A very simple tool that compresses the overall size of the ONNX model by aggregating duplicate constant values as much as possible. Simple Constant value Shrink for ONNX.
A very simple tool that compresses the overall size of the ONNX model by aggregating duplicate constant values as much as possible. Simple Constant value Shrink for ONNX.
https://github.com/PINTO0309/simple-onnx-processing-tools
### option
$ echo export PATH="~/.local/bin:$PATH" >> ~/.bashrc \
&& source ~/.bashrc
### run
$ pip install -U onnx \
&& python3 -m pip install -U onnx_graphsurgeon --index-url https://pypi.ngc.nvidia.com \
&& pip install -U scs4onnx
https://github.com/PINTO0309/simple-onnx-processing-tools#docker
$ scs4onnx -h
usage:
scs4onnx [-h]
[-m {shrink,npy}]
[-fo FORCED_EXTRACTION_OP_NAMES]
[-fc FORCED_EXTRACTION_CONSTANT_NAMES]
[-d]
[-n]
input_onnx_file_path output_onnx_file_path
positional arguments:
input_onnx_file_path
Input onnx file path.
output_onnx_file_path
Output onnx file path.
optional arguments:
-h, --help
show this help message and exit
-m {shrink,npy}, --mode {shrink,npy}
Constant Value Compression Mode.
shrink: Share constant values inside the model as much as possible.
The model size is slightly larger because
some shared constant values remain inside the model,
but performance is maximized.
npy: Outputs constant values used repeatedly in the model to an
external file .npy. Instead of the smallest model body size,
the file loading overhead is greater.
Default: shrink
-fo FORCED_EXTRACTION_OP_NAMES [FORCED_EXTRACTION_OP_NAMES ...], --forced_extraction_op_names FORCED_EXTRACTION_OP_NAMES [FORCED_EXTRACTION_OP_NAMES ...]
Extracts the constant value of the specified OP name to .npy
regardless of the mode specified.
Cannot be used with --forced_extraction_constant_names at the same time.
e.g. --forced_extraction_op_names aaa bbb ccc
-fc FORCED_EXTRACTION_CONSTANT_NAMES [FORCED_EXTRACTION_CONSTANT_NAMES ...], --forced_extraction_constant_names FORCED_EXTRACTION_CONSTANT_NAMES [FORCED_EXTRACTION_CONSTANT_NAMES ...]
Extracts the constant value of the specified Constant name to .npy
regardless of the mode specified.
Cannot be used with --forced_extraction_op_names at the same time.
e.g. --forced_extraction_constant_names aaa bbb ccc
-d, --disable_auto_downcast
Disables automatic downcast processing from Float64 to Float32 and INT64
to INT32. Try enabling it and re-running it if you encounter type-related
errors.
-n, --non_verbose
Do not show all information logs. Only error logs are displayed.
$ python
>>> from scs4onnx import shrinking
>>> help(shrinking)
Help on function shrinking in module scs4onnx.onnx_shrink_constant:
shrinking(
input_onnx_file_path: Union[str, NoneType] = '',
output_onnx_file_path: Union[str, NoneType] = '',
onnx_graph: Union[onnx.onnx_ml_pb2.ModelProto, NoneType] = None,
mode: Union[str, NoneType] = 'shrink',
forced_extraction_op_names: List[str] = [],
forced_extraction_constant_names: List[str] = [],
disable_auto_downcast: Union[bool, NoneType] = False
non_verbose: Union[bool, NoneType] = False
) -> Tuple[onnx.onnx_ml_pb2.ModelProto, str]
Parameters
----------
input_onnx_file_path: Optional[str]
Input onnx file path.
Either input_onnx_file_path or onnx_graph must be specified.
output_onnx_file_path: Optional[str]
Output onnx file path.
If output_onnx_file_path is not specified, no .onnx file is output.
onnx_graph: Optional[onnx.ModelProto]
onnx.ModelProto.
Either input_onnx_file_path or onnx_graph must be specified.
onnx_graph If specified, ignore input_onnx_file_path and process onnx_graph.
mode: Optional[str]
Constant Value Compression Mode.
'shrink': Share constant values inside the model as much as possible.
The model size is slightly larger because some shared constant values remain
inside the model, but performance is maximized.
'npy': Outputs constant values used repeatedly in the model to an external file .npy.
Instead of the smallest model body size, the file loading overhead is greater.
Default: shrink
forced_extraction_op_names: List[str]
Extracts the constant value of the specified OP name to .npy
regardless of the mode specified.
Cannot be used with --forced_extraction_constant_names at the same time.
e.g. ['aaa','bbb','ccc']
forced_extraction_constant_names: List[str]
Extracts the constant value of the specified Constant name to .npy
regardless of the mode specified.
Cannot be used with --forced_extraction_op_names at the same time.
e.g. ['aaa','bbb','ccc']
disable_auto_downcast: Optional[bool]
Disables automatic downcast processing from Float64 to Float32 and INT64 to INT32.
Try enabling it and re-running it if you encounter type-related errors.
Default: False
non_verbose: Optional[bool]
Do not show all information logs. Only error logs are displayed.
Default: False
Returns
-------
shrunken_graph: onnx.ModelProto
Shrunken onnx ModelProto
npy_file_paths: List[str]
List of paths to externally output .npy files.
An empty list is always returned when in 'shrink' mode.
$ scs4onnx input.onnx output.onnx --mode shrink
If output_onnx_file_path is not specified, no .onnx file is output.
from scs4onnx import shrinking
shrunk_graph, npy_file_paths = shrinking(
input_onnx_file_path='input.onnx',
output_onnx_file_path='output.onnx',
mode='npy',
non_verbose=False
)
onnx_graph If specified, ignore input_onnx_file_path and process onnx_graph.
from scs4onnx import shrinking
shrunk_graph, npy_file_paths = shrinking(
onnx_graph=graph,
mode='npy',
non_verbose=True
)
shrink mode sample297.8MB -> 67.4MB (.onnx)
$ scs4onnx gmflow_sintel_480x640.onnx gmflow_sintel_480x640_opt.onnx
1.8GB -> 886.8MB (.onnx)
$ scs4onnx hitnet_sf_finalpass_720x960.onnx hitnet_sf_finalpass_720x960_opt.onnx
1.8GB -> 2.1MB (.onnx) + 884.7MB (.npy)
$ scs4onnx \
hitnet_sf_finalpass_720x960.onnx \
hitnet_sf_finalpass_720x960_opt.onnx \
--forced_extraction_op_names GatherElements_660
297.8MB -> 21.3MB (.onnx) + 46.1MB (.npy)
$ scs4onnx \
gmflow_sintel_480x640.onnx \
gmflow_sintel_480x640_opt.onnx \
--forced_extraction_constant_names 1646
npy mode sample297.8MB -> 21.3MB (.onnx)
.npy file view$ python
>>> import numpy as np
>>> param = np.load('gmflow_sintel_480x640_shrunken_exported_1646.npy')
>>> param.shape
(8, 1200, 1200)
>>> param
array([[[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.],
...,
[-100., -100., -100., ..., 0., 0., 0.],
[-100., -100., -100., ..., 0., 0., 0.],
[-100., -100., -100., ..., 0., 0., 0.]]], dtype=float32)
https://github.com/PINTO0309/simple-onnx-processing-tools/issues
FAQs
A very simple tool that compresses the overall size of the ONNX model by aggregating duplicate constant values as much as possible. Simple Constant value Shrink for ONNX.
We found that scs4onnx demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
vlt's new "reproduce" tool verifies npm packages against their source code, outperforming traditional provenance adoption in the JavaScript ecosystem.
Research
Security News
Socket researchers uncovered a malicious PyPI package exploiting Deezer’s API to enable coordinated music piracy through API abuse and C2 server control.
Research
The Socket Research Team discovered a malicious npm package, '@ton-wallet/create', stealing cryptocurrency wallet keys from developers and users in the TON ecosystem.