn/layers_cn.html)
* PaddlePaddle [加载预测模型](http://www.paddlepaddle.org/documentation/docs/zh/1.4/api_guides/low_level/inference.html#id4)
* PaddlePaddle [算子](http://www.paddlepaddle.org/documentation/docs/zh/1.5/api_cn/layers_cn.html)
* PaddlePaddle [加载预测模型](http://www.paddlepaddle.org/documentation/docs/zh/1.5/api_guides/low_level/inference.html#id4)
onnx2fluid/examples/gen_some_samples.py
浏览文件 @ 2a82fdeb
......@@ -20,50 +20,56 @@ from onnx2fluid.torch_export_helper import export_onnx_with_validation
prefix = 'sample_'
idx = 0
######### example: RNN ########
#
#class Model(nn.Module):
# def __init__(self):
# super(Model, self).__init__()
# self.rnn = nn.RNN(4, 6, 2)
#
# def forward(self, x):
# y = x
# y, h = self.rnn(y)
# return y
#
#
#model = Model()
#model.eval()
#xb = torch.rand((2, 3, 4))
#yp = model(xb)
#idx += 1
#print('index: ', idx)
#export_onnx_with_validation(model, [xb], prefix + str(idx),
# ['x'], ['y'],
# verbose=True, training=False)
######## example: RNN ########
######### example: random ########
#
#class Model(nn.Module):
# def __init__(self):
# super(Model, self).__init__()
#
# def forward(self, x):
# y = torch.rand((2, 3)) # + torch.rand_like(xb)
# y = y + torch.randn((2, 3)) # + torch.randn_like(xb)
# return y
#
#
#model = Model()
#model.eval()
#xb = torch.rand((2, 3))
#yp = model(xb)
#idx += 1
#print('index: ', idx)
#export_onnx_with_validation(model, [xb], prefix + str(idx),
# ['x'], ['y'],
# verbose=True, training=False)
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.gru = nn.GRU(4, 5, 3)
self.lstm = nn.LSTM(5, 6, 2)
def forward(self, x):
y = x
y, h = self.gru(y)
y, h = self.lstm(y)
return y
model = Model()
model.eval()
xb = torch.rand((2, 3, 4))
yp = model(xb)
idx += 1
print('index: ', idx)
export_onnx_with_validation(model, [xb],
prefix + str(idx), ['x'], ['y'],
verbose=True,
training=False)
######## example: random ########
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
def forward(self, x):
y = torch.rand((2, 3)) # + torch.rand_like(xb)
y = y + torch.randn((2, 3)) # + torch.randn_like(xb)
return y
model = Model()
model.eval()
xb = torch.rand((2, 3))
yp = model(xb)
idx += 1
print('index: ', idx)
export_onnx_with_validation(model, [xb],
prefix + str(idx), ['x'], ['y'],
verbose=True,
training=False)
######## example: fc ########
......@@ -175,7 +181,7 @@ class Model(nn.Module):
super(Model, self).__init__()
self.conv = nn.Conv2d(3, 8, 3)
self.batch_norm = nn.BatchNorm2d(8)
self.pool = nn.AdaptiveAvgPool2d(2)
self.pool = nn.AdaptiveAvgPool2d(1)
def forward(self, x):
y = x
......@@ -215,7 +221,8 @@ export_onnx_with_validation(model, [xb],
#yp = model(xb)
#idx += 1
#print('index: ', idx)
#export_onnx_with_validation(model, [xb], prefix + str(idx),
#export_onnx_with_validation(
# model, [xb], prefix + str(idx),
# ['x'], ['y'],
# verbose=True, training=False)
......
onnx2fluid/examples/onnx_model_zoo.sh
浏览文件 @ 2a82fdeb
......@@ -24,14 +24,14 @@ bvlc_alexnet()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model"
for npz in "$bn_tar"/*.npz
for npz in "$bn_tar/"*.npz
do
echo "converting $npz ..."
python convert_data_npz.py "$npz" data_0 prob_1 -s
python -m onnx2fluid.validation $validate_flags1 -t "$npz"
python -m onnx2fluid.validation $validate_flags2 -t "$npz"
done
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" data_0 prob_1
......@@ -54,7 +54,7 @@ bvlc_googlenet()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model"
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir"
python convert_data_pb.py "$pb_dir" data_0 prob_1
......@@ -77,7 +77,7 @@ bvlc_reference_caffenet()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model"
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir"
python convert_data_pb.py "$pb_dir" data_0 prob_1
......@@ -100,7 +100,7 @@ bvlc_reference_rcnn_ilsvrc13()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model"
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir"
python convert_data_pb.py "$pb_dir" data_0 fc-rcnn_1
......@@ -123,14 +123,14 @@ densenet121()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model"
for npz in "$bn_tar"/*.npz
for npz in "$bn_tar/"*.npz
do
echo "converting $npz ..."
python convert_data_npz.py "$npz" data_0 fc6_1 -s
python -m onnx2fluid.validation $validate_flags1 -t "$npz"
python -m onnx2fluid.validation $validate_flags2 -t "$npz"
done
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir"
python convert_data_pb.py "$pb_dir" data_0 fc6_1
......@@ -153,7 +153,7 @@ emotion_ferplus()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model" -y
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" Input3 Plus692_Output_0
......@@ -176,14 +176,14 @@ inception_v1()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model"
for npz in "$bn_tar"/*.npz
for npz in "$bn_tar/"*.npz
do
echo "converting $npz ..."
python convert_data_npz.py "$npz" data_0 prob_1 -s
python -m onnx2fluid.validation $validate_flags1 -t "$npz"
python -m onnx2fluid.validation $validate_flags2 -t "$npz"
done
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" data_0 prob_1
......@@ -206,14 +206,14 @@ inception_v2()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model"
for npz in "$bn_tar"/*.npz
for npz in "$bn_tar/"*.npz
do
echo "converting $npz ..."
python convert_data_npz.py "$npz" data_0 prob_1 -s
python -m onnx2fluid.validation $validate_flags1 -t "$npz"
python -m onnx2fluid.validation $validate_flags2 -t "$npz"
done
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" data_0 prob_1
......@@ -236,7 +236,7 @@ mobilenet()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model" -y
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" data mobilenetv20_output_flatten0_reshape0
......@@ -259,7 +259,7 @@ resnet18()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model" -y
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" data resnetv15_dense0_fwd
......@@ -282,14 +282,14 @@ resnet50()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model"
for npz in "$bn_tar"/*.npz
for npz in "$bn_tar/"*.npz
do
echo "converting $npz ..."
python convert_data_npz.py "$npz" gpu_0/data_0 gpu_0/softmaxout_1 -s
python -m onnx2fluid.validation $validate_flags1 -t "$npz"
python -m onnx2fluid.validation $validate_flags2 -t "$npz"
done
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" gpu_0/data_0 gpu_0/softmaxout_1
......@@ -312,7 +312,7 @@ resnet100_arcface()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model" -y
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" data fc1
......@@ -335,7 +335,7 @@ resnet101_duc()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model" -y
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" data seg_loss
......@@ -358,7 +358,7 @@ resnet152()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model" -y
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" data resnetv27_dense0_fwd
......@@ -381,7 +381,7 @@ shufflenet()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model"
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" gpu_0/data_0 gpu_0/softmax_1
......@@ -404,7 +404,7 @@ squeezenet()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model"
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir"
python convert_data_pb.py "$pb_dir" data_0 softmaxout_1
......@@ -427,7 +427,7 @@ squeezenet1v1()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model"
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" data squeezenet0_flatten0_reshape0
......@@ -448,10 +448,10 @@ ssd()
rm -rf "$bn_tar/"
echo "extracting ..."
mkdir "$bn_tar"
tar xf "$fn_tar" -C "$bn_tar"/
tar xf "$fn_tar" -C "$bn_tar/"
python -m onnx2fluid $convert_flags "$fn_model"
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" image bboxes,labels,scores
......@@ -474,7 +474,7 @@ tiny_yolov2()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model" -y
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir"
python convert_data_pb.py "$pb_dir" image grid
......@@ -497,7 +497,7 @@ vgg16bn()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model" -y
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" data vgg0_dense2_fwd
......@@ -520,7 +520,7 @@ vgg19()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model"
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir"
python convert_data_pb.py "$pb_dir" data_0 prob_1
......@@ -543,7 +543,7 @@ yolov3()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model" -x #
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir ..."
python convert_data_pb.py "$pb_dir" input_1:01,image_shape:01 yolonms_layer_1/ExpandDims_1:0,yolonms_layer_1/ExpandDims_3:0,yolonms_layer_1/concat_2:0
......@@ -566,7 +566,7 @@ zfnet512()
tar xf "$fn_tar"
python -m onnx2fluid $convert_flags "$fn_model"
for pb_dir in "$bn_tar"/*/
for pb_dir in "$bn_tar/"*/
do
echo "converting $pb_dir"
python convert_data_pb.py "$pb_dir" gpu_0/data_0 gpu_0/softmax_1
......
onnx2fluid/onnx2fluid/conversion.py
浏览文件 @ 2a82fdeb
......@@ -17,6 +17,22 @@ __all__ = [
DEFAULT_ONNX_OPSET_VERSION = 9
def make_var_name(name):
"""
make a valid variable name in Python code and filename in filesystem
"""
if name == '':
return '_'
if name[0].isdigit():
return 'var_' + name
for s in ' \\|/:.-':
name = name.replace(s, '_')
if name.startswith('_'):
name = 'var' + name
return name
def convert(onnx_model_filename,
save_dir,
model_basename='model.py',
......@@ -30,6 +46,12 @@ def convert(onnx_model_filename,
convert an ONNX model to Paddle fluid Python code and desc pb
"""
assert isinstance(onnx_model_filename, str)
assert isinstance(save_dir, str)
assert isinstance(model_basename, str)
assert isinstance(model_func_name, str)
assert onnx_opset_version is None or isinstance(onnx_opset_version, int)
import onnx
from onnx.checker import ValidationError
......@@ -41,7 +63,6 @@ def convert(onnx_model_filename,
from .onnx_utils import inferred_model_value_info
from .onnx_utils import polish_model
from .writer import Program, Writer
from .writer import make_var_name
logger = logging.getLogger('convert')
......@@ -88,17 +109,21 @@ def convert(onnx_model_filename,
fluid_writer = Writer()
# model components
# graph_name = onnx_graph.name
graph_inputs = [value.name for value in onnx_graph.input]
graph_outputs = [value.name for value in onnx_graph.output]
graph_params = []
graph_value_infos = inferred_model_value_info(onnx_model)
inp_vars = [make_var_name(value.name) for value in onnx_graph.input]
out_vars = [make_var_name(value.name) for value in onnx_graph.output]
par_vars = []
value_infos = inferred_model_value_info(onnx_model)
value_infos = {
make_var_name(key): value
for key, value in value_infos.items()
}
# prepare additional value_info
# for weights
for name, weight in graph_weights(onnx_graph):
value_info = graph_value_infos[name]
value_info['embeded_as'] = []
var_name = make_var_name(name)
value_info = value_infos[var_name]
value_info['embedded_as'] = []
value_info['get_weight'] = (lambda w: lambda: w.tolist())(
weight) # lazy getter
......@@ -108,19 +133,23 @@ def convert(onnx_model_filename,
topo = 'forward'
for name, domain, op_type, inputs, outputs, attrs in graph_ops(onnx_graph,
topo=topo):
logger.debug('translating op %s %s::%s ...', name, domain, op_type)
op_name = make_var_name(name)
inputs = [make_var_name(val) for val in inputs]
outputs = [make_var_name(val) for val in outputs]
logger.debug('translating op %s(%s) %s::%s ...', name, op_name, domain,
op_type)
if domain == DEFAULT_OP_DOMAIN:
domain = ''
try:
fluid_writer.emit_op(
fluid_program,
name,
op_name,
domain,
op_type,
inputs,
outputs,
attrs,
graph_value_infos,
value_infos,
embed_params=embed_params,
)
except BaseException as e:
......@@ -133,17 +162,16 @@ def convert(onnx_model_filename,
len(fluid_program.op_descs))
# type-shape info copy
for name, value_info in graph_value_infos.items():
var_name = make_var_name(name)
for var_name, value_info in value_infos.items():
fluid_program.VarTypeShapeInfo(var_name, value_info,
remove_batch=False) #
bad_var_names = []
bad_vars = []
for var_name, var_desc in fluid_program.var_descs.items():
if not var_desc.type.lod_tensor.HasField('tensor'):
bad_var_names.append(var_name)
if len(bad_var_names) > 0:
bad_vars.append(var_name)
if len(bad_vars) > 0:
logger.warning('type-shape not infered for var %s ...',
', '.join(bad_var_names[:5]))
', '.join(bad_vars[:5]))
logger.warning('this causes little problem for PaddlePaddle, '
'but Paddle Mobile may not infer correctly')
logger.warning('please consider running validation with -i '
......@@ -151,40 +179,41 @@ def convert(onnx_model_filename,
# weight writer
for name, weight in graph_weights(onnx_graph):
graph_params.append(name)
value_info = graph_value_infos[name]
var_names = value_info.get('embeded_as', [])
if var_names:
if len(var_names) > 1:
var_name = make_var_name(name)
par_vars.append(var_name)
value_info = value_infos[var_name]
embedded_names = value_info.get('embedded_as', [])
if embedded_names:
if len(embedded_names) > 1:
logger.info(
'weight %s is shared between ops, more disk space will be consumed',
name)
logger.debug('saving weight %s(%s[%d], %dB) as %s ...', name,
weight.dtype, weight.size, weight.nbytes, var_names)
for var_name in var_names: # multiple references
weight.dtype, weight.size, weight.nbytes,
embedded_names)
for embedded_name in embedded_names: # multiple references
fluid_writer.write_weight(
weight, shutil.os.path.join(save_dir, var_name))
weight, shutil.os.path.join(save_dir, embedded_name))
else:
logger.debug('saving weight %s(%s[%d], %dB) to %s ...', name,
weight.dtype, weight.size, weight.nbytes,
make_var_name(name))
fluid_writer.write_weight(
weight, shutil.os.path.join(save_dir, make_var_name(name)))
fluid_writer.emit_param(fluid_program, name, value_info)
weight.dtype, weight.size, weight.nbytes, var_name)
fluid_writer.write_weight(weight,
shutil.os.path.join(save_dir, var_name))
fluid_writer.emit_param(fluid_program, var_name, value_info)
param_codes = fluid_program.codes
fluid_program.codes = []
logger.info('%d weights converted', len(graph_params))
logger.info('%d weights converted', len(par_vars))
# input writer
external_inputs = []
for name in graph_inputs:
if name not in graph_params:
value_info = graph_value_infos[name]
for var_name in inp_vars:
if var_name not in par_vars:
value_info = value_infos[var_name]
assert value_info['external']
external_inputs.append(name)
external_inputs.append(var_name)
fluid_writer.emit_inputs(fluid_program,
external_inputs,
graph_value_infos,
value_infos,
remove_batch=False) # TODO:
input_codes = fluid_program.codes
fluid_program.codes = []
......@@ -192,11 +221,11 @@ def convert(onnx_model_filename,
# output writer
external_outputs = []
for name in graph_outputs:
if name not in graph_params:
value_info = graph_value_infos[name]
for var_name in out_vars:
if var_name not in par_vars:
value_info = value_infos[var_name]
assert value_info['external']
external_outputs.append(name)
external_outputs.append(var_name)
fluid_writer.emit_outputs(fluid_program, external_outputs)
output_codes = [''] + fluid_program.codes # add an empty line
fluid_program.codes = []
......@@ -204,10 +233,18 @@ def convert(onnx_model_filename,
# code generation
header_codes = fluid_writer.header_code(
model_func_name, 'From: {}'.format(onnx_model_filename))
model_func_name,
'From: {}'.format(onnx_model_filename),
)
code_filename = shutil.os.path.join(save_dir, model_basename)
fluid_writer.write_code_file(code_filename, header_codes, input_codes,
param_codes, op_codes, output_codes)
fluid_writer.write_code_file(
code_filename,
header_codes,
input_codes,
param_codes,
op_codes,
output_codes,
)
logger.info('code saved to %s, factory function: %s', code_filename,
model_func_name)
......
onnx2fluid/onnx2fluid/onnx_utils.py
浏览文件 @ 2a82fdeb
......@@ -87,6 +87,9 @@ def get_attribute_value2(attr):
get_attribute_value enhanced
"""
assert isinstance(
attr, onnx.AttributeProto), 'attr is not a AttributeProto instance'
if attr.type == onnx.AttributeProto.TENSOR:
dtype = np.dtype(TENSOR_TYPE_TO_NP_TYPE[attr.t.data_type])
data = attr.t.raw_data
......@@ -106,6 +109,9 @@ def tensor_dtype(tensor):
get ONNX tensor in np.dtype
"""
assert isinstance(
tensor, onnx.ValueInfoProto), 'tensor is not a ValueInfoProto instance'
return TENSOR_TYPE_TO_NP_TYPE[tensor.type.tensor_type.elem_type]
......@@ -114,6 +120,9 @@ def tensor_shape(tensor):
get ONNX tensor shape
"""
assert isinstance(
tensor, onnx.ValueInfoProto), 'tensor is not a ValueInfoProto instance'
return tuple([dim.dim_value for dim in tensor.type.tensor_type.shape.dim])
......@@ -122,6 +131,8 @@ def node_attrs(node):
convert ONNX node attributes to dict
"""
assert isinstance(node, onnx.NodeProto), 'node is not a NodeProto instance'
return {attr.name: get_attribute_value2(attr)
for attr in node.attribute} # dict
......@@ -224,9 +235,8 @@ def graph_ops(graph, topo='default'):
generator for ONNX node graph with given topology
"""
if not isinstance(graph, onnx.GraphProto):
logger.error('graph is not a GraphProto instance')
return
assert isinstance(graph,
onnx.GraphProto), 'graph is not a GraphProto instance'
return node_iter(graph.node, node_topo(graph.node, topo))
......@@ -236,9 +246,8 @@ def graph_weights(graph):
generator for weights of an ONNX model
"""
if not isinstance(graph, onnx.GraphProto):
logger.error('graph is not a GraphProto instance')
return
assert isinstance(graph,
onnx.GraphProto), 'graph is not a GraphProto instance'
for initializer in graph.initializer:
name = initializer.name
......@@ -251,6 +260,9 @@ def inferred_model_value_info(model):
collect value/type info for an ONNX model
"""
assert isinstance(model,
onnx.ModelProto), 'model is not a ModelProto instance'
model = infer_shapes(model)
graph = model.graph
value_info = Dict()
......@@ -353,6 +365,10 @@ def optimize_model_skip_op_for_inference(model, op_list=None):
"""
skip ops can be bypassed for inference
"""
assert isinstance(model,
onnx.ModelProto), 'model is not a ModelProto instance'
if op_list is None:
op_list = ('Dropout', 'Identity')
......@@ -415,6 +431,9 @@ def optimize_model_strip_initializer(model, keep_input_only=True):
strip weights for inference
"""
assert isinstance(model,
onnx.ModelProto), 'model is not a ModelProto instance'
nodes = model.graph.node
input_refs, output_refs = build_value_refs(nodes)
out_names = [val.name for val in model.graph.output]
......@@ -456,6 +475,9 @@ def optimize_model_cast(model):
strip cascade and unecessary onnx::Cast-9:
"""
assert isinstance(model,
onnx.ModelProto), 'model is not a ModelProto instance'
nodes = model.graph.node
input_refs, output_refs = build_value_refs(nodes)
value_info = inferred_model_value_info(model)
......@@ -513,6 +535,9 @@ def optimize_model_slice(model):
strip cascade and unecessary onnx::Slice-1:9
"""
assert isinstance(model,
onnx.ModelProto), 'model is not a ModelProto instance'
nodes = model.graph.node
input_refs, output_refs = build_value_refs(nodes)
......
onnx2fluid/onnx2fluid/symbolic.py
浏览文件 @ 2a82fdeb
此差异已折叠。
onnx2fluid/onnx2fluid/torch_export_helper.py
浏览文件 @ 2a82fdeb
......@@ -6,6 +6,9 @@ Created on Fri Mar 22 11:22:46 2019
@author: Macrobull
"""
from __future__ import division
import logging
import numpy as np
import torch
......@@ -24,6 +27,8 @@ from typing import (
Union,
)
logger = logging.getLogger(__name__)
__all__ = [
'export_data',
'export_onnx_with_validation',
......@@ -76,7 +81,7 @@ def export_data(state_dict: Mapping[Text, Any], prefix: Text = '') -> None:
return str(obj)
prefix_ = prefix + ('_' if prefix else '')
fp = open('{}.txt'.format(prefix or 'meta'), 'w')
fp = open('{}.txt'.format(prefix or 'meta'), mode='w')
for key, value in state_dict.items():
data = None
if torch.is_tensor(value):
......@@ -93,7 +98,7 @@ def export_data(state_dict: Mapping[Text, Any], prefix: Text = '') -> None:
def export_onnx_with_validation(
model: torch.nn.Module,
model: torch.nn.Module, # or JITScriptModule
inputs: Sequence[Union[torch.Tensor, Sequence[object]]],
export_basepath: Text,
input_names: Optional[List[Text]] = None,
......
onnx2fluid/onnx2fluid/validation.py
浏览文件 @ 2a82fdeb
......@@ -43,7 +43,8 @@ def fluid_prog_shape_infer(prog):
import paddle.fluid as fluid
assert isinstance(prog, fluid.framework.Program)
assert isinstance(prog,
fluid.framework.Program), 'prog is not a Program instance'
logger.info('performing type-shape inference ...')
for block in prog.blocks:
......@@ -84,6 +85,8 @@ def validate(fluid_model_filename,
inference the converted Paddle fluid model, validate with given golden data
"""
assert isinstance(fluid_model_filename, str)
import numpy as np
import paddle.fluid as fluid
......@@ -153,7 +156,7 @@ def validate(fluid_model_filename,
input_data = flatten_dict(input_data)
output_data = flatten_dict(output_data)
input_names = input_data.keys()
output_names = output_data.keys()
# output_names = output_data.keys()
logger.info('with %d inputs and %d outputs', len(input_data),
len(output_data))
else:
......
onnx2fluid/onnx2fluid/writer.py
浏览文件 @ 2a82fdeb
......@@ -16,7 +16,6 @@ from collections import OrderedDict as Dict
logger = logging.getLogger(__name__)
from . import symbolic
from .symbolic import _make_var_name as make_var_name
try:
import paddle.fluid.proto.framework_pb2 as framework_pb2
......@@ -63,7 +62,7 @@ def make_attr_name(name):
assert name != '', 'name should not be empty'
for s in ' \\|/:-': #
for s in ' \\|/:.-': #
name = name.replace(s, '_')
if not name.startswith('_'):
name = '_' + name
......@@ -207,7 +206,7 @@ class Program(object):
return desc
def VarDesc(self,
var_name,
name,
persistable=False,
value_info=None,
remove_batch=None):
......@@ -215,18 +214,16 @@ class Program(object):
add VarDesc,
"""
assert var_name not in self.var_descs, 'var naming conflicted'
assert name not in self.var_descs, 'var naming conflicted'
var_desc = framework_pb2.VarDesc()
var_desc.name = var_name
var_desc.name = name
var_desc.persistable = persistable
var_desc.type.type = framework_pb2.VarType.LOD_TENSOR
self.var_descs[var_name] = var_desc
self.var_descs[name] = var_desc
if value_info:
self.VarTypeShapeInfo(var_name,
value_info,
remove_batch=remove_batch)
self.VarTypeShapeInfo(name, value_info, remove_batch=remove_batch)
def Op(self, domain, op_type, *args, **kwargs):
"""
......@@ -260,19 +257,19 @@ class Program(object):
else:
self.code_mutable = code_mutable
def VarTypeShapeInfo(self, var_name, value_info, remove_batch=None):
def VarTypeShapeInfo(self, name, value_info, remove_batch=None):
"""
set value_info for var
"""
if var_name not in self.var_descs:
if name not in self.var_descs:
return
dtype = value_info.get('dtype', None)
if dtype is None:
return
var_desc = self.var_descs[var_name]
var_desc = self.var_descs[name]
tensor_desc = var_desc.type.lod_tensor.tensor
tensor_desc.data_type = self.Dtype(dtype) # required
......@@ -292,8 +289,7 @@ class Writer(object):
fluid code and desc writter
"""
# CODE_INDENT = ' ' * 4
CODE_INDENT = '\t'
CODE_INDENT = ' ' * 4 # '\t'
@staticmethod
def header_code(func_name, info=''):
......@@ -313,6 +309,7 @@ class Writer(object):
codes.append('from paddle.fluid import initializer, layers')
codes.append('')
codes.append('')
codes.append('def {}():'.format(func_name))
return codes
......@@ -342,24 +339,26 @@ class Writer(object):
emit an ONNX weight into program
"""
if value_info.get('embeded_as', []):
var_names = value_info['embeded_as']
prog.Code('# parameter {} embeded as {}'.format(name, var_names))
for var_name in var_names:
prog.VarDesc(var_name, persistable=True, value_info=value_info)
if value_info.get('embedded_as', []):
embedded_names = value_info['embedded_as']
prog.Code('# parameter {} embedded as {}'.format(
name, embedded_names))
for embedded_name in embedded_names:
prog.VarDesc(embedded_name,
persistable=True,
value_info=value_info)
else:
var_name = make_var_name(name)
attr_name = make_attr_name(name)
prog.Code('# parameter {}: {}'.format(name, var_name))
prog.Code('# parameter {}'.format(name))
prog.Code('{} = ParamAttr(name={})' # , trainable=True
.format(attr_name, repr(var_name)))
.format(attr_name, repr(name)))
prog.Code(
'{} = layers.create_parameter(shape={}, dtype={}, name={}, attr={}'
', default_initializer=initializer.Constant(0))' #, is_bias={}
.format(var_name, value_info['shape'],
.format(name, value_info['shape'],
repr(value_info['dtype'].name), repr(name),
attr_name)) #, value_info.get('is_bias', False)))
prog.VarDesc(var_name, persistable=True, value_info=value_info)
prog.VarDesc(name, persistable=True, value_info=value_info)
@staticmethod
def emit_inputs(prog, names, value_infos, remove_batch=None):
......@@ -368,7 +367,6 @@ class Writer(object):
"""
for idx, name in enumerate(names):
var_name = make_var_name(name)
value_info = value_infos[name]
shape = value_info['shape']
if remove_batch is None:
......@@ -377,13 +375,13 @@ class Writer(object):
if remove_batch:
shape = shape[1:]
prog.Code('# input {}: {}'.format(name, var_name))
prog.Code('# input {}'.format(name))
prog.Code((
'{} = layers.data(name={}, shape={}, dtype={}, '
'append_batch_size={})' # , stop_gradient=True
).format(
var_name,
repr(var_name),
name,
repr(name),
shape,
repr(value_info['dtype'].name),
remove_batch,
......@@ -391,12 +389,10 @@ class Writer(object):
prog.OpDesc(
'feed',
(['feed'], 'X'),
([var_name], 'Out'),
([name], 'Out'),
{'col': idx},
)
prog.VarDesc(var_name,
value_info=value_info,
remove_batch=remove_batch)
prog.VarDesc(name, value_info=value_info, remove_batch=remove_batch)
@staticmethod
def emit_outputs(prog, names): #, value_infos
......@@ -406,12 +402,11 @@ class Writer(object):
code = 'return '
for idx, name in enumerate(names):
var_name = make_var_name(name)
code += var_name + ', '
code += name + ', '
prog.OpDesc(
'fetch',
([var_name], 'X'),
([name], 'X'),
(['fetch'], 'Out'),
{'col': idx},
)
......@@ -458,8 +453,7 @@ class Writer(object):
for name, weight in weights.items():
assert isinstance(weights, dict), 'dict type weights required'
var_name = make_var_name(name)
filename = os.path.join(save_dir, var_name)
filename = os.path.join(save_dir, name)
Writer.write_weight(weight, filename)
logger.debug('saved weight %s to %s', name, filename)
......
onnx2fluid/requirements.txt
浏览文件 @ 2a82fdeb
-e .
onnx>=1.4
paddlepaddle
paddlepaddle>=1.5
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