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@ -0,0 +1,123 @@
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||||
Metadata-Version: 2.1
|
||||
Name: openvino
|
||||
Version: 2023.0.1
|
||||
Summary: OpenVINO(TM) Runtime
|
||||
Home-page: https://docs.openvino.ai/2023.0/index.html
|
||||
Download-URL: https://github.com/openvinotoolkit/openvino/tags
|
||||
Author: Intel(R) Corporation
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Author-email: openvino_pushbot@intel.com
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License: OSI Approved :: Apache Software License
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Description-Content-Type: text/markdown
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License-File: readme.txt
|
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License-File: LICENSE
|
||||
Requires-Dist: numpy (>=1.16.6)
|
||||
Requires-Dist: singledispatchmethod ; python_version < "3.8"
|
||||
|
||||
# OpenVINO™ Runtime
|
||||
|
||||
Intel® Distribution of OpenVINO™ toolkit is an open-source toolkit for optimizing and deploying AI inference. It can be used to develop applications and solutions based on deep learning tasks, such as: emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, etc. It provides high-performance and rich deployment options, from edge to cloud.
|
||||
|
||||
If you have already finished developing your models and converting them to the OpenVINO model format, you can install OpenVINO Runtime to deploy your applications on various devices. The [OpenVINO™ Runtime](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) Python package includes a set of libraries for an easy inference integration with your products.
|
||||
|
||||
## System Requirements
|
||||
|
||||
Before you start the installation, check the supported operating systems and required Python* versions. The complete list of supported hardware is available in the [System Requirements](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html).
|
||||
|
||||
**C++ libraries** are also required for the installation on Windows*. To install that, you can [download the Visual Studio Redistributable file (.exe)](https://aka.ms/vs/17/release/vc_redist.x64.exe).
|
||||
|
||||
> **NOTE**: This package can be installed on other versions of Linux and Windows OSes, but only the specific versions above are fully validated.
|
||||
|
||||
## Install the OpenVINO™ Runtime Package
|
||||
|
||||
### Step 1. Set Up Python Virtual Environment
|
||||
|
||||
Use a virtual environment to avoid dependency conflicts.
|
||||
|
||||
To create a virtual environment, use the following commands:
|
||||
|
||||
On Windows:
|
||||
```sh
|
||||
python -m venv openvino_env
|
||||
```
|
||||
|
||||
On Linux and macOS:
|
||||
```sh
|
||||
python3 -m venv openvino_env
|
||||
```
|
||||
|
||||
> **NOTE**: On Linux and macOS, you may need to [install pip](https://pip.pypa.io/en/stable/installation/). For example, on Ubuntu execute the following command to get pip installed: `sudo apt install python3-venv python3-pip`.
|
||||
|
||||
### Step 2. Activate Virtual Environment
|
||||
|
||||
On Linux and macOS:
|
||||
```sh
|
||||
source openvino_env/bin/activate
|
||||
```
|
||||
On Windows:
|
||||
```sh
|
||||
openvino_env\Scripts\activate
|
||||
```
|
||||
|
||||
### Step 3. Set Up and Update PIP to the Highest Version
|
||||
|
||||
Run the command below:
|
||||
```sh
|
||||
python -m pip install --upgrade pip
|
||||
```
|
||||
|
||||
### Step 4. Install the Package
|
||||
|
||||
Run the command below: <br>
|
||||
|
||||
```sh
|
||||
pip install openvino
|
||||
```
|
||||
|
||||
### Step 5. Verify that the Package Is Installed
|
||||
|
||||
Run the command below:
|
||||
```sh
|
||||
python -c "from openvino.runtime import Core; print(Core().available_devices)"
|
||||
```
|
||||
|
||||
If installation was successful, you will see the list of available devices.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
For general troubleshooting steps and issues, see [Troubleshooting Guide for OpenVINO Installation](https://docs.openvino.ai/2023.0/openvino_docs_get_started_guide_troubleshooting.html). The following sections also provide explanations to several error messages.
|
||||
|
||||
### Errors with Installing via PIP for Users in China
|
||||
|
||||
Users in China might encounter errors while downloading sources via PIP during OpenVINO™ installation. To resolve the issues, try the following solution:
|
||||
|
||||
* Add the download source using the ``-i`` parameter with the Python ``pip`` command. For example:
|
||||
|
||||
``` sh
|
||||
pip install openvino -i https://mirrors.aliyun.com/pypi/simple/
|
||||
```
|
||||
Use the ``--trusted-host`` parameter if the URL above is ``http`` instead of ``https``.
|
||||
|
||||
### ERROR:root:Could not find the Inference Engine or nGraph Python API.
|
||||
|
||||
On Windows*, some libraries are necessary to run OpenVINO. To resolve this issue, install the [C++ redistributable (.exe)](https://aka.ms/vs/17/release/vc_redist.x64.exe). You can also view a full download list on the [official support page](https://docs.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist).
|
||||
|
||||
### ImportError: libpython3.7m.so.1.0: cannot open shared object file: No such file or directory
|
||||
|
||||
To resolve missing external dependency on Ubuntu*, execute the following command:
|
||||
```sh
|
||||
sudo apt-get install libpython3.7
|
||||
```
|
||||
|
||||
## Additional Resources
|
||||
|
||||
- [Intel® Distribution of OpenVINO™ toolkit](https://software.intel.com/en-us/openvino-toolkit)
|
||||
- [OpenVINO™ Documentation](https://docs.openvino.ai/)
|
||||
- [OpenVINO™ Notebooks](https://github.com/openvinotoolkit/openvino_notebooks)
|
||||
- [OpenVINO Installation Selector Tool](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/download.html)
|
||||
|
||||
Copyright © 2018-2023 Intel Corporation
|
||||
> **LEGAL NOTICE**: Your use of this software and any required dependent software (the
|
||||
“Software Package”) is subject to the terms and conditions of the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.html) for the Software Package, which may also include notices, disclaimers, or
|
||||
license terms for third party or open source software included in or with the Software Package, and your use indicates your acceptance of all such terms. Please refer to the “third-party-programs.txt” or other similarly-named text file included with the Software Package for additional details.
|
||||
|
||||
>Intel is committed to the respect of human rights and avoiding complicity in human rights abuses, a policy reflected in the [Intel Global Human Rights Principles](https://www.intel.com/content/www/us/en/policy/policy-human-rights.html). Accordingly, by accessing the Intel material on this platform you agree that you will not use the material in a product or application that causes or contributes to a violation of an internationally recognized human right.
|
||||
@ -0,0 +1,233 @@
|
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||||
openvino/runtime/opset_utils.py,sha256=8BxAg0sSGoY_WIuBf0z6iIhckvJYE13Ah1MJbT6SJak,650
|
||||
openvino/runtime/passes/__init__.py,sha256=g54HC_ioch9cXs3Tm9pBGVo1gYqAUMiNaOPV1CSP2_4,699
|
||||
openvino/runtime/passes/__pycache__/__init__.cpython-310.pyc,,
|
||||
openvino/runtime/passes/__pycache__/graph_rewrite.cpython-310.pyc,,
|
||||
openvino/runtime/passes/__pycache__/manager.cpython-310.pyc,,
|
||||
openvino/runtime/passes/graph_rewrite.py,sha256=CzA_ldPL0oFHG7jM9OqOQ0DNAIVNjQjVMA4C_Afh-LA,1340
|
||||
openvino/runtime/passes/manager.py,sha256=s_t_3BCd44JNJ3sr84X7jqTn5pQTdln48gikUVP5ELM,865
|
||||
openvino/runtime/properties/__init__.py,sha256=wymwuMmR-X_Ww6XQo7dOPqcwxKd8hVxx5ZZA7k8_PS4,1614
|
||||
openvino/runtime/properties/__pycache__/__init__.cpython-310.pyc,,
|
||||
openvino/runtime/properties/hint/__init__.py,sha256=nv8HPhwfU6S_whesUlYBMknxUilTqSexglwoXbEjv3c,1048
|
||||
openvino/runtime/properties/hint/__pycache__/__init__.cpython-310.pyc,,
|
||||
openvino/runtime/properties/hint/__pycache__/overloads.cpython-310.pyc,,
|
||||
openvino/runtime/properties/hint/overloads.py,sha256=UT5zedbbOr3HbCr34OPgwFrFCkXv1QxWME5olpSVV-c,592
|
||||
openvino/runtime/utils/__init__.py,sha256=9_UFA71-b9EDRk1pdU30U432rZWAZ12vfLu3JgLcYQc,385
|
||||
openvino/runtime/utils/__pycache__/__init__.cpython-310.pyc,,
|
||||
openvino/runtime/utils/__pycache__/broadcasting.cpython-310.pyc,,
|
||||
openvino/runtime/utils/__pycache__/decorators.cpython-310.pyc,,
|
||||
openvino/runtime/utils/__pycache__/input_validation.cpython-310.pyc,,
|
||||
openvino/runtime/utils/__pycache__/node_factory.cpython-310.pyc,,
|
||||
openvino/runtime/utils/__pycache__/reduction.cpython-310.pyc,,
|
||||
openvino/runtime/utils/__pycache__/types.cpython-310.pyc,,
|
||||
openvino/runtime/utils/broadcasting.py,sha256=dQrkBjFo7yivycUI1L46xC8Os6J9UdsFqUiRbAojvtw,1372
|
||||
openvino/runtime/utils/data_helpers/__init__.py,sha256=hbS2zLhXXPbgYQad7YrNnOt5-TE3bhkrZL1m5YSOiV0,402
|
||||
openvino/runtime/utils/data_helpers/__pycache__/__init__.cpython-310.pyc,,
|
||||
openvino/runtime/utils/data_helpers/__pycache__/data_dispatcher.cpython-310.pyc,,
|
||||
openvino/runtime/utils/data_helpers/__pycache__/wrappers.cpython-310.pyc,,
|
||||
openvino/runtime/utils/data_helpers/data_dispatcher.py,sha256=JT1ZyCYe6D5V33cTJn9_kltHh_GhKEh72lIkzAhFEdE,11036
|
||||
openvino/runtime/utils/data_helpers/wrappers.py,sha256=pzyoxEMyTulDtvsKKDHcQJ9aVi38A5D8sws017R67Ck,4877
|
||||
openvino/runtime/utils/decorators.py,sha256=znuoUS2_Gyh8hkGLtysQyc854uW8aN8fdLAExw4oj2M,2046
|
||||
openvino/runtime/utils/input_validation.py,sha256=1OeAf6vbmfTaiU3Et6eMFWCXJ5Q0OM6xPP4sXPHM89w,4809
|
||||
openvino/runtime/utils/node_factory.py,sha256=tIBnXvQ2YwI0zcSkllJZkKH6mC7wCfsvMOLK8HpyYbg,5785
|
||||
openvino/runtime/utils/reduction.py,sha256=M5FxoM7XDNmguyRoEjx_7Iwsl59LRWL5GRXvVAT_9M8,835
|
||||
openvino/runtime/utils/types.py,sha256=6t74_PGRokrWKJ0PWZhtP6epRvgGG7XEg8HnGV5wjs0,4820
|
||||
openvino/utils.py,sha256=vbGQ-0h0ff9PUj5jXRnaFABT97HcmcagvSdqDv-mjAs,4482
|
||||
requirements.txt,sha256=4PoHh5l_sIAH_Y4_zZcRFau7raAs3UphQUaO7qnN8og,57
|
||||
@ -0,0 +1,6 @@
|
||||
Wheel-Version: 1.0
|
||||
Generator: bdist_wheel (0.40.0)
|
||||
Root-Is-Purelib: false
|
||||
Build: 11005
|
||||
Tag: cp310-cp310-manylinux2014_x86_64
|
||||
|
||||
@ -0,0 +1,10 @@
|
||||
“LEGAL NOTICE: Your use of this software and any required dependent software (the “Software Package”) is subject to the terms and conditions of the software license agreements for the Software Package, which may also include notices, disclaimers, or license terms for third party or open source software included in or with the Software Package, and your use indicates your acceptance of all such terms. Please refer to the “third-party-programs.txt” or other similarly-named text file included with the Software Package for additional details.
|
||||
|
||||
------------------------------------------------------------------------
|
||||
Components and their third party programs:
|
||||
|
||||
* OpenVINO(TM) Runtime (Apache 2.0): <install_root>/runtime-third-party-programs.txt, <install_root>/onednn_third-party-programs.txt, <install_root>/tbb_third-party-programs.txt
|
||||
|
||||
------------------------------------------------------------------------
|
||||
Licenses:
|
||||
* Apache 2.0 <install_root>/LICENSE
|
||||
@ -0,0 +1 @@
|
||||
openvino
|
||||
@ -0,0 +1,5 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore # mypy issue #1422
|
||||
Binary file not shown.
Binary file not shown.
@ -0,0 +1,24 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
# flake8: noqa
|
||||
|
||||
from openvino.utils import _add_openvino_libs_to_search_path
|
||||
|
||||
_add_openvino_libs_to_search_path()
|
||||
|
||||
from openvino._pyopenvino import get_version
|
||||
|
||||
__version__ = get_version()
|
||||
|
||||
from openvino._pyopenvino._offline_transformations import apply_fused_names_cleanup
|
||||
from openvino._pyopenvino._offline_transformations import apply_moc_transformations
|
||||
from openvino._pyopenvino._offline_transformations import apply_moc_legacy_transformations
|
||||
from openvino._pyopenvino._offline_transformations import apply_pot_transformations
|
||||
from openvino._pyopenvino._offline_transformations import apply_low_latency_transformation
|
||||
from openvino._pyopenvino._offline_transformations import apply_pruning_transformation
|
||||
from openvino._pyopenvino._offline_transformations import apply_make_stateful_transformation
|
||||
from openvino._pyopenvino._offline_transformations import compress_model_transformation
|
||||
from openvino._pyopenvino._offline_transformations import compress_quantize_weights_transformation
|
||||
from openvino._pyopenvino._offline_transformations import convert_sequence_to_tensor_iterator_transformation
|
||||
Binary file not shown.
Binary file not shown.
@ -0,0 +1,38 @@
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""
|
||||
Package: openvino
|
||||
Low level wrappers for the FrontEnd C++ API.
|
||||
"""
|
||||
|
||||
# flake8: noqa
|
||||
|
||||
from openvino.utils import _add_openvino_libs_to_search_path
|
||||
|
||||
_add_openvino_libs_to_search_path()
|
||||
|
||||
from openvino._pyopenvino import get_version
|
||||
|
||||
__version__ = get_version()
|
||||
|
||||
# main classes
|
||||
from openvino._pyopenvino import FrontEndManager
|
||||
from openvino._pyopenvino import FrontEnd
|
||||
from openvino._pyopenvino import InputModel
|
||||
from openvino._pyopenvino import NodeContext
|
||||
from openvino._pyopenvino import Place
|
||||
|
||||
# extensions
|
||||
from openvino._pyopenvino import DecoderTransformationExtension
|
||||
from openvino._pyopenvino import ConversionExtension
|
||||
from openvino._pyopenvino import OpExtension
|
||||
from openvino._pyopenvino import ProgressReporterExtension
|
||||
from openvino._pyopenvino import TelemetryExtension
|
||||
|
||||
# exceptions
|
||||
from openvino._pyopenvino import NotImplementedFailure
|
||||
from openvino._pyopenvino import InitializationFailure
|
||||
from openvino._pyopenvino import OpConversionFailure
|
||||
from openvino._pyopenvino import OpValidationFailure
|
||||
from openvino._pyopenvino import GeneralFailure
|
||||
Binary file not shown.
@ -0,0 +1,19 @@
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""
|
||||
Package: openvino
|
||||
Low level wrappers for the FrontEnd C++ API.
|
||||
"""
|
||||
|
||||
# flake8: noqa
|
||||
|
||||
from openvino.utils import _add_openvino_libs_to_search_path
|
||||
|
||||
_add_openvino_libs_to_search_path()
|
||||
|
||||
try:
|
||||
from openvino.frontend.onnx.py_onnx_frontend import ConversionExtensionONNX as ConversionExtension
|
||||
from openvino.frontend.onnx.py_onnx_frontend import OpExtensionONNX as OpExtension
|
||||
except ImportError as err:
|
||||
raise ImportError("OpenVINO ONNX frontend is not available, please make sure the frontend is built. " "{}".format(err))
|
||||
Binary file not shown.
Binary file not shown.
@ -0,0 +1,20 @@
|
||||
# Copyright (C) 2018-2021 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""
|
||||
Package: openvino
|
||||
Low level wrappers for the FrontEnd C++ API.
|
||||
"""
|
||||
|
||||
# flake8: noqa
|
||||
|
||||
from openvino.utils import _add_openvino_libs_to_search_path
|
||||
|
||||
_add_openvino_libs_to_search_path()
|
||||
|
||||
|
||||
try:
|
||||
from openvino.frontend.paddle.py_paddle_frontend import ConversionExtensionPaddle as ConversionExtension
|
||||
from openvino.frontend.paddle.py_paddle_frontend import OpExtensionPaddle as OpExtension
|
||||
except ImportError as err:
|
||||
raise ImportError("OpenVINO Paddle frontend is not available, please make sure the frontend is built." "{}".format(err))
|
||||
Binary file not shown.
Binary file not shown.
@ -0,0 +1,23 @@
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""
|
||||
Package: openvino
|
||||
Low level wrappers for the FrontEnd C++ API.
|
||||
"""
|
||||
|
||||
# flake8: noqa
|
||||
|
||||
from openvino.utils import _add_openvino_libs_to_search_path
|
||||
|
||||
_add_openvino_libs_to_search_path()
|
||||
|
||||
|
||||
try:
|
||||
from openvino.frontend.pytorch.py_pytorch_frontend import _FrontEndPytorchDecoder as Decoder
|
||||
from openvino.frontend.pytorch.py_pytorch_frontend import _Type as DecoderType
|
||||
from openvino.frontend.pytorch.py_pytorch_frontend import ConversionExtensionPytorch as ConversionExtension
|
||||
from openvino.frontend.pytorch.py_pytorch_frontend import OpExtensionPytorch as OpExtension
|
||||
except ImportError as err:
|
||||
raise ImportError("OpenVINO PyTorch frontend is not available, please make sure the frontend is built."
|
||||
"{}".format(err))
|
||||
Binary file not shown.
Binary file not shown.
@ -0,0 +1,435 @@
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
# flake8: noqa
|
||||
# mypy: ignore-errors
|
||||
|
||||
from openvino.frontend.pytorch.py_pytorch_frontend import _FrontEndPytorchDecoder as Decoder
|
||||
from openvino.frontend.pytorch.py_pytorch_frontend import _Type as DecoderType
|
||||
from openvino.runtime import op, PartialShape, Type as OVType, OVAny, Shape
|
||||
|
||||
import typing
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
|
||||
def get_type_from_py_type(value):
|
||||
if isinstance(value, float):
|
||||
return OVType.f32
|
||||
if isinstance(value, bool):
|
||||
return OVType.boolean
|
||||
if isinstance(value, int):
|
||||
# Python int is 64 bit, but we will convert it to int32 except cases when it can't fit in 32 bits
|
||||
if torch.iinfo(torch.int).min <= value <= torch.iinfo(torch.int).max:
|
||||
return OVType.i32
|
||||
return OVType.i64
|
||||
return OVType.dynamic
|
||||
|
||||
|
||||
def ivalue_to_constant(ivalue):
|
||||
ov_type = get_type_from_py_type(ivalue)
|
||||
if ov_type.is_static():
|
||||
return op.Constant(ov_type, Shape([]), [ivalue]).outputs()
|
||||
|
||||
if isinstance(ivalue, (list, tuple)):
|
||||
assert len(ivalue) > 0, "Can't deduce type for empty list"
|
||||
ov_type = get_type_from_py_type(ivalue[0])
|
||||
assert ov_type.is_static(), "Can't deduce type for list"
|
||||
return op.Constant(ov_type, Shape([len(ivalue)]), ivalue).outputs()
|
||||
|
||||
if isinstance(ivalue, torch.Tensor):
|
||||
if ivalue.dim() == 0:
|
||||
assert str(ivalue.dtype) in pt_to_ov_type_map, f"Type is not known {ivalue.dtype}"
|
||||
ov_type = pt_to_ov_type_map[str(ivalue.dtype)]
|
||||
ov_const = op.Constant(ov_type, Shape([]), [ivalue.item()])
|
||||
else:
|
||||
ivalue = ivalue.to(memory_format=torch.contiguous_format)
|
||||
narr = ivalue.numpy(force=True)
|
||||
if not narr.flags['C_CONTIGUOUS']:
|
||||
narr = np.ascontiguousarray(narr)
|
||||
ov_const = op.Constant(narr, shared_memory=True)
|
||||
return ov_const.outputs()
|
||||
return None
|
||||
|
||||
|
||||
def get_value_from_getattr(getattr_node, self_module):
|
||||
assert getattr_node.kind() == "prim::GetAttr", "Got node of kind not equal to prim::GetAttr"
|
||||
# GetAttr nodes can be nested
|
||||
stack = []
|
||||
while getattr_node.kind() == "prim::GetAttr":
|
||||
stack.append(getattr_node)
|
||||
inputs = list(getattr_node.inputs())
|
||||
if len(inputs) == 0:
|
||||
break
|
||||
getattr_node = inputs[0].node()
|
||||
module = self_module
|
||||
while len(stack) > 0:
|
||||
node = stack.pop()
|
||||
assert (hasattr(module, node.s("name")))
|
||||
module = getattr(module, node.s("name"))
|
||||
return module
|
||||
|
||||
|
||||
pt_to_ov_type_map = {
|
||||
"float": OVType.f32,
|
||||
"int": OVType.i32,
|
||||
"bool": OVType.boolean,
|
||||
"torch.float16": OVType.f16,
|
||||
"torch.float32": OVType.f32,
|
||||
"torch.float64": OVType.f64,
|
||||
"torch.uint8": OVType.u8,
|
||||
"torch.int8": OVType.i8,
|
||||
"torch.int32": OVType.i32,
|
||||
"torch.int64": OVType.i64,
|
||||
"torch.bool": OVType.boolean,
|
||||
"torch.DoubleTensor": OVType.f64,
|
||||
"torch.FloatTensor": OVType.f32,
|
||||
"torch.IntTensor": OVType.i32,
|
||||
"torch.LongTensor": OVType.i64,
|
||||
"torch.BoolTensor": OVType.boolean,
|
||||
}
|
||||
|
||||
|
||||
class TorchScriptPythonDecoder (Decoder):
|
||||
def __init__(self, pt_module, graph_element=None, example_input=None, freeze=True):
|
||||
Decoder.__init__(self)
|
||||
# We store every decoder created by this decoder so that all them are not deleted until the first decoder is deleted
|
||||
self.m_decoders = []
|
||||
self._input_signature = None
|
||||
if graph_element is None:
|
||||
try:
|
||||
pt_module = self._get_scripted_model(pt_module, example_input, freeze)
|
||||
except Exception as e:
|
||||
if example_input is not None:
|
||||
msg = "tracing or scripting"
|
||||
help_msg = ""
|
||||
else:
|
||||
msg = "scripting"
|
||||
help_msg = "Tracing sometimes provide better results, "
|
||||
"please provide valid 'example_input' argument. "
|
||||
raise RuntimeError(
|
||||
f"Couldn't get TorchScript module by {msg}. {help_msg}"
|
||||
"You can also provide TorchScript module that you obtained"
|
||||
" yourself, please refer to PyTorch documentation: "
|
||||
"https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html.")
|
||||
self.graph_element = pt_module.inlined_graph
|
||||
else:
|
||||
self.graph_element = graph_element
|
||||
self.pt_module = pt_module
|
||||
self.raw_inputs = list(self.graph_element.inputs())
|
||||
self.raw_outputs = list(self.graph_element.outputs())
|
||||
if self._input_signature is not None and "self" in self.raw_inputs[0].debugName():
|
||||
self._input_signature.insert(0, "self")
|
||||
|
||||
if isinstance(self.graph_element, torch.Graph):
|
||||
self._transform_tensor_list_constants_to_listconstruct(self.graph_element)
|
||||
self._transform_optional_constants(self.graph_element)
|
||||
|
||||
def _get_scripted_model(self, pt_module, example_inputs=None, freeze=True):
|
||||
import torch
|
||||
import inspect
|
||||
|
||||
def prepare_example_inputs(inputs, input_signature):
|
||||
if inputs is not None:
|
||||
if isinstance(inputs, dict):
|
||||
if input_signature is not None:
|
||||
ordered_inputs = []
|
||||
used_sign = []
|
||||
for key in input_signature:
|
||||
if key not in inputs:
|
||||
continue
|
||||
ordered_inputs.append(inputs[key])
|
||||
used_sign.append(key)
|
||||
inputs = ordered_inputs
|
||||
input_signature = used_sign
|
||||
else:
|
||||
inputs = list(inputs.values())
|
||||
input_signature = input_signature[:len(inputs)]
|
||||
if isinstance(inputs, torch.Tensor):
|
||||
inputs = [inputs]
|
||||
return inputs, input_signature
|
||||
|
||||
if isinstance(pt_module, torch.nn.Module):
|
||||
pt_module.eval()
|
||||
input_signature = None
|
||||
if isinstance(pt_module, torch.nn.Module) and not isinstance(pt_module, (torch.jit._trace.TopLevelTracedModule, torch.jit._script.RecursiveScriptModule)):
|
||||
input_signature = list(inspect.signature(pt_module.forward).parameters.keys())
|
||||
if example_inputs is None:
|
||||
scripted = torch.jit.script(pt_module)
|
||||
else:
|
||||
inputs, input_signature = prepare_example_inputs(example_inputs, input_signature)
|
||||
try:
|
||||
scripted = torch.jit.trace(pt_module, inputs)
|
||||
except Exception:
|
||||
try:
|
||||
scripted = torch.jit.script(pt_module)
|
||||
except Exception:
|
||||
scripted = torch.jit.trace(pt_module, inputs, strict=False)
|
||||
else:
|
||||
scripted = pt_module
|
||||
if freeze:
|
||||
try:
|
||||
f_model = torch.jit.freeze(scripted)
|
||||
except Exception:
|
||||
# usually freezing failed when model already frozen for inference
|
||||
f_model = scripted
|
||||
else:
|
||||
f_model = scripted
|
||||
self._input_signature = input_signature
|
||||
return f_model
|
||||
|
||||
def inputs(self) -> list:
|
||||
return [x.unique() for x in self.raw_inputs]
|
||||
|
||||
def get_input(self, index: int):
|
||||
return self.inputs()[index]
|
||||
|
||||
def get_input_debug_name(self, index: int) -> str:
|
||||
return self._raw_input(index).debugName()
|
||||
|
||||
def get_input_signature_name(self, index: int) -> str:
|
||||
if self._input_signature is not None and index < len(self._input_signature):
|
||||
return self._input_signature[index]
|
||||
return self.get_input_debug_name(index)
|
||||
|
||||
def get_input_shape(self, index: int):
|
||||
raw_input = self._raw_input(index)
|
||||
return self.get_shape_for_value(raw_input)
|
||||
|
||||
def get_input_type(self, index: int):
|
||||
raw_input = self._raw_input(index)
|
||||
return self.get_type_for_value(raw_input)
|
||||
|
||||
def get_output_debug_name(self, index: int) -> str:
|
||||
return self._raw_output(index).debugName()
|
||||
|
||||
def get_output_shape(self, index: int):
|
||||
output = self._raw_output(index)
|
||||
return self.get_shape_for_value(output)
|
||||
|
||||
def get_output_type(self, index: int):
|
||||
output = self._raw_output(index)
|
||||
return self.get_type_for_value(output)
|
||||
|
||||
def _get_known_type_for_value(self, pt_type):
|
||||
"""Returns known/unknown types wrapped as OVAny."""
|
||||
# Check for simple scalar types first
|
||||
if pt_type is None:
|
||||
return OVAny(OVType.dynamic)
|
||||
# TODO: Don't use str, use native types
|
||||
if str(pt_type) in pt_to_ov_type_map:
|
||||
return OVAny(pt_to_ov_type_map[str(pt_type)])
|
||||
elif isinstance(pt_type, torch.TensorType):
|
||||
# Tensor type, parse element type
|
||||
return OVAny(DecoderType.Tensor(self._get_known_type_for_value(pt_type.dtype())))
|
||||
elif isinstance(pt_type, torch.ListType):
|
||||
element_type = pt_type.getElementType()
|
||||
return OVAny(DecoderType.List(self._get_known_type_for_value(element_type)))
|
||||
elif isinstance(pt_type, (torch.StringType, torch.DeviceObjType)):
|
||||
return OVAny(DecoderType.Str())
|
||||
elif isinstance(pt_type, torch.NoneType):
|
||||
return OVAny(DecoderType.PyNone())
|
||||
else:
|
||||
# Not yet recognized
|
||||
return OVAny(OVType.dynamic)
|
||||
|
||||
def get_shape_for_value(self, value: torch.Value):
|
||||
if value.isCompleteTensor():
|
||||
ps = PartialShape(value.type().sizes())
|
||||
return ps
|
||||
else:
|
||||
# TODO: Recognize types that we can represent as a nested constructs with objects from DecoderType
|
||||
# If recognized, return scalar instead of dynamic. Scalar means a single value of that custom type.
|
||||
# See get_type_for_value for reference
|
||||
pass
|
||||
return PartialShape.dynamic()
|
||||
|
||||
def get_type_for_value(self, value: torch.Value):
|
||||
full_type = self._get_known_type_for_value(value.type())
|
||||
return full_type
|
||||
|
||||
def get_input_transpose_order(self, index: int) -> list:
|
||||
raw_input = self._raw_input(index)
|
||||
if raw_input.type() is not None and raw_input.type().kind() == "TensorType":
|
||||
strides = raw_input.type().strides()
|
||||
if strides is not None:
|
||||
return [s[0] for s in sorted(enumerate(strides), key=lambda x:x[1], reverse=True)]
|
||||
return []
|
||||
|
||||
def get_output_transpose_order(self, index: int) -> list:
|
||||
output = self._raw_output(index)
|
||||
if output.type() is not None and output.type().kind() == "TensorType":
|
||||
strides = output.type().strides()
|
||||
if strides is not None:
|
||||
return [s[0] for s in sorted(enumerate(strides), key=lambda x:x[1], reverse=True)]
|
||||
return []
|
||||
|
||||
def get_subgraph_size(self) -> int:
|
||||
if isinstance(self.graph_element, torch.Node):
|
||||
return len(self.get_subgraphs())
|
||||
else:
|
||||
return 1
|
||||
|
||||
def visit_subgraph(self, node_visitor) -> None:
|
||||
# make sure topological order is satisfied
|
||||
for node in self.graph_element.nodes():
|
||||
decoder = TorchScriptPythonDecoder(self.pt_module, node)
|
||||
self.m_decoders.append(decoder)
|
||||
node_visitor(decoder)
|
||||
|
||||
def get_subgraphs(self) -> list:
|
||||
if self.graph_element.kind() == "prim::PythonOp":
|
||||
if "Subgraph" in self.graph_element.attributeNames():
|
||||
assert isinstance(self.graph_element, torch.Node), "Graph element must be of type torch.Node."
|
||||
return [getattr(self.graph_element, self.graph_element.kindOf("Subgraph"))("Subgraph")]
|
||||
else:
|
||||
# Attribute "Subgraph" is only available if Graph was created using tracing.
|
||||
# TODO Find way to extract subgraph for scripted Graph.
|
||||
return []
|
||||
return list(self.graph_element.blocks())
|
||||
|
||||
def get_subgraph_decoder(self, index: int):
|
||||
decoder = TorchScriptPythonDecoder(self.pt_module, self.get_subgraphs()[index])
|
||||
self.m_decoders.append(decoder)
|
||||
return decoder
|
||||
|
||||
def get_op_type(self) -> str:
|
||||
assert isinstance(self.graph_element, torch.Node), "Function can be called only when self.graph_element is of type torch.Node"
|
||||
return self.graph_element.kind()
|
||||
|
||||
def get_schema(self) -> str:
|
||||
return self.graph_element.schema()
|
||||
|
||||
def outputs(self) -> list:
|
||||
return [x.unique() for x in self.raw_outputs]
|
||||
|
||||
def _raw_output(self, index: int):
|
||||
return self.raw_outputs[index]
|
||||
|
||||
def _raw_input(self, index: int):
|
||||
return self.raw_inputs[index]
|
||||
|
||||
def num_of_outputs(self):
|
||||
return len(self.raw_outputs)
|
||||
|
||||
def output(self, index: int):
|
||||
return self.outputs()[index]
|
||||
|
||||
def mark_node(self, node):
|
||||
return node
|
||||
|
||||
def try_decode_get_attr(self):
|
||||
pt_value = get_value_from_getattr(self.graph_element, self.pt_module)
|
||||
assert pt_value is not None, "Couldn't retrieve value from prim::GetAttr"
|
||||
if not isinstance(pt_value, (torch.jit.ScriptModule, torch.jit.TracedModule)):
|
||||
return ivalue_to_constant(pt_value)
|
||||
else:
|
||||
return []
|
||||
|
||||
def as_constant(self):
|
||||
if not isinstance(self.graph_element, torch.Node):
|
||||
return None
|
||||
if not self.get_op_type() == "prim::Constant":
|
||||
return None
|
||||
pt_value = self._raw_output(0)
|
||||
pt_type = pt_value.type()
|
||||
if isinstance(pt_type, torch.TensorType):
|
||||
return ivalue_to_constant(pt_value.toIValue())
|
||||
if isinstance(pt_type, torch.ListType):
|
||||
return self._as_constant_list(pt_value)
|
||||
return ivalue_to_constant(pt_value.toIValue())
|
||||
|
||||
def as_string(self):
|
||||
if self.get_op_type() == "prim::Constant":
|
||||
pt_value = self._raw_output(0)
|
||||
if str(pt_value.type()) in ["torch.StringType", "str"]:
|
||||
return pt_value.toIValue()
|
||||
elif str(pt_value.type()) == "Device":
|
||||
return pt_value.toIValue().type
|
||||
elif self.get_op_type() == "prim::device":
|
||||
return self._get_device_string()
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def _as_constant_list(pt_value: torch.Value):
|
||||
# For now it is treat a list as a 1D tensor; it is required by converters to avoid need to massively
|
||||
# rewrite them in that part where constant attributes are queried
|
||||
pt_element_type = str(pt_value.type().getElementType())
|
||||
ivalue = pt_value.toIValue()
|
||||
is_known_type = pt_element_type in pt_to_ov_type_map
|
||||
|
||||
if is_known_type:
|
||||
ovtype = pt_to_ov_type_map[pt_element_type]
|
||||
ovshape = PartialShape([len(ivalue)])
|
||||
ov_const = op.Constant(ovtype, ovshape.get_shape(), ivalue)
|
||||
return ov_const.outputs()
|
||||
|
||||
def _get_device_string(self) -> str:
|
||||
assert self.graph_element.kind() == "prim::device", "This function can be called for prim::device node."
|
||||
value = self.raw_inputs[0]
|
||||
if value.type().isSubtypeOf(torch.TensorType.get()):
|
||||
tensor = typing.cast(torch.TensorType, value.type())
|
||||
device = tensor.device()
|
||||
if device:
|
||||
return str(device)
|
||||
# Device cannot be statically determined.
|
||||
return "cpu"
|
||||
|
||||
def input_is_none(self, index: int) -> bool:
|
||||
if index >= len(self.inputs()) or self._raw_input(index) is None:
|
||||
return True
|
||||
else:
|
||||
r_input = self._raw_input(index)
|
||||
if str(r_input.type()) in ["torch.NoneType", "NoneType"]:
|
||||
return True
|
||||
else:
|
||||
in_node = r_input.node()
|
||||
if in_node.kind() == "prim::GetAttr":
|
||||
pt_value = get_value_from_getattr(in_node, self.pt_module)
|
||||
return pt_value is None
|
||||
return False
|
||||
|
||||
@staticmethod
|
||||
def _transform_tensor_list_constants_to_listconstruct(graph: torch.Graph):
|
||||
# Function replaces prim::Constant containing List of Tensors with
|
||||
# prim::ListConstruct containing prim::Constant Tensors.
|
||||
assert isinstance(graph, torch.Graph), "Function can be called only with parameters of type torch.Graph."
|
||||
for node in graph.nodes():
|
||||
if node.kind() != "prim::Constant":
|
||||
continue
|
||||
output_type = node.output().type()
|
||||
allowed_types = [
|
||||
output_type.isSubtypeOf(torch.ListType.ofTensors()),
|
||||
output_type.isSubtypeOf(torch.ListType(torch.OptionalType.ofTensor())),
|
||||
]
|
||||
if not any(allowed_types):
|
||||
continue
|
||||
const_inputs = []
|
||||
for val in node.output().toIValue():
|
||||
const_input = graph.insertConstant(val)
|
||||
const_input.node().moveBefore(node)
|
||||
const_input.node().copyMetadata(node)
|
||||
const_inputs.append(const_input)
|
||||
|
||||
replacement = graph.create("prim::ListConstruct", const_inputs)
|
||||
replacement.insertBefore(node)
|
||||
replacement.output().setType(torch.ListType.ofTensors())
|
||||
replacement.copyMetadata(node)
|
||||
node.output().replaceAllUsesWith(replacement.output())
|
||||
|
||||
@staticmethod
|
||||
def _transform_optional_constants(graph: torch.Graph):
|
||||
# Function replaces prim::Constant containing torch.OptionalType with
|
||||
# prim::Constant containing torch.NoneType or type of IValue.
|
||||
assert isinstance(graph, torch.Graph), "Function can be called only with parameters of type torch.Graph."
|
||||
for node in graph.nodes():
|
||||
if node.kind() != "prim::Constant":
|
||||
continue
|
||||
output_type = node.output().type()
|
||||
if not isinstance(output_type, torch.OptionalType):
|
||||
continue
|
||||
value = node.output().toIValue()
|
||||
const_input = graph.insertConstant(value)
|
||||
const_input.node().moveBefore(node)
|
||||
const_input.node().copyMetadata(node)
|
||||
node.output().replaceAllUsesWith(const_input)
|
||||
Binary file not shown.
Binary file not shown.
@ -0,0 +1,98 @@
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
# flake8: noqa
|
||||
# mypy: ignore-errors
|
||||
|
||||
import logging
|
||||
import os
|
||||
import torch
|
||||
from torch._dynamo.backends.common import fake_tensor_unsupported
|
||||
from torch._dynamo.backends.registry import register_backend
|
||||
from torch._inductor.compile_fx import compile_fx
|
||||
|
||||
from openvino.frontend import FrontEndManager
|
||||
from openvino.runtime import Core, Type, PartialShape
|
||||
from openvino.frontend.pytorch.decoder import TorchScriptPythonDecoder
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
"""
|
||||
This is a preview feature in OpenVINO. Torchscript backend
|
||||
enables users to compile PyTorch models using torch.compile
|
||||
with OpenVINO as a target backend in PyTorch applications
|
||||
|
||||
Sample usage:
|
||||
This sample code loads resnet50 torchvision model and compiles it using torch dynamo.
|
||||
We can then use this model for inference. We only need to add two lines of code to
|
||||
the Pytorch applications which are marked in the code below
|
||||
|
||||
1) import openvino.frontend.pytorch.torchdynamo.backend
|
||||
model = torchvision.models.resnet50()
|
||||
2) model = torch.compile(model, backend="openvino")
|
||||
"""
|
||||
|
||||
|
||||
@register_backend
|
||||
@fake_tensor_unsupported
|
||||
def openvino(subgraph, example_inputs):
|
||||
return ts_openvino(subgraph, example_inputs)
|
||||
|
||||
|
||||
def ts_openvino(subgraph, example_inputs):
|
||||
try:
|
||||
model = torch.jit.script(subgraph)
|
||||
model.eval()
|
||||
fr_model = torch.jit.freeze(model)
|
||||
|
||||
core = Core()
|
||||
fe_manager = FrontEndManager()
|
||||
fe = fe_manager.load_by_framework('pytorch')
|
||||
dtype_mapping = {
|
||||
torch.float64: Type.f64,
|
||||
torch.float32: Type.f32,
|
||||
torch.float16: Type.f16,
|
||||
torch.int64: Type.i64,
|
||||
torch.int32: Type.i32,
|
||||
torch.uint8: Type.u8,
|
||||
torch.int8: Type.i8,
|
||||
torch.bool: Type.boolean,
|
||||
}
|
||||
decoder = TorchScriptPythonDecoder(fr_model)
|
||||
|
||||
# TODO: Use convert_model instead when mo --convert_model api becomes a part of OV runtime
|
||||
im = fe.load(decoder)
|
||||
om = fe.convert(im)
|
||||
|
||||
for idx, input_data in enumerate(example_inputs):
|
||||
om.inputs[idx].get_node().set_element_type(dtype_mapping[input_data.dtype])
|
||||
om.inputs[idx].get_node().set_partial_shape(PartialShape(list(input_data.shape)))
|
||||
om.validate_nodes_and_infer_types()
|
||||
|
||||
device = "CPU"
|
||||
if (os.getenv("OPENVINO_TS_BACKEND_DEVICE") is not None):
|
||||
device = os.getenv("OPENVINO_TS_BACKEND_DEVICE")
|
||||
assert device in core.available_devices, "Specified device " + device + " is not in the list of OpenVINO Available Devices"
|
||||
|
||||
compiled_model = core.compile_model(om, device)
|
||||
|
||||
def _call(*args):
|
||||
if not hasattr(_call, "execute_on_ov"):
|
||||
_call.execute_on_ov = True
|
||||
execute_on_ov = getattr(_call, "execute_on_ov")
|
||||
if execute_on_ov:
|
||||
ov_inputs = [a.detach().cpu().numpy() for a in args]
|
||||
try:
|
||||
res = compiled_model(ov_inputs)
|
||||
except Exception as e:
|
||||
log.debug(f"Failed in OpenVINO execution: {e}")
|
||||
_call.execute_on_ov = False
|
||||
return subgraph.forward(*args)
|
||||
result = [torch.from_numpy(res[out]) for out in compiled_model.outputs]
|
||||
return result
|
||||
else:
|
||||
return subgraph.forward(*args)
|
||||
return _call
|
||||
except Exception as e:
|
||||
log.debug(f"Failed in compilation: {e}")
|
||||
return compile_fx(subgraph, example_inputs)
|
||||
@ -0,0 +1,19 @@
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""
|
||||
Package: openvino
|
||||
Low level wrappers for the FrontEnd C++ API.
|
||||
"""
|
||||
|
||||
# flake8: noqa
|
||||
|
||||
from openvino.utils import _add_openvino_libs_to_search_path
|
||||
|
||||
_add_openvino_libs_to_search_path()
|
||||
|
||||
try:
|
||||
from openvino.frontend.tensorflow.py_tensorflow_frontend import ConversionExtensionTensorflow as ConversionExtension
|
||||
from openvino.frontend.tensorflow.py_tensorflow_frontend import OpExtensionTensorflow as OpExtension
|
||||
except ImportError as err:
|
||||
raise ImportError("OpenVINO Tensorflow frontend is not available, please make sure the frontend is built. " "{}".format(err))
|
||||
Binary file not shown.
@ -0,0 +1,6 @@
|
||||
# Copyright (C) 2022 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
# flake8: noqa
|
||||
|
||||
from openvino.helpers.packing import pack_data, unpack_data
|
||||
Binary file not shown.
Binary file not shown.
@ -0,0 +1,87 @@
|
||||
# Copyright (C) 2022 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
# flake8: noqa
|
||||
|
||||
import numpy as np
|
||||
from typing import Union
|
||||
from openvino.runtime import Type, Shape
|
||||
|
||||
|
||||
def pack_data(array: np.ndarray, type: Type) -> np.ndarray:
|
||||
"""Represent array values as u1,u4 or i4 openvino element type and pack them into uint8 numpy array.
|
||||
|
||||
If the number of elements in array is odd we pad them with zero value to be able to fit the bit
|
||||
sequence into the uint8 array.
|
||||
|
||||
Example: two uint8 values - [7, 8] can be represented as uint4 values and be packed into one int8
|
||||
value - [120], because [7, 8] bit representation is [0111, 1000] will be viewed
|
||||
as [01111000], which is bit representation of [120].
|
||||
|
||||
:param array: numpy array with values to pack.
|
||||
:type array: numpy array
|
||||
:param type: Type to interpret the array values. Type must be u1, u4 or i4.
|
||||
:type type: openvino.runtime.Type
|
||||
"""
|
||||
assert type in [Type.u1, Type.u4, Type.i4], "Packing algorithm for the" "data types stored in 1, 2 or 4 bits"
|
||||
|
||||
minimum_regular_dtype = np.int8 if type == Type.i4 else np.uint8
|
||||
casted_to_regular_type = array.astype(dtype=minimum_regular_dtype, casting="unsafe")
|
||||
if not np.array_equal(casted_to_regular_type, array):
|
||||
raise RuntimeError(f'The conversion of array "{array}" to dtype' f' "{casted_to_regular_type}" results in rounding')
|
||||
|
||||
data_size = casted_to_regular_type.size
|
||||
num_bits = type.bitwidth
|
||||
|
||||
assert num_bits < 8 and 8 % num_bits == 0, "Packing algorithm for the" "data types stored in 1, 2 or 4 bits"
|
||||
num_values_fitting_into_uint8 = 8 // num_bits
|
||||
pad = (-data_size) % num_values_fitting_into_uint8
|
||||
|
||||
flattened = casted_to_regular_type.flatten()
|
||||
padded = np.concatenate((flattened, np.zeros([pad], dtype=minimum_regular_dtype))) # type: ignore
|
||||
assert padded.size % num_values_fitting_into_uint8 == 0
|
||||
|
||||
bit_order_little = (padded[:, None] & (1 << np.arange(num_bits)) > 0).astype(minimum_regular_dtype)
|
||||
bit_order_big = np.flip(bit_order_little, axis=1) # type: ignore
|
||||
bit_order_big_flattened = bit_order_big.flatten()
|
||||
|
||||
return np.packbits(bit_order_big_flattened)
|
||||
|
||||
|
||||
def unpack_data(array: np.ndarray, type: Type, shape: Union[list, Shape]) -> np.ndarray:
|
||||
"""Extract openvino element type values from array into new uint8/int8 array given shape.
|
||||
|
||||
Example: uint8 value [120] can be represented as two u4 values and be unpacked into [7, 8]
|
||||
because [120] bit representation is [01111000] will be viewed as [0111, 1000],
|
||||
which is bit representation of [7, 8].
|
||||
|
||||
:param array: numpy array to unpack.
|
||||
:type array: numpy array
|
||||
:param type: Type to extract from array values. Type must be u1, u4 or i4.
|
||||
:type type: openvino.runtime.Type
|
||||
:param shape: the new shape for the unpacked array.
|
||||
:type shape: Union[list, openvino.runtime.Shape]
|
||||
"""
|
||||
assert type in [Type.u1, Type.u4, Type.i4], "Unpacking algorithm for the" "data types stored in 1, 2 or 4 bits"
|
||||
unpacked = np.unpackbits(array.view(np.uint8))
|
||||
shape = list(shape)
|
||||
if type.bitwidth == 1:
|
||||
return np.resize(unpacked, shape)
|
||||
else:
|
||||
unpacked = unpacked.reshape(-1, type.bitwidth)
|
||||
padding_shape = (unpacked.shape[0], 8 - type.bitwidth)
|
||||
padding = np.ndarray(padding_shape, np.uint8) # type: np.ndarray
|
||||
if type == Type.i4:
|
||||
for axis, bits in enumerate(unpacked):
|
||||
if bits[0] == 1:
|
||||
padding[axis] = np.ones((padding_shape[1],), np.uint8)
|
||||
else:
|
||||
padding[axis] = np.zeros((padding_shape[1],), np.uint8)
|
||||
else:
|
||||
padding = np.zeros(padding_shape, np.uint8)
|
||||
padded = np.concatenate((padding, unpacked), 1) # type: ignore
|
||||
packed = np.packbits(padded, 1)
|
||||
if type == Type.i4:
|
||||
return np.resize(packed, shape).astype(dtype=np.int8)
|
||||
else:
|
||||
return np.resize(packed, shape)
|
||||
@ -0,0 +1,33 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import os
|
||||
import sys
|
||||
|
||||
if sys.platform == "win32":
|
||||
# Installer, yum, pip installs openvino dlls to the different directories
|
||||
# and those paths need to be visible to the openvino modules
|
||||
#
|
||||
# If you're using a custom installation of openvino,
|
||||
# add the location of openvino dlls to your system PATH.
|
||||
#
|
||||
# looking for the libs in the pip installation path by default.
|
||||
openvino_libs = [os.path.join(os.path.dirname(__file__), "..", "..", "openvino", "libs")]
|
||||
# setupvars.bat script set all libs paths to OPENVINO_LIB_PATHS environment variable.
|
||||
openvino_libs_installer = os.getenv("OPENVINO_LIB_PATHS")
|
||||
if openvino_libs_installer:
|
||||
openvino_libs.extend(openvino_libs_installer.split(";"))
|
||||
for lib in openvino_libs:
|
||||
lib_path = os.path.join(os.path.dirname(__file__), lib)
|
||||
if os.path.isdir(lib_path):
|
||||
# On Windows, with Python >= 3.8, DLLs are no longer imported from the PATH.
|
||||
if (3, 8) <= sys.version_info:
|
||||
os.add_dll_directory(os.path.abspath(lib_path))
|
||||
else:
|
||||
os.environ["PATH"] = os.path.abspath(lib_path) + ";" + os.environ["PATH"]
|
||||
|
||||
from .ie_api import *
|
||||
|
||||
__all__ = ["IENetwork", "TensorDesc", "IECore", "Blob", "PreProcessInfo", "get_version"]
|
||||
__version__ = get_version() # type: ignore
|
||||
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114134
modules/openvino/python3.10/dist-packages/openvino/libs/cache.json
Normal file
114134
modules/openvino/python3.10/dist-packages/openvino/libs/cache.json
Normal file
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Load Diff
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@ -0,0 +1,88 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
# flake8: noqa
|
||||
# type: ignore
|
||||
|
||||
import warnings
|
||||
|
||||
warnings.warn(
|
||||
message="The module is private and following namespace "
|
||||
"`offline_transformations` will be removed in the future.",
|
||||
category=FutureWarning,
|
||||
)
|
||||
|
||||
from openvino.utils import _add_openvino_libs_to_search_path, deprecated
|
||||
|
||||
_add_openvino_libs_to_search_path()
|
||||
|
||||
from openvino._pyopenvino import get_version
|
||||
from openvino._pyopenvino import serialize as _base_serialize
|
||||
import openvino._pyopenvino._offline_transformations as _base
|
||||
|
||||
__version__ = get_version()
|
||||
|
||||
|
||||
@deprecated(
|
||||
version="2023.1",
|
||||
message="The module is private and following namespace "
|
||||
"`offline_transformations` will be removed in "
|
||||
"the future, use `openvino.runtime.passes` instead!",
|
||||
)
|
||||
def serialize(model, xml_path, bin_path, version):
|
||||
_base_serialize(model, xml_path, bin_path, version)
|
||||
|
||||
|
||||
@deprecated(version="2023.1", message="The module is private and following namespace " "`offline_transformations` will be removed in " "the future.")
|
||||
def apply_fused_names_cleanup(model):
|
||||
_base.apply_fused_names_cleanup(model)
|
||||
|
||||
|
||||
@deprecated(version="2023.1", message="The module is private and following namespace " "`offline_transformations` will be removed in " "the future.")
|
||||
def apply_moc_transformations(model, cf, smart_reshape=False):
|
||||
_base.apply_moc_transformations(model, cf, smart_reshape)
|
||||
|
||||
|
||||
@deprecated(version="2023.1", message="The module is private and following namespace " "`offline_transformations` will be removed in " "the future.")
|
||||
def apply_moc_legacy_transformations(model, params_with_custom_types):
|
||||
_base.apply_moc_legacy_transformations(model, params_with_custom_types)
|
||||
|
||||
|
||||
@deprecated(version="2023.1", message="The module is private and following namespace " "`offline_transformations` will be removed in " "the future.")
|
||||
def apply_pot_transformations(model, device):
|
||||
_base.apply_pot_transformations(model, device)
|
||||
|
||||
|
||||
@deprecated(version="2023.1", message="The module is private and following namespace " "`offline_transformations` will be removed in " "the future.")
|
||||
def apply_low_latency_transformation(model, use_const_initializer):
|
||||
_base.apply_low_latency_transformation(model, use_const_initializer)
|
||||
|
||||
|
||||
@deprecated(version="2023.1", message="The module is private and following namespace " "`offline_transformations` will be removed in " "the future.")
|
||||
def apply_pruning_transformation(model):
|
||||
_base.apply_pruning_transformation(model)
|
||||
|
||||
|
||||
@deprecated(version="2023.1", message="The module is private and following namespace " "`offline_transformations` will be removed in " "the future.")
|
||||
def apply_make_stateful_transformation(model, param_res_names):
|
||||
_base.apply_make_stateful_transformation(model, param_res_names)
|
||||
|
||||
|
||||
@deprecated(version="2023.1", message="The module is private and following namespace " "`offline_transformations` will be removed in " "the future.")
|
||||
def compress_model_transformation(model):
|
||||
_base.compress_model_transformation(model)
|
||||
|
||||
|
||||
@deprecated(version="2023.1",
|
||||
message="The module is private and following namespace "
|
||||
"`offline_transformations` will be removed in the future. "
|
||||
"This transformation will be enabled as a part of read_model method of ov::Core "
|
||||
"and convert method of ov::Frontend classes.")
|
||||
def compress_quantize_weights_transformation(model):
|
||||
_base.compress_quantize_weights_transformation(model)
|
||||
|
||||
|
||||
@deprecated(version="2023.1", message="The module is private and following namespace " "`offline_transformations` will be removed in " "the future.")
|
||||
def convert_sequence_to_tensor_iterator_transformation(model):
|
||||
_base.convert_sequence_to_tensor_iterator_transformation(model)
|
||||
Binary file not shown.
@ -0,0 +1,30 @@
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""
|
||||
Package: openvino
|
||||
Low level wrappers for the PrePostProcessing C++ API.
|
||||
"""
|
||||
|
||||
# flake8: noqa
|
||||
|
||||
from openvino.utils import _add_openvino_libs_to_search_path
|
||||
|
||||
_add_openvino_libs_to_search_path()
|
||||
|
||||
from openvino._pyopenvino import get_version
|
||||
|
||||
__version__ = get_version()
|
||||
|
||||
# main classes
|
||||
from openvino._pyopenvino.preprocess import InputInfo
|
||||
from openvino._pyopenvino.preprocess import OutputInfo
|
||||
from openvino._pyopenvino.preprocess import InputTensorInfo
|
||||
from openvino._pyopenvino.preprocess import OutputTensorInfo
|
||||
from openvino._pyopenvino.preprocess import InputModelInfo
|
||||
from openvino._pyopenvino.preprocess import OutputModelInfo
|
||||
from openvino._pyopenvino.preprocess import PrePostProcessor
|
||||
from openvino._pyopenvino.preprocess import PreProcessSteps
|
||||
from openvino._pyopenvino.preprocess import PostProcessSteps
|
||||
from openvino._pyopenvino.preprocess import ColorFormat
|
||||
from openvino._pyopenvino.preprocess import ResizeAlgorithm
|
||||
Binary file not shown.
@ -0,0 +1,12 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
# flake8: noqa
|
||||
# type: ignore
|
||||
|
||||
import warnings
|
||||
|
||||
warnings.warn(message="The module is private and following namespace " "`pyopenvino` will be removed in the future", category=FutureWarning)
|
||||
|
||||
from openvino._pyopenvino import *
|
||||
Binary file not shown.
@ -0,0 +1,86 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""openvino module namespace, exposing factory functions for all ops and other classes."""
|
||||
# noqa: F401
|
||||
|
||||
from openvino.utils import _add_openvino_libs_to_search_path
|
||||
|
||||
_add_openvino_libs_to_search_path()
|
||||
|
||||
from openvino._pyopenvino import get_version
|
||||
|
||||
__version__ = get_version()
|
||||
|
||||
# Openvino pybind bindings and python extended classes
|
||||
from openvino._pyopenvino import Dimension
|
||||
from openvino._pyopenvino import Model
|
||||
from openvino._pyopenvino import Input
|
||||
from openvino._pyopenvino import Output
|
||||
from openvino._pyopenvino import Node
|
||||
from openvino._pyopenvino import Type
|
||||
from openvino._pyopenvino import PartialShape
|
||||
from openvino._pyopenvino import Shape
|
||||
from openvino._pyopenvino import Strides
|
||||
from openvino._pyopenvino import CoordinateDiff
|
||||
from openvino._pyopenvino import DiscreteTypeInfo
|
||||
from openvino._pyopenvino import AxisSet
|
||||
from openvino._pyopenvino import AxisVector
|
||||
from openvino._pyopenvino import Coordinate
|
||||
from openvino._pyopenvino import Layout
|
||||
from openvino._pyopenvino import ConstOutput
|
||||
from openvino._pyopenvino import layout_helpers
|
||||
from openvino._pyopenvino import OVAny
|
||||
from openvino._pyopenvino import RTMap
|
||||
from openvino.runtime.ie_api import Core
|
||||
from openvino.runtime.ie_api import CompiledModel
|
||||
from openvino.runtime.ie_api import InferRequest
|
||||
from openvino.runtime.ie_api import AsyncInferQueue
|
||||
from openvino._pyopenvino import Version
|
||||
from openvino._pyopenvino import Tensor
|
||||
from openvino._pyopenvino import Extension
|
||||
from openvino._pyopenvino import ProfilingInfo
|
||||
from openvino._pyopenvino import get_batch
|
||||
from openvino._pyopenvino import set_batch
|
||||
from openvino._pyopenvino import serialize
|
||||
from openvino._pyopenvino import shutdown
|
||||
|
||||
# Import opsets
|
||||
from openvino.runtime import opset1
|
||||
from openvino.runtime import opset2
|
||||
from openvino.runtime import opset3
|
||||
from openvino.runtime import opset4
|
||||
from openvino.runtime import opset5
|
||||
from openvino.runtime import opset6
|
||||
from openvino.runtime import opset7
|
||||
from openvino.runtime import opset8
|
||||
from openvino.runtime import opset9
|
||||
from openvino.runtime import opset10
|
||||
from openvino.runtime import opset11
|
||||
|
||||
# Import properties API
|
||||
from openvino.runtime import properties
|
||||
|
||||
# Helper functions for openvino module
|
||||
from openvino.runtime.ie_api import tensor_from_file
|
||||
from openvino.runtime.ie_api import compile_model
|
||||
|
||||
|
||||
# Extend Node class to support binary operators
|
||||
Node.__add__ = opset11.add
|
||||
Node.__sub__ = opset11.subtract
|
||||
Node.__mul__ = opset11.multiply
|
||||
Node.__div__ = opset11.divide
|
||||
Node.__truediv__ = opset11.divide
|
||||
Node.__radd__ = lambda left, right: opset11.add(right, left)
|
||||
Node.__rsub__ = lambda left, right: opset11.subtract(right, left)
|
||||
Node.__rmul__ = lambda left, right: opset11.multiply(right, left)
|
||||
Node.__rdiv__ = lambda left, right: opset11.divide(right, left)
|
||||
Node.__rtruediv__ = lambda left, right: opset11.divide(right, left)
|
||||
Node.__eq__ = opset11.equal
|
||||
Node.__ne__ = opset11.not_equal
|
||||
Node.__lt__ = opset11.less
|
||||
Node.__le__ = opset11.less_equal
|
||||
Node.__gt__ = opset11.greater
|
||||
Node.__ge__ = opset11.greater_equal
|
||||
Binary file not shown.
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Binary file not shown.
Binary file not shown.
@ -0,0 +1,17 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""openvino exceptions hierarchy. All exceptions are descendants of OVError."""
|
||||
|
||||
|
||||
class OVError(Exception):
|
||||
"""Base class for OV exceptions."""
|
||||
|
||||
|
||||
class UserInputError(OVError):
|
||||
"""User provided unexpected input."""
|
||||
|
||||
|
||||
class OVTypeError(OVError, TypeError):
|
||||
"""Type mismatch error."""
|
||||
@ -0,0 +1,465 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from typing import Any, Iterable, Union, Dict, Optional
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
|
||||
from openvino._pyopenvino import Model
|
||||
from openvino._pyopenvino import Core as CoreBase
|
||||
from openvino._pyopenvino import CompiledModel as CompiledModelBase
|
||||
from openvino._pyopenvino import AsyncInferQueue as AsyncInferQueueBase
|
||||
from openvino._pyopenvino import ConstOutput
|
||||
from openvino._pyopenvino import Tensor
|
||||
|
||||
from openvino.runtime.utils.data_helpers import (
|
||||
OVDict,
|
||||
_InferRequestWrapper,
|
||||
_data_dispatch,
|
||||
tensor_from_file,
|
||||
)
|
||||
|
||||
|
||||
class InferRequest(_InferRequestWrapper):
|
||||
"""InferRequest class represents infer request which can be run in asynchronous or synchronous manners."""
|
||||
|
||||
def infer(self, inputs: Any = None, shared_memory: bool = False) -> OVDict:
|
||||
"""Infers specified input(s) in synchronous mode.
|
||||
|
||||
Blocks all methods of InferRequest while request is running.
|
||||
Calling any method will lead to throwing exceptions.
|
||||
|
||||
The allowed types of keys in the `inputs` dictionary are:
|
||||
|
||||
(1) `int`
|
||||
(2) `str`
|
||||
(3) `openvino.runtime.ConstOutput`
|
||||
|
||||
The allowed types of values in the `inputs` are:
|
||||
|
||||
(1) `numpy.ndarray` and all the types that are castable to it, e.g. `torch.Tensor`
|
||||
(2) `openvino.runtime.Tensor`
|
||||
|
||||
Can be called with only one `openvino.runtime.Tensor` or `numpy.ndarray`,
|
||||
it will work only with one-input models. When model has more inputs,
|
||||
function throws error.
|
||||
|
||||
:param inputs: Data to be set on input tensors.
|
||||
:type inputs: Any, optional
|
||||
:param shared_memory: Enables `shared_memory` mode.
|
||||
|
||||
If set to `False` inputs the data dispatcher will safely copy data
|
||||
to existing Tensors (including up- or down-casting according to data type,
|
||||
resizing of the input Tensor). Keeps Tensor inputs "as-is".
|
||||
|
||||
If set to `True` the data dispatcher tries to provide "zero-copy"
|
||||
Tensors for every input in form of:
|
||||
* `numpy.ndarray` and all the types that are castable to it, e.g. `torch.Tensor`
|
||||
Data that is going to be copied:
|
||||
* `numpy.ndarray` which are not C contiguous
|
||||
* inputs which data types are mismatched from Infer Request's inputs
|
||||
* inputs that should be in `BF16` data type
|
||||
* scalar inputs (i.e. `np.float_`/`int`/`float`)
|
||||
Keeps Tensor inputs "as-is".
|
||||
Note: Use with extra care, shared data can be modified during runtime!
|
||||
Note: Using `shared_memory` may result in the extra memory overhead.
|
||||
|
||||
Default value: False
|
||||
:type shared_memory: bool, optional
|
||||
:return: Dictionary of results from output tensors with port/int/str keys.
|
||||
:rtype: OVDict
|
||||
"""
|
||||
return OVDict(super().infer(_data_dispatch(
|
||||
self,
|
||||
inputs,
|
||||
is_shared=shared_memory,
|
||||
)))
|
||||
|
||||
def start_async(
|
||||
self,
|
||||
inputs: Any = None,
|
||||
userdata: Any = None,
|
||||
shared_memory: bool = False,
|
||||
) -> None:
|
||||
"""Starts inference of specified input(s) in asynchronous mode.
|
||||
|
||||
Returns immediately. Inference starts also immediately.
|
||||
Calling any method on the `InferRequest` object while the request is running
|
||||
will lead to throwing exceptions.
|
||||
|
||||
The allowed types of keys in the `inputs` dictionary are:
|
||||
|
||||
(1) `int`
|
||||
(2) `str`
|
||||
(3) `openvino.runtime.ConstOutput`
|
||||
|
||||
The allowed types of values in the `inputs` are:
|
||||
|
||||
(1) `numpy.ndarray` and all the types that are castable to it, e.g. `torch.Tensor`
|
||||
(2) `openvino.runtime.Tensor`
|
||||
|
||||
Can be called with only one `openvino.runtime.Tensor` or `numpy.ndarray`,
|
||||
it will work only with one-input models. When model has more inputs,
|
||||
function throws error.
|
||||
|
||||
:param inputs: Data to be set on input tensors.
|
||||
:type inputs: Any, optional
|
||||
:param userdata: Any data that will be passed inside the callback.
|
||||
:type userdata: Any
|
||||
:param shared_memory: Enables `shared_memory` mode.
|
||||
|
||||
If set to `False` inputs the data dispatcher will safely copy data
|
||||
to existing Tensors (including up- or down-casting according to data type,
|
||||
resizing of the input Tensor). Keeps Tensor inputs "as-is".
|
||||
|
||||
If set to `True` the data dispatcher tries to provide "zero-copy"
|
||||
Tensors for every input in form of:
|
||||
* `numpy.ndarray` and all the types that are castable to it, e.g. `torch.Tensor`
|
||||
Data that is going to be copied:
|
||||
* `numpy.ndarray` which are not C contiguous
|
||||
* inputs which data types are mismatched from Infer Request's inputs
|
||||
* inputs that should be in `BF16` data type
|
||||
* scalar inputs (i.e. `np.float_`/`int`/`float`)
|
||||
Keeps Tensor inputs "as-is".
|
||||
Note: Use with extra care, shared data can be modified during runtime!
|
||||
Note: Using `shared_memory` may result in extra memory overhead.
|
||||
|
||||
Default value: False
|
||||
:type shared_memory: bool, optional
|
||||
"""
|
||||
super().start_async(
|
||||
_data_dispatch(
|
||||
self,
|
||||
inputs,
|
||||
is_shared=shared_memory,
|
||||
),
|
||||
userdata,
|
||||
)
|
||||
|
||||
@property
|
||||
def results(self) -> OVDict:
|
||||
"""Gets all outputs tensors of this InferRequest.
|
||||
|
||||
:return: Dictionary of results from output tensors with ports as keys.
|
||||
:rtype: Dict[openvino.runtime.ConstOutput, numpy.array]
|
||||
"""
|
||||
return OVDict(super().results)
|
||||
|
||||
|
||||
class CompiledModel(CompiledModelBase):
|
||||
"""CompiledModel class.
|
||||
|
||||
CompiledModel represents Model that is compiled for a specific device by applying
|
||||
multiple optimization transformations, then mapping to compute kernels.
|
||||
"""
|
||||
|
||||
def __init__(self, other: CompiledModelBase) -> None:
|
||||
# Private memeber to store already created InferRequest
|
||||
self._infer_request: Optional[InferRequest] = None
|
||||
super().__init__(other)
|
||||
|
||||
def create_infer_request(self) -> InferRequest:
|
||||
"""Creates an inference request object used to infer the compiled model.
|
||||
|
||||
The created request has allocated input and output tensors.
|
||||
|
||||
:return: New InferRequest object.
|
||||
:rtype: openvino.runtime.InferRequest
|
||||
"""
|
||||
return InferRequest(super().create_infer_request())
|
||||
|
||||
def infer_new_request(self, inputs: Union[dict, list, tuple, Tensor, np.ndarray] = None) -> OVDict:
|
||||
"""Infers specified input(s) in synchronous mode.
|
||||
|
||||
Blocks all methods of CompiledModel while request is running.
|
||||
|
||||
Method creates new temporary InferRequest and run inference on it.
|
||||
It is advised to use a dedicated InferRequest class for performance,
|
||||
optimizing workflows, and creating advanced pipelines.
|
||||
|
||||
The allowed types of keys in the `inputs` dictionary are:
|
||||
|
||||
(1) `int`
|
||||
(2) `str`
|
||||
(3) `openvino.runtime.ConstOutput`
|
||||
|
||||
The allowed types of values in the `inputs` are:
|
||||
|
||||
(1) `numpy.ndarray` and all the types that are castable to it, e.g. `torch.Tensor`
|
||||
(2) `openvino.runtime.Tensor`
|
||||
|
||||
Can be called with only one `openvino.runtime.Tensor` or `numpy.ndarray`,
|
||||
it will work only with one-input models. When model has more inputs,
|
||||
function throws error.
|
||||
|
||||
:param inputs: Data to be set on input tensors.
|
||||
:type inputs: Union[Dict[keys, values], List[values], Tuple[values], Tensor, numpy.ndarray], optional
|
||||
:return: Dictionary of results from output tensors with port/int/str keys.
|
||||
:rtype: OVDict
|
||||
"""
|
||||
# It returns wrapped python InferReqeust and then call upon
|
||||
# overloaded functions of InferRequest class
|
||||
return self.create_infer_request().infer(inputs)
|
||||
|
||||
def __call__(self,
|
||||
inputs: Union[dict, list, tuple, Tensor, np.ndarray] = None,
|
||||
shared_memory: bool = True) -> OVDict:
|
||||
"""Callable infer wrapper for CompiledModel.
|
||||
|
||||
Infers specified input(s) in synchronous mode.
|
||||
|
||||
Blocks all methods of CompiledModel while request is running.
|
||||
|
||||
Method creates new temporary InferRequest and run inference on it.
|
||||
It is advised to use a dedicated InferRequest class for performance,
|
||||
optimizing workflows, and creating advanced pipelines.
|
||||
|
||||
This method stores created `InferRequest` inside `CompiledModel` object,
|
||||
which can be later reused in consecutive calls.
|
||||
|
||||
The allowed types of keys in the `inputs` dictionary are:
|
||||
|
||||
(1) `int`
|
||||
(2) `str`
|
||||
(3) `openvino.runtime.ConstOutput`
|
||||
|
||||
The allowed types of values in the `inputs` are:
|
||||
|
||||
(1) `numpy.ndarray` and all the types that are castable to it, e.g. `torch.Tensor`
|
||||
(2) `openvino.runtime.Tensor`
|
||||
|
||||
Can be called with only one `openvino.runtime.Tensor` or `numpy.ndarray`,
|
||||
it will work only with one-input models. When model has more inputs,
|
||||
function throws error.
|
||||
|
||||
:param inputs: Data to be set on input tensors.
|
||||
:type inputs: Union[Dict[keys, values], List[values], Tuple[values], Tensor, numpy.ndarray], optional
|
||||
:param shared_memory: Enables `shared_memory` mode.
|
||||
|
||||
If set to `False` inputs the data dispatcher will safely copy data
|
||||
to existing Tensors (including up- or down-casting according to data type,
|
||||
resizing of the input Tensor). Keeps Tensor inputs "as-is".
|
||||
|
||||
If set to `True` the data dispatcher tries to provide "zero-copy"
|
||||
Tensors for every input in form of:
|
||||
* `numpy.ndarray` and all the types that are castable to it, e.g. `torch.Tensor`
|
||||
Data that is going to be copied:
|
||||
* `numpy.ndarray` which are not C contiguous
|
||||
* inputs which data types are mismatched from Infer Request's inputs
|
||||
* inputs that should be in `BF16` data type
|
||||
* scalar inputs (i.e. `np.float_`/`int`/`float`)
|
||||
Keeps Tensor inputs "as-is".
|
||||
Note: Use with extra care, shared data can be modified during runtime!
|
||||
Note: Using `shared_memory` may result in extra memory overhead.
|
||||
|
||||
Default value: True
|
||||
:type shared_memory: bool, optional
|
||||
|
||||
:return: Dictionary of results from output tensors with port/int/str as keys.
|
||||
:rtype: OVDict
|
||||
"""
|
||||
if self._infer_request is None:
|
||||
self._infer_request = self.create_infer_request()
|
||||
|
||||
return self._infer_request.infer(
|
||||
inputs,
|
||||
shared_memory=shared_memory,
|
||||
)
|
||||
|
||||
|
||||
class AsyncInferQueue(AsyncInferQueueBase):
|
||||
"""AsyncInferQueue with a pool of asynchronous requests.
|
||||
|
||||
AsyncInferQueue represents a helper that creates a pool of asynchronous
|
||||
InferRequests and provides synchronization functions to control flow of
|
||||
a simple pipeline.
|
||||
"""
|
||||
|
||||
def __iter__(self) -> Iterable[InferRequest]:
|
||||
"""Allows to iterate over AsyncInferQueue.
|
||||
|
||||
:return: a generator that yields InferRequests.
|
||||
:rtype: Iterable[openvino.runtime.InferRequest]
|
||||
"""
|
||||
return (InferRequest(x) for x in super().__iter__())
|
||||
|
||||
def __getitem__(self, i: int) -> InferRequest:
|
||||
"""Gets InferRequest from the pool with given i id.
|
||||
|
||||
:param i: InferRequest id.
|
||||
:type i: int
|
||||
:return: InferRequests from the pool with given id.
|
||||
:rtype: openvino.runtime.InferRequest
|
||||
"""
|
||||
return InferRequest(super().__getitem__(i))
|
||||
|
||||
def start_async(
|
||||
self,
|
||||
inputs: Any = None,
|
||||
userdata: Any = None,
|
||||
shared_memory: bool = False,
|
||||
) -> None:
|
||||
"""Run asynchronous inference using the next available InferRequest from the pool.
|
||||
|
||||
The allowed types of keys in the `inputs` dictionary are:
|
||||
|
||||
(1) `int`
|
||||
(2) `str`
|
||||
(3) `openvino.runtime.ConstOutput`
|
||||
|
||||
The allowed types of values in the `inputs` are:
|
||||
|
||||
(1) `numpy.ndarray` and all the types that are castable to it, e.g. `torch.Tensor`
|
||||
(2) `openvino.runtime.Tensor`
|
||||
|
||||
Can be called with only one `openvino.runtime.Tensor` or `numpy.ndarray`,
|
||||
it will work only with one-input models. When model has more inputs,
|
||||
function throws error.
|
||||
|
||||
:param inputs: Data to be set on input tensors of the next available InferRequest.
|
||||
:type inputs: Any, optional
|
||||
:param userdata: Any data that will be passed to a callback.
|
||||
:type userdata: Any, optional
|
||||
:param shared_memory: Enables `shared_memory` mode.
|
||||
|
||||
If set to `False` inputs the data dispatcher will safely copy data
|
||||
to existing Tensors (including up- or down-casting according to data type,
|
||||
resizing of the input Tensor). Keeps Tensor inputs "as-is".
|
||||
|
||||
If set to `True` the data dispatcher tries to provide "zero-copy"
|
||||
Tensors for every input in form of:
|
||||
* `numpy.ndarray` and all the types that are castable to it, e.g. `torch.Tensor`
|
||||
Data that is going to be copied:
|
||||
* `numpy.ndarray` which are not C contiguous
|
||||
* inputs which data types are mismatched from Infer Request's inputs
|
||||
* inputs that should be in `BF16` data type
|
||||
* scalar inputs (i.e. `np.float_`/`int`/`float`)
|
||||
Keeps Tensor inputs "as-is".
|
||||
Note: Use with extra care, shared data can be modified during runtime!
|
||||
Note: Using `shared_memory` may result in extra memory overhead.
|
||||
|
||||
Default value: False
|
||||
"""
|
||||
super().start_async(
|
||||
_data_dispatch(
|
||||
self[self.get_idle_request_id()],
|
||||
inputs,
|
||||
is_shared=shared_memory,
|
||||
),
|
||||
userdata,
|
||||
)
|
||||
|
||||
|
||||
class Core(CoreBase):
|
||||
"""Core class represents OpenVINO runtime Core entity.
|
||||
|
||||
User applications can create several Core class instances, but in this
|
||||
case, the underlying plugins are created multiple times and not shared
|
||||
between several Core instances. The recommended way is to have a single
|
||||
Core instance per application.
|
||||
"""
|
||||
|
||||
def compile_model(
|
||||
self,
|
||||
model: Union[Model, str, Path],
|
||||
device_name: Optional[str] = None,
|
||||
config: Optional[dict] = None,
|
||||
) -> CompiledModel:
|
||||
"""Creates a compiled model.
|
||||
|
||||
Creates a compiled model from a source Model object or
|
||||
reads model and creates a compiled model from IR / ONNX / PDPD / TF and TFLite file.
|
||||
This can be more efficient than using read_model + compile_model(model_in_memory_object) flow,
|
||||
especially for cases when caching is enabled and cached model is available.
|
||||
If device_name is not specified, the default OpenVINO device will be selected by AUTO plugin.
|
||||
Users can create as many compiled models as they need, and use them simultaneously
|
||||
(up to the limitation of the hardware resources).
|
||||
|
||||
:param model: Model acquired from read_model function or a path to a model in IR / ONNX / PDPD /
|
||||
TF and TFLite format.
|
||||
:type model: Union[openvino.runtime.Model, str, pathlib.Path]
|
||||
:param device_name: Optional. Name of the device to load the model to. If not specified,
|
||||
the default OpenVINO device will be selected by AUTO plugin.
|
||||
:type device_name: str
|
||||
:param config: Optional dict of pairs:
|
||||
(property name, property value) relevant only for this load operation.
|
||||
:type config: dict, optional
|
||||
:return: A compiled model.
|
||||
:rtype: openvino.runtime.CompiledModel
|
||||
"""
|
||||
if device_name is None:
|
||||
return CompiledModel(
|
||||
super().compile_model(model, {} if config is None else config),
|
||||
)
|
||||
|
||||
return CompiledModel(
|
||||
super().compile_model(model, device_name, {} if config is None else config),
|
||||
)
|
||||
|
||||
def import_model(
|
||||
self,
|
||||
model_stream: bytes,
|
||||
device_name: str,
|
||||
config: Optional[dict] = None,
|
||||
) -> CompiledModel:
|
||||
"""Imports a compiled model from a previously exported one.
|
||||
|
||||
:param model_stream: Input stream, containing a model previously exported, using export_model method.
|
||||
:type model_stream: bytes
|
||||
:param device_name: Name of device to which compiled model is imported.
|
||||
Note: if device_name is not used to compile the original model,
|
||||
an exception is thrown.
|
||||
:type device_name: str
|
||||
:param config: Optional dict of pairs:
|
||||
(property name, property value) relevant only for this load operation.
|
||||
:type config: dict, optional
|
||||
:return: A compiled model.
|
||||
:rtype: openvino.runtime.CompiledModel
|
||||
|
||||
:Example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
user_stream = compiled.export_model()
|
||||
|
||||
with open('./my_model', 'wb') as f:
|
||||
f.write(user_stream)
|
||||
|
||||
# ...
|
||||
|
||||
new_compiled = core.import_model(user_stream, "CPU")
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
user_stream = io.BytesIO()
|
||||
compiled.export_model(user_stream)
|
||||
|
||||
with open('./my_model', 'wb') as f:
|
||||
f.write(user_stream.getvalue()) # or read() if seek(0) was applied before
|
||||
|
||||
# ...
|
||||
|
||||
new_compiled = core.import_model(user_stream, "CPU")
|
||||
"""
|
||||
return CompiledModel(
|
||||
super().import_model(
|
||||
model_stream,
|
||||
device_name,
|
||||
{} if config is None else config,
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def compile_model(model_path: Union[str, Path]) -> CompiledModel:
|
||||
"""Compact method to compile model with AUTO plugin.
|
||||
|
||||
:param model_path: Path to file with model.
|
||||
:type model_path: str, pathlib.Path
|
||||
:return: A compiled model
|
||||
:rtype: openvino.runtime.CompiledModel
|
||||
|
||||
"""
|
||||
core = Core()
|
||||
return core.compile_model(model_path, "AUTO")
|
||||
@ -0,0 +1,26 @@
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""
|
||||
Package: openvino.op
|
||||
Low level wrappers for the c++ api in ov::op.
|
||||
"""
|
||||
|
||||
# flake8: noqa
|
||||
|
||||
import numpy as np
|
||||
|
||||
from openvino._pyopenvino.op import Constant
|
||||
|
||||
"""Retrieve Constant inner data.
|
||||
|
||||
Internally uses PyBind11 Numpy's buffer protocol.
|
||||
|
||||
:return Numpy array containing internally stored constant data.
|
||||
"""
|
||||
Constant.get_data = lambda self: np.array(self, copy=True)
|
||||
|
||||
from openvino._pyopenvino.op import Parameter
|
||||
from openvino._pyopenvino.op import if_op
|
||||
from openvino._pyopenvino.op import loop
|
||||
from openvino._pyopenvino.op import tensor_iterator
|
||||
Binary file not shown.
@ -0,0 +1,22 @@
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""
|
||||
Package: openvino.op.util
|
||||
Low level wrappers for the c++ api in ov::op::util.
|
||||
"""
|
||||
# flake8: noqa
|
||||
|
||||
from openvino._pyopenvino.op.util import UnaryElementwiseArithmetic
|
||||
from openvino._pyopenvino.op.util import BinaryElementwiseComparison
|
||||
from openvino._pyopenvino.op.util import BinaryElementwiseArithmetic
|
||||
from openvino._pyopenvino.op.util import BinaryElementwiseLogical
|
||||
from openvino._pyopenvino.op.util import ArithmeticReduction
|
||||
from openvino._pyopenvino.op.util import IndexReduction
|
||||
from openvino._pyopenvino.op.util import VariableInfo
|
||||
from openvino._pyopenvino.op.util import Variable
|
||||
from openvino._pyopenvino.op.util import MergedInputDescription
|
||||
from openvino._pyopenvino.op.util import InvariantInputDescription
|
||||
from openvino._pyopenvino.op.util import SliceInputDescription
|
||||
from openvino._pyopenvino.op.util import ConcatOutputDescription
|
||||
from openvino._pyopenvino.op.util import BodyOutputDescription
|
||||
Binary file not shown.
@ -0,0 +1,112 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from openvino.runtime.opset1.ops import absolute
|
||||
from openvino.runtime.opset1.ops import absolute as abs
|
||||
from openvino.runtime.opset1.ops import acos
|
||||
from openvino.runtime.opset1.ops import add
|
||||
from openvino.runtime.opset1.ops import asin
|
||||
from openvino.runtime.opset1.ops import atan
|
||||
from openvino.runtime.opset1.ops import avg_pool
|
||||
from openvino.runtime.opset1.ops import batch_norm_inference
|
||||
from openvino.runtime.opset1.ops import binary_convolution
|
||||
from openvino.runtime.opset1.ops import broadcast
|
||||
from openvino.runtime.opset1.ops import ceiling
|
||||
from openvino.runtime.opset1.ops import ceiling as ceil
|
||||
from openvino.runtime.opset1.ops import clamp
|
||||
from openvino.runtime.opset1.ops import concat
|
||||
from openvino.runtime.opset1.ops import constant
|
||||
from openvino.runtime.opset1.ops import convert
|
||||
from openvino.runtime.opset1.ops import convert_like
|
||||
from openvino.runtime.opset1.ops import convolution
|
||||
from openvino.runtime.opset1.ops import convolution_backprop_data
|
||||
from openvino.runtime.opset1.ops import cos
|
||||
from openvino.runtime.opset1.ops import cosh
|
||||
from openvino.runtime.opset1.ops import ctc_greedy_decoder
|
||||
from openvino.runtime.opset1.ops import deformable_convolution
|
||||
from openvino.runtime.opset1.ops import deformable_psroi_pooling
|
||||
from openvino.runtime.opset1.ops import depth_to_space
|
||||
from openvino.runtime.opset1.ops import detection_output
|
||||
from openvino.runtime.opset1.ops import divide
|
||||
from openvino.runtime.opset1.ops import elu
|
||||
from openvino.runtime.opset1.ops import equal
|
||||
from openvino.runtime.opset1.ops import erf
|
||||
from openvino.runtime.opset1.ops import exp
|
||||
from openvino.runtime.opset1.ops import fake_quantize
|
||||
from openvino.runtime.opset1.ops import floor
|
||||
from openvino.runtime.opset1.ops import floor_mod
|
||||
from openvino.runtime.opset1.ops import gather
|
||||
from openvino.runtime.opset1.ops import gather_tree
|
||||
from openvino.runtime.opset1.ops import greater
|
||||
from openvino.runtime.opset1.ops import greater_equal
|
||||
from openvino.runtime.opset1.ops import grn
|
||||
from openvino.runtime.opset1.ops import group_convolution
|
||||
from openvino.runtime.opset1.ops import group_convolution_backprop_data
|
||||
from openvino.runtime.opset1.ops import hard_sigmoid
|
||||
from openvino.runtime.opset1.ops import interpolate
|
||||
from openvino.runtime.opset1.ops import less
|
||||
from openvino.runtime.opset1.ops import less_equal
|
||||
from openvino.runtime.opset1.ops import log
|
||||
from openvino.runtime.opset1.ops import logical_and
|
||||
from openvino.runtime.opset1.ops import logical_not
|
||||
from openvino.runtime.opset1.ops import logical_or
|
||||
from openvino.runtime.opset1.ops import logical_xor
|
||||
from openvino.runtime.opset1.ops import lrn
|
||||
from openvino.runtime.opset1.ops import lstm_cell
|
||||
from openvino.runtime.opset1.ops import lstm_sequence
|
||||
from openvino.runtime.opset1.ops import matmul
|
||||
from openvino.runtime.opset1.ops import max_pool
|
||||
from openvino.runtime.opset1.ops import maximum
|
||||
from openvino.runtime.opset1.ops import minimum
|
||||
from openvino.runtime.opset1.ops import mod
|
||||
from openvino.runtime.opset1.ops import multiply
|
||||
from openvino.runtime.opset1.ops import negative
|
||||
from openvino.runtime.opset1.ops import non_max_suppression
|
||||
from openvino.runtime.opset1.ops import normalize_l2
|
||||
from openvino.runtime.opset1.ops import not_equal
|
||||
from openvino.runtime.opset1.ops import one_hot
|
||||
from openvino.runtime.opset1.ops import pad
|
||||
from openvino.runtime.opset1.ops import parameter
|
||||
from openvino.runtime.opset1.ops import power
|
||||
from openvino.runtime.opset1.ops import prelu
|
||||
from openvino.runtime.opset1.ops import prior_box
|
||||
from openvino.runtime.opset1.ops import prior_box_clustered
|
||||
from openvino.runtime.opset1.ops import psroi_pooling
|
||||
from openvino.runtime.opset1.ops import proposal
|
||||
from openvino.runtime.opset1.ops import range
|
||||
from openvino.runtime.opset1.ops import reduce_logical_and
|
||||
from openvino.runtime.opset1.ops import reduce_logical_or
|
||||
from openvino.runtime.opset1.ops import reduce_max
|
||||
from openvino.runtime.opset1.ops import reduce_mean
|
||||
from openvino.runtime.opset1.ops import reduce_min
|
||||
from openvino.runtime.opset1.ops import reduce_prod
|
||||
from openvino.runtime.opset1.ops import reduce_sum
|
||||
from openvino.runtime.opset1.ops import region_yolo
|
||||
from openvino.runtime.opset1.ops import relu
|
||||
from openvino.runtime.opset1.ops import reshape
|
||||
from openvino.runtime.opset1.ops import result
|
||||
from openvino.runtime.opset1.ops import reverse_sequence
|
||||
from openvino.runtime.opset1.ops import select
|
||||
from openvino.runtime.opset1.ops import selu
|
||||
from openvino.runtime.opset1.ops import shape_of
|
||||
from openvino.runtime.opset1.ops import sigmoid
|
||||
from openvino.runtime.opset1.ops import sign
|
||||
from openvino.runtime.opset1.ops import sin
|
||||
from openvino.runtime.opset1.ops import sinh
|
||||
from openvino.runtime.opset1.ops import softmax
|
||||
from openvino.runtime.opset1.ops import space_to_depth
|
||||
from openvino.runtime.opset1.ops import split
|
||||
from openvino.runtime.opset1.ops import sqrt
|
||||
from openvino.runtime.opset1.ops import squared_difference
|
||||
from openvino.runtime.opset1.ops import squeeze
|
||||
from openvino.runtime.opset1.ops import strided_slice
|
||||
from openvino.runtime.opset1.ops import subtract
|
||||
from openvino.runtime.opset1.ops import tan
|
||||
from openvino.runtime.opset1.ops import tanh
|
||||
from openvino.runtime.opset1.ops import tensor_iterator
|
||||
from openvino.runtime.opset1.ops import tile
|
||||
from openvino.runtime.opset1.ops import topk
|
||||
from openvino.runtime.opset1.ops import transpose
|
||||
from openvino.runtime.opset1.ops import unsqueeze
|
||||
from openvino.runtime.opset1.ops import variadic_split
|
||||
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@ -0,0 +1,178 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from openvino.runtime.opset1.ops import absolute
|
||||
from openvino.runtime.opset1.ops import absolute as abs
|
||||
from openvino.runtime.opset1.ops import acos
|
||||
from openvino.runtime.opset4.ops import acosh
|
||||
from openvino.runtime.opset8.ops import adaptive_avg_pool
|
||||
from openvino.runtime.opset8.ops import adaptive_max_pool
|
||||
from openvino.runtime.opset1.ops import add
|
||||
from openvino.runtime.opset1.ops import asin
|
||||
from openvino.runtime.opset4.ops import asinh
|
||||
from openvino.runtime.opset3.ops import assign
|
||||
from openvino.runtime.opset1.ops import atan
|
||||
from openvino.runtime.opset4.ops import atanh
|
||||
from openvino.runtime.opset1.ops import avg_pool
|
||||
from openvino.runtime.opset5.ops import batch_norm_inference
|
||||
from openvino.runtime.opset2.ops import batch_to_space
|
||||
from openvino.runtime.opset1.ops import binary_convolution
|
||||
from openvino.runtime.opset3.ops import broadcast
|
||||
from openvino.runtime.opset3.ops import bucketize
|
||||
from openvino.runtime.opset1.ops import ceiling
|
||||
from openvino.runtime.opset1.ops import ceiling as ceil
|
||||
from openvino.runtime.opset1.ops import clamp
|
||||
from openvino.runtime.opset1.ops import concat
|
||||
from openvino.runtime.opset1.ops import constant
|
||||
from openvino.runtime.opset1.ops import convert
|
||||
from openvino.runtime.opset1.ops import convert_like
|
||||
from openvino.runtime.opset1.ops import convolution
|
||||
from openvino.runtime.opset1.ops import convolution_backprop_data
|
||||
from openvino.runtime.opset1.ops import cos
|
||||
from openvino.runtime.opset1.ops import cosh
|
||||
from openvino.runtime.opset1.ops import ctc_greedy_decoder
|
||||
from openvino.runtime.opset6.ops import ctc_greedy_decoder_seq_len
|
||||
from openvino.runtime.opset4.ops import ctc_loss
|
||||
from openvino.runtime.opset3.ops import cum_sum
|
||||
from openvino.runtime.opset3.ops import cum_sum as cumsum
|
||||
from openvino.runtime.opset8.ops import deformable_convolution
|
||||
from openvino.runtime.opset1.ops import deformable_psroi_pooling
|
||||
from openvino.runtime.opset1.ops import depth_to_space
|
||||
from openvino.runtime.opset8.ops import detection_output
|
||||
from openvino.runtime.opset7.ops import dft
|
||||
from openvino.runtime.opset1.ops import divide
|
||||
from openvino.runtime.opset7.ops import einsum
|
||||
from openvino.runtime.opset1.ops import elu
|
||||
from openvino.runtime.opset3.ops import embedding_bag_offsets_sum
|
||||
from openvino.runtime.opset3.ops import embedding_bag_packed_sum
|
||||
from openvino.runtime.opset3.ops import embedding_segments_sum
|
||||
from openvino.runtime.opset3.ops import extract_image_patches
|
||||
from openvino.runtime.opset1.ops import equal
|
||||
from openvino.runtime.opset1.ops import erf
|
||||
from openvino.runtime.opset1.ops import exp
|
||||
from openvino.runtime.opset9.ops import eye
|
||||
from openvino.runtime.opset1.ops import fake_quantize
|
||||
from openvino.runtime.opset1.ops import floor
|
||||
from openvino.runtime.opset1.ops import floor_mod
|
||||
from openvino.runtime.opset8.ops import gather
|
||||
from openvino.runtime.opset6.ops import gather_elements
|
||||
from openvino.runtime.opset8.ops import gather_nd
|
||||
from openvino.runtime.opset1.ops import gather_tree
|
||||
from openvino.runtime.opset7.ops import gelu
|
||||
from openvino.runtime.opset9.ops import generate_proposals
|
||||
from openvino.runtime.opset1.ops import greater
|
||||
from openvino.runtime.opset1.ops import greater_equal
|
||||
from openvino.runtime.opset9.ops import grid_sample
|
||||
from openvino.runtime.opset1.ops import grn
|
||||
from openvino.runtime.opset1.ops import group_convolution
|
||||
from openvino.runtime.opset1.ops import group_convolution_backprop_data
|
||||
from openvino.runtime.opset3.ops import gru_cell
|
||||
from openvino.runtime.opset5.ops import gru_sequence
|
||||
from openvino.runtime.opset1.ops import hard_sigmoid
|
||||
from openvino.runtime.opset5.ops import hsigmoid
|
||||
from openvino.runtime.opset4.ops import hswish
|
||||
from openvino.runtime.opset7.ops import idft
|
||||
from openvino.runtime.opset8.ops import if_op
|
||||
from openvino.runtime.opset10.ops import interpolate
|
||||
from openvino.runtime.opset9.ops import irdft
|
||||
from openvino.runtime.opset10.ops import is_finite
|
||||
from openvino.runtime.opset10.ops import is_inf
|
||||
from openvino.runtime.opset10.ops import is_nan
|
||||
from openvino.runtime.opset8.ops import i420_to_bgr
|
||||
from openvino.runtime.opset8.ops import i420_to_rgb
|
||||
from openvino.runtime.opset1.ops import less
|
||||
from openvino.runtime.opset1.ops import less_equal
|
||||
from openvino.runtime.opset1.ops import log
|
||||
from openvino.runtime.opset1.ops import logical_and
|
||||
from openvino.runtime.opset1.ops import logical_not
|
||||
from openvino.runtime.opset1.ops import logical_or
|
||||
from openvino.runtime.opset1.ops import logical_xor
|
||||
from openvino.runtime.opset5.ops import log_softmax
|
||||
from openvino.runtime.opset5.ops import loop
|
||||
from openvino.runtime.opset1.ops import lrn
|
||||
from openvino.runtime.opset4.ops import lstm_cell
|
||||
from openvino.runtime.opset5.ops import lstm_sequence
|
||||
from openvino.runtime.opset1.ops import matmul
|
||||
from openvino.runtime.opset8.ops import matrix_nms
|
||||
from openvino.runtime.opset8.ops import max_pool
|
||||
from openvino.runtime.opset1.ops import maximum
|
||||
from openvino.runtime.opset1.ops import minimum
|
||||
from openvino.runtime.opset4.ops import mish
|
||||
from openvino.runtime.opset1.ops import mod
|
||||
from openvino.runtime.opset9.ops import multiclass_nms
|
||||
from openvino.runtime.opset1.ops import multiply
|
||||
from openvino.runtime.opset6.ops import mvn
|
||||
from openvino.runtime.opset1.ops import negative
|
||||
from openvino.runtime.opset9.ops import non_max_suppression
|
||||
from openvino.runtime.opset3.ops import non_zero
|
||||
from openvino.runtime.opset1.ops import normalize_l2
|
||||
from openvino.runtime.opset1.ops import not_equal
|
||||
from openvino.runtime.opset8.ops import nv12_to_bgr
|
||||
from openvino.runtime.opset8.ops import nv12_to_rgb
|
||||
from openvino.runtime.opset1.ops import one_hot
|
||||
from openvino.runtime.opset1.ops import pad
|
||||
from openvino.runtime.opset1.ops import parameter
|
||||
from openvino.runtime.opset1.ops import power
|
||||
from openvino.runtime.opset1.ops import prelu
|
||||
from openvino.runtime.opset8.ops import prior_box
|
||||
from openvino.runtime.opset1.ops import prior_box_clustered
|
||||
from openvino.runtime.opset1.ops import psroi_pooling
|
||||
from openvino.runtime.opset4.ops import proposal
|
||||
from openvino.runtime.opset1.ops import range
|
||||
from openvino.runtime.opset8.ops import random_uniform
|
||||
from openvino.runtime.opset9.ops import rdft
|
||||
from openvino.runtime.opset3.ops import read_value
|
||||
from openvino.runtime.opset4.ops import reduce_l1
|
||||
from openvino.runtime.opset4.ops import reduce_l2
|
||||
from openvino.runtime.opset1.ops import reduce_logical_and
|
||||
from openvino.runtime.opset1.ops import reduce_logical_or
|
||||
from openvino.runtime.opset1.ops import reduce_max
|
||||
from openvino.runtime.opset1.ops import reduce_mean
|
||||
from openvino.runtime.opset1.ops import reduce_min
|
||||
from openvino.runtime.opset1.ops import reduce_prod
|
||||
from openvino.runtime.opset1.ops import reduce_sum
|
||||
from openvino.runtime.opset1.ops import region_yolo
|
||||
from openvino.runtime.opset2.ops import reorg_yolo
|
||||
from openvino.runtime.opset1.ops import relu
|
||||
from openvino.runtime.opset1.ops import reshape
|
||||
from openvino.runtime.opset1.ops import result
|
||||
from openvino.runtime.opset1.ops import reverse_sequence
|
||||
from openvino.runtime.opset3.ops import rnn_cell
|
||||
from openvino.runtime.opset5.ops import rnn_sequence
|
||||
from openvino.runtime.opset9.ops import roi_align
|
||||
from openvino.runtime.opset2.ops import roi_pooling
|
||||
from openvino.runtime.opset7.ops import roll
|
||||
from openvino.runtime.opset5.ops import round
|
||||
from openvino.runtime.opset3.ops import scatter_elements_update
|
||||
from openvino.runtime.opset3.ops import scatter_update
|
||||
from openvino.runtime.opset1.ops import select
|
||||
from openvino.runtime.opset1.ops import selu
|
||||
from openvino.runtime.opset3.ops import shape_of
|
||||
from openvino.runtime.opset3.ops import shuffle_channels
|
||||
from openvino.runtime.opset1.ops import sigmoid
|
||||
from openvino.runtime.opset1.ops import sign
|
||||
from openvino.runtime.opset1.ops import sin
|
||||
from openvino.runtime.opset1.ops import sinh
|
||||
from openvino.runtime.opset8.ops import slice
|
||||
from openvino.runtime.opset8.ops import softmax
|
||||
from openvino.runtime.opset4.ops import softplus
|
||||
from openvino.runtime.opset9.ops import softsign
|
||||
from openvino.runtime.opset2.ops import space_to_batch
|
||||
from openvino.runtime.opset1.ops import space_to_depth
|
||||
from openvino.runtime.opset1.ops import split
|
||||
from openvino.runtime.opset1.ops import sqrt
|
||||
from openvino.runtime.opset1.ops import squared_difference
|
||||
from openvino.runtime.opset1.ops import squeeze
|
||||
from openvino.runtime.opset1.ops import strided_slice
|
||||
from openvino.runtime.opset1.ops import subtract
|
||||
from openvino.runtime.opset4.ops import swish
|
||||
from openvino.runtime.opset1.ops import tan
|
||||
from openvino.runtime.opset1.ops import tanh
|
||||
from openvino.runtime.opset1.ops import tensor_iterator
|
||||
from openvino.runtime.opset1.ops import tile
|
||||
from openvino.runtime.opset3.ops import topk
|
||||
from openvino.runtime.opset1.ops import transpose
|
||||
from openvino.runtime.opset10.ops import unique
|
||||
from openvino.runtime.opset1.ops import unsqueeze
|
||||
from openvino.runtime.opset1.ops import variadic_split
|
||||
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Binary file not shown.
@ -0,0 +1,173 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""Factory functions for all openvino ops."""
|
||||
from functools import partial
|
||||
from typing import List, Optional
|
||||
|
||||
from openvino.runtime import Node
|
||||
from openvino.runtime.opset_utils import _get_node_factory
|
||||
from openvino.runtime.utils.decorators import nameable_op
|
||||
from openvino.runtime.utils.types import (
|
||||
NodeInput,
|
||||
as_nodes,
|
||||
as_node,
|
||||
make_constant_node,
|
||||
)
|
||||
|
||||
_get_node_factory_opset4 = partial(_get_node_factory, "opset4")
|
||||
_get_node_factory_opset10 = partial(_get_node_factory, "opset10")
|
||||
|
||||
# -------------------------------------------- ops ------------------------------------------------
|
||||
|
||||
|
||||
@nameable_op
|
||||
def interpolate(
|
||||
image: NodeInput,
|
||||
output_shape: NodeInput,
|
||||
scales: NodeInput,
|
||||
mode: str,
|
||||
shape_calculation_mode: str,
|
||||
pads_begin: Optional[List[int]] = None,
|
||||
pads_end: Optional[List[int]] = None,
|
||||
coordinate_transformation_mode: str = "half_pixel",
|
||||
nearest_mode: str = "round_prefer_floor",
|
||||
antialias: bool = False,
|
||||
cube_coeff: float = -0.75,
|
||||
axes: Optional[NodeInput] = None,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Perform interpolation of independent slices in input tensor.
|
||||
|
||||
:param image: The node providing input tensor with data for interpolation.
|
||||
:param output_shape: 1D tensor describing output shape for spatial axes.
|
||||
:param scales: 1D tensor describing scales for spatial axes.
|
||||
:param mode: Specifies type of interpolation. Possible values are: nearest, linear,
|
||||
linear_onnx, cubic.
|
||||
:param shape_calculation_mode:
|
||||
Specifies which input, sizes or scales, is used to calculate an output
|
||||
shape.
|
||||
:param pads_begin: Specifies the number of pixels to add to the beginning of the image
|
||||
being interpolated. Default is None.
|
||||
:param pads_end: Specifies the number of pixels to add to the end of the image being
|
||||
interpolated. Default is None.
|
||||
:param coordinate_transformation_mode:
|
||||
Specifies how to transform the coordinate in the resized tensor to the
|
||||
coordinate in the original tensor. Default is "half_pixel".
|
||||
:param nearest_mode: Specifies round mode when mode == nearest and is used only when
|
||||
mode == nearest. Default is "round_prefer_floor".
|
||||
:param antialias: Specifies whether to perform anti-aliasing. Default is False.
|
||||
:param cube_coeff: Specifies the parameter a for cubic interpolation. Default is -0.75.
|
||||
:param axes: 1D tensor specifying dimension indices where interpolation is applied.
|
||||
Default is None.
|
||||
:param name: Optional name for the output node. Default is None.
|
||||
:return: Node representing interpolation operation.
|
||||
"""
|
||||
attrs = {
|
||||
"mode": mode,
|
||||
"shape_calculation_mode": shape_calculation_mode,
|
||||
"coordinate_transformation_mode": coordinate_transformation_mode,
|
||||
"nearest_mode": nearest_mode,
|
||||
"antialias": antialias,
|
||||
"cube_coeff": cube_coeff,
|
||||
}
|
||||
|
||||
attrs["pads_begin"] = [] if pads_begin is None else pads_begin
|
||||
attrs["pads_end"] = [] if pads_end is None else pads_end
|
||||
|
||||
inputs = as_nodes(image, output_shape, scales) if axes is None else as_nodes(image, output_shape, scales, axes)
|
||||
|
||||
# This is an update of the operator version, so even though this is opset 10,
|
||||
# the operator is taken from opset 4.
|
||||
return _get_node_factory_opset4().create("Interpolate", inputs, attrs)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def is_finite(data: NodeInput, name: Optional[str] = None) -> Node:
|
||||
"""Performs element-wise mapping from NaN and Infinity to False. Other values are mapped to True.
|
||||
|
||||
:param data: A tensor of floating-point numeric type and arbitrary shape.
|
||||
:param name: Optional name for the output node. The default is None.
|
||||
:return: Node representing is_finite operation.
|
||||
"""
|
||||
return _get_node_factory_opset10().create("IsFinite", as_nodes(data))
|
||||
|
||||
|
||||
@nameable_op
|
||||
def is_inf(
|
||||
data: NodeInput,
|
||||
attributes: Optional[dict] = None,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return a node which performs IsInf operation.
|
||||
|
||||
:param data: The input tensor.
|
||||
:param attributes: Optional dictionary containing IsInf attributes.
|
||||
:param name: Optional name of the node.
|
||||
|
||||
Available attributes:
|
||||
|
||||
* detect_negative Specifies whether to map negative infinities to true in output map.
|
||||
Range of values: true, false
|
||||
Default value: true
|
||||
Required: no
|
||||
* detect_positive Specifies whether to map positive infinities to true in output map.
|
||||
Range of values: true, false
|
||||
Default value: true
|
||||
Required: no
|
||||
|
||||
:return: A new IsInf node.
|
||||
"""
|
||||
if not attributes:
|
||||
attributes = {}
|
||||
return _get_node_factory_opset10().create("IsInf", as_nodes(data), attributes)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def is_nan(data: NodeInput, name: Optional[str] = None) -> Node:
|
||||
"""Performs element-wise mapping from NaN to True. Other values are mapped to False.
|
||||
|
||||
:param data: A tensor of floating point numeric type and arbitrary shape.
|
||||
:param name: Optional name for the output node. Default is None.
|
||||
:return: Node representing is_nan operation.
|
||||
"""
|
||||
return _get_node_factory_opset10().create("IsNaN", as_nodes(data))
|
||||
|
||||
|
||||
@nameable_op
|
||||
def unique(
|
||||
data: NodeInput,
|
||||
axis: Optional[NodeInput] = None,
|
||||
sorted: Optional[bool] = True,
|
||||
index_element_type: Optional[str] = "i64",
|
||||
count_element_type: Optional[str] = "i64",
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Operator which selects and returns unique elements or unique slices of the input tensor.
|
||||
|
||||
:param data: Input data tensor.
|
||||
:param axis: (Optional) An input tensor containing the axis value.
|
||||
If not provided or None, data input is considered as a flattened tensor.
|
||||
Default value: None.
|
||||
:param sorted: (Optional) Controls the order of the returned unique values,
|
||||
sorts ascendingly when true.
|
||||
Default value: True.
|
||||
:param index_element_type: (Optional) The data type set for outputs containing indices.
|
||||
Default value: "i64".
|
||||
:param count_element_type: (Optional) The data type set for the output with repetition count.
|
||||
Default value: "i64".
|
||||
:param name: (Optional) A name for the output node. Default value: None.
|
||||
:return: Node representing Unique operation.
|
||||
"""
|
||||
if axis is None:
|
||||
inputs = as_nodes(data)
|
||||
else:
|
||||
inputs = as_nodes(data, axis)
|
||||
|
||||
attributes = {
|
||||
"sorted": sorted,
|
||||
"index_element_type": index_element_type,
|
||||
"count_element_type": count_element_type,
|
||||
}
|
||||
return _get_node_factory_opset10().create("Unique", inputs, attributes)
|
||||
@ -0,0 +1,178 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from openvino.runtime.opset1.ops import absolute
|
||||
from openvino.runtime.opset1.ops import absolute as abs
|
||||
from openvino.runtime.opset1.ops import acos
|
||||
from openvino.runtime.opset4.ops import acosh
|
||||
from openvino.runtime.opset8.ops import adaptive_avg_pool
|
||||
from openvino.runtime.opset8.ops import adaptive_max_pool
|
||||
from openvino.runtime.opset1.ops import add
|
||||
from openvino.runtime.opset1.ops import asin
|
||||
from openvino.runtime.opset4.ops import asinh
|
||||
from openvino.runtime.opset3.ops import assign
|
||||
from openvino.runtime.opset1.ops import atan
|
||||
from openvino.runtime.opset4.ops import atanh
|
||||
from openvino.runtime.opset1.ops import avg_pool
|
||||
from openvino.runtime.opset5.ops import batch_norm_inference
|
||||
from openvino.runtime.opset2.ops import batch_to_space
|
||||
from openvino.runtime.opset1.ops import binary_convolution
|
||||
from openvino.runtime.opset3.ops import broadcast
|
||||
from openvino.runtime.opset3.ops import bucketize
|
||||
from openvino.runtime.opset1.ops import ceiling
|
||||
from openvino.runtime.opset1.ops import ceiling as ceil
|
||||
from openvino.runtime.opset1.ops import clamp
|
||||
from openvino.runtime.opset1.ops import concat
|
||||
from openvino.runtime.opset1.ops import constant
|
||||
from openvino.runtime.opset1.ops import convert
|
||||
from openvino.runtime.opset1.ops import convert_like
|
||||
from openvino.runtime.opset1.ops import convolution
|
||||
from openvino.runtime.opset1.ops import convolution_backprop_data
|
||||
from openvino.runtime.opset1.ops import cos
|
||||
from openvino.runtime.opset1.ops import cosh
|
||||
from openvino.runtime.opset1.ops import ctc_greedy_decoder
|
||||
from openvino.runtime.opset6.ops import ctc_greedy_decoder_seq_len
|
||||
from openvino.runtime.opset4.ops import ctc_loss
|
||||
from openvino.runtime.opset3.ops import cum_sum
|
||||
from openvino.runtime.opset3.ops import cum_sum as cumsum
|
||||
from openvino.runtime.opset8.ops import deformable_convolution
|
||||
from openvino.runtime.opset1.ops import deformable_psroi_pooling
|
||||
from openvino.runtime.opset1.ops import depth_to_space
|
||||
from openvino.runtime.opset8.ops import detection_output
|
||||
from openvino.runtime.opset7.ops import dft
|
||||
from openvino.runtime.opset1.ops import divide
|
||||
from openvino.runtime.opset7.ops import einsum
|
||||
from openvino.runtime.opset1.ops import elu
|
||||
from openvino.runtime.opset3.ops import embedding_bag_offsets_sum
|
||||
from openvino.runtime.opset3.ops import embedding_bag_packed_sum
|
||||
from openvino.runtime.opset3.ops import embedding_segments_sum
|
||||
from openvino.runtime.opset3.ops import extract_image_patches
|
||||
from openvino.runtime.opset1.ops import equal
|
||||
from openvino.runtime.opset1.ops import erf
|
||||
from openvino.runtime.opset1.ops import exp
|
||||
from openvino.runtime.opset9.ops import eye
|
||||
from openvino.runtime.opset1.ops import fake_quantize
|
||||
from openvino.runtime.opset1.ops import floor
|
||||
from openvino.runtime.opset1.ops import floor_mod
|
||||
from openvino.runtime.opset8.ops import gather
|
||||
from openvino.runtime.opset6.ops import gather_elements
|
||||
from openvino.runtime.opset8.ops import gather_nd
|
||||
from openvino.runtime.opset1.ops import gather_tree
|
||||
from openvino.runtime.opset7.ops import gelu
|
||||
from openvino.runtime.opset9.ops import generate_proposals
|
||||
from openvino.runtime.opset1.ops import greater
|
||||
from openvino.runtime.opset1.ops import greater_equal
|
||||
from openvino.runtime.opset9.ops import grid_sample
|
||||
from openvino.runtime.opset1.ops import grn
|
||||
from openvino.runtime.opset1.ops import group_convolution
|
||||
from openvino.runtime.opset1.ops import group_convolution_backprop_data
|
||||
from openvino.runtime.opset3.ops import gru_cell
|
||||
from openvino.runtime.opset5.ops import gru_sequence
|
||||
from openvino.runtime.opset1.ops import hard_sigmoid
|
||||
from openvino.runtime.opset5.ops import hsigmoid
|
||||
from openvino.runtime.opset4.ops import hswish
|
||||
from openvino.runtime.opset7.ops import idft
|
||||
from openvino.runtime.opset8.ops import if_op
|
||||
from openvino.runtime.opset11.ops import interpolate
|
||||
from openvino.runtime.opset9.ops import irdft
|
||||
from openvino.runtime.opset10.ops import is_finite
|
||||
from openvino.runtime.opset10.ops import is_inf
|
||||
from openvino.runtime.opset10.ops import is_nan
|
||||
from openvino.runtime.opset8.ops import i420_to_bgr
|
||||
from openvino.runtime.opset8.ops import i420_to_rgb
|
||||
from openvino.runtime.opset1.ops import less
|
||||
from openvino.runtime.opset1.ops import less_equal
|
||||
from openvino.runtime.opset1.ops import log
|
||||
from openvino.runtime.opset1.ops import logical_and
|
||||
from openvino.runtime.opset1.ops import logical_not
|
||||
from openvino.runtime.opset1.ops import logical_or
|
||||
from openvino.runtime.opset1.ops import logical_xor
|
||||
from openvino.runtime.opset5.ops import log_softmax
|
||||
from openvino.runtime.opset5.ops import loop
|
||||
from openvino.runtime.opset1.ops import lrn
|
||||
from openvino.runtime.opset4.ops import lstm_cell
|
||||
from openvino.runtime.opset5.ops import lstm_sequence
|
||||
from openvino.runtime.opset1.ops import matmul
|
||||
from openvino.runtime.opset8.ops import matrix_nms
|
||||
from openvino.runtime.opset8.ops import max_pool
|
||||
from openvino.runtime.opset1.ops import maximum
|
||||
from openvino.runtime.opset1.ops import minimum
|
||||
from openvino.runtime.opset4.ops import mish
|
||||
from openvino.runtime.opset1.ops import mod
|
||||
from openvino.runtime.opset9.ops import multiclass_nms
|
||||
from openvino.runtime.opset1.ops import multiply
|
||||
from openvino.runtime.opset6.ops import mvn
|
||||
from openvino.runtime.opset1.ops import negative
|
||||
from openvino.runtime.opset9.ops import non_max_suppression
|
||||
from openvino.runtime.opset3.ops import non_zero
|
||||
from openvino.runtime.opset1.ops import normalize_l2
|
||||
from openvino.runtime.opset1.ops import not_equal
|
||||
from openvino.runtime.opset8.ops import nv12_to_bgr
|
||||
from openvino.runtime.opset8.ops import nv12_to_rgb
|
||||
from openvino.runtime.opset1.ops import one_hot
|
||||
from openvino.runtime.opset1.ops import pad
|
||||
from openvino.runtime.opset1.ops import parameter
|
||||
from openvino.runtime.opset1.ops import power
|
||||
from openvino.runtime.opset1.ops import prelu
|
||||
from openvino.runtime.opset8.ops import prior_box
|
||||
from openvino.runtime.opset1.ops import prior_box_clustered
|
||||
from openvino.runtime.opset1.ops import psroi_pooling
|
||||
from openvino.runtime.opset4.ops import proposal
|
||||
from openvino.runtime.opset1.ops import range
|
||||
from openvino.runtime.opset8.ops import random_uniform
|
||||
from openvino.runtime.opset9.ops import rdft
|
||||
from openvino.runtime.opset3.ops import read_value
|
||||
from openvino.runtime.opset4.ops import reduce_l1
|
||||
from openvino.runtime.opset4.ops import reduce_l2
|
||||
from openvino.runtime.opset1.ops import reduce_logical_and
|
||||
from openvino.runtime.opset1.ops import reduce_logical_or
|
||||
from openvino.runtime.opset1.ops import reduce_max
|
||||
from openvino.runtime.opset1.ops import reduce_mean
|
||||
from openvino.runtime.opset1.ops import reduce_min
|
||||
from openvino.runtime.opset1.ops import reduce_prod
|
||||
from openvino.runtime.opset1.ops import reduce_sum
|
||||
from openvino.runtime.opset1.ops import region_yolo
|
||||
from openvino.runtime.opset2.ops import reorg_yolo
|
||||
from openvino.runtime.opset1.ops import relu
|
||||
from openvino.runtime.opset1.ops import reshape
|
||||
from openvino.runtime.opset1.ops import result
|
||||
from openvino.runtime.opset1.ops import reverse_sequence
|
||||
from openvino.runtime.opset3.ops import rnn_cell
|
||||
from openvino.runtime.opset5.ops import rnn_sequence
|
||||
from openvino.runtime.opset9.ops import roi_align
|
||||
from openvino.runtime.opset2.ops import roi_pooling
|
||||
from openvino.runtime.opset7.ops import roll
|
||||
from openvino.runtime.opset5.ops import round
|
||||
from openvino.runtime.opset3.ops import scatter_elements_update
|
||||
from openvino.runtime.opset3.ops import scatter_update
|
||||
from openvino.runtime.opset1.ops import select
|
||||
from openvino.runtime.opset1.ops import selu
|
||||
from openvino.runtime.opset3.ops import shape_of
|
||||
from openvino.runtime.opset3.ops import shuffle_channels
|
||||
from openvino.runtime.opset1.ops import sigmoid
|
||||
from openvino.runtime.opset1.ops import sign
|
||||
from openvino.runtime.opset1.ops import sin
|
||||
from openvino.runtime.opset1.ops import sinh
|
||||
from openvino.runtime.opset8.ops import slice
|
||||
from openvino.runtime.opset8.ops import softmax
|
||||
from openvino.runtime.opset4.ops import softplus
|
||||
from openvino.runtime.opset9.ops import softsign
|
||||
from openvino.runtime.opset2.ops import space_to_batch
|
||||
from openvino.runtime.opset1.ops import space_to_depth
|
||||
from openvino.runtime.opset1.ops import split
|
||||
from openvino.runtime.opset1.ops import sqrt
|
||||
from openvino.runtime.opset1.ops import squared_difference
|
||||
from openvino.runtime.opset1.ops import squeeze
|
||||
from openvino.runtime.opset1.ops import strided_slice
|
||||
from openvino.runtime.opset1.ops import subtract
|
||||
from openvino.runtime.opset4.ops import swish
|
||||
from openvino.runtime.opset1.ops import tan
|
||||
from openvino.runtime.opset1.ops import tanh
|
||||
from openvino.runtime.opset1.ops import tensor_iterator
|
||||
from openvino.runtime.opset1.ops import tile
|
||||
from openvino.runtime.opset11.ops import topk
|
||||
from openvino.runtime.opset1.ops import transpose
|
||||
from openvino.runtime.opset10.ops import unique
|
||||
from openvino.runtime.opset1.ops import unsqueeze
|
||||
from openvino.runtime.opset1.ops import variadic_split
|
||||
Binary file not shown.
Binary file not shown.
@ -0,0 +1,107 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""Factory functions for all openvino ops."""
|
||||
from functools import partial
|
||||
from typing import List, Optional
|
||||
|
||||
from openvino.runtime import Node
|
||||
from openvino.runtime.opset_utils import _get_node_factory
|
||||
from openvino.runtime.utils.decorators import nameable_op
|
||||
from openvino.runtime.utils.types import (
|
||||
NodeInput,
|
||||
as_nodes,
|
||||
)
|
||||
|
||||
_get_node_factory_opset11 = partial(_get_node_factory, "opset11")
|
||||
|
||||
# -------------------------------------------- ops ------------------------------------------------
|
||||
|
||||
|
||||
@nameable_op
|
||||
def interpolate(
|
||||
image: NodeInput,
|
||||
scales_or_sizes: NodeInput,
|
||||
mode: str,
|
||||
shape_calculation_mode: str,
|
||||
pads_begin: Optional[List[int]] = None,
|
||||
pads_end: Optional[List[int]] = None,
|
||||
coordinate_transformation_mode: str = "half_pixel",
|
||||
nearest_mode: str = "round_prefer_floor",
|
||||
antialias: bool = False,
|
||||
cube_coeff: float = -0.75,
|
||||
axes: Optional[NodeInput] = None,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Perfors the interpolation of the input tensor.
|
||||
|
||||
:param image: The node providing input tensor with data for interpolation.
|
||||
:param scales_or_sizes:
|
||||
1D tensor providing information used to calculate the output shape
|
||||
of the operation. It might contain floats (scales) or integers(sizes).
|
||||
:param mode: Specifies type of interpolation. Possible values are: nearest, linear,
|
||||
linear_onnx, cubic, bilinear_pillow, bicubic_pillow.
|
||||
:param shape_calculation_mode:
|
||||
Specifies how the scales_or_sizes input should be interpreted.
|
||||
:param pads_begin: Specifies the number of pixels to add to the beginning of the image
|
||||
being interpolated. Default is None.
|
||||
:param pads_end: Specifies the number of pixels to add to the end of the image being
|
||||
interpolated. Default is None.
|
||||
:param coordinate_transformation_mode:
|
||||
Specifies how to transform the coordinate in the resized tensor to the
|
||||
coordinate in the original tensor. Default is "half_pixel".
|
||||
:param nearest_mode: Specifies round mode when mode == nearest and is used only when
|
||||
mode == nearest. Default is "round_prefer_floor".
|
||||
:param antialias: Specifies whether to perform anti-aliasing. Default is False.
|
||||
:param cube_coeff: Specifies the parameter a for cubic interpolation. Default is -0.75.
|
||||
:param axes: 1D tensor specifying dimension indices where interpolation is applied.
|
||||
The default is None.
|
||||
:param name: Optional name for the output node. The default is None.
|
||||
:return: Node representing the interpolation operation.
|
||||
"""
|
||||
attrs = {
|
||||
"mode": mode,
|
||||
"shape_calculation_mode": shape_calculation_mode,
|
||||
"coordinate_transformation_mode": coordinate_transformation_mode,
|
||||
"nearest_mode": nearest_mode,
|
||||
"antialias": antialias,
|
||||
"cube_coeff": cube_coeff,
|
||||
}
|
||||
|
||||
attrs["pads_begin"] = [] if pads_begin is None else pads_begin
|
||||
attrs["pads_end"] = [] if pads_end is None else pads_end
|
||||
|
||||
inputs = as_nodes(image, scales_or_sizes) if axes is None else as_nodes(image, scales_or_sizes, axes)
|
||||
|
||||
return _get_node_factory_opset11().create("Interpolate", inputs, attrs)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def topk(
|
||||
data: NodeInput,
|
||||
k: NodeInput,
|
||||
axis: int,
|
||||
mode: str,
|
||||
sort: str,
|
||||
index_element_type: str = "i32",
|
||||
stable: bool = False,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return a node which performs TopK.
|
||||
|
||||
:param data: Input data.
|
||||
:param k: K.
|
||||
:param axis: TopK Axis.
|
||||
:param mode: Compute TopK largest ('max') or smallest ('min')
|
||||
:param sort: Order of output elements (sort by: 'none', 'index' or 'value')
|
||||
:param index_element_type: Type of output tensor with indices.
|
||||
:param stable: Specifies whether the equivalent elements should maintain
|
||||
their relative order from the input tensor during sorting.
|
||||
:return: The new node which performs TopK
|
||||
"""
|
||||
return _get_node_factory_opset11().create(
|
||||
"TopK",
|
||||
as_nodes(data, k),
|
||||
{"axis": axis, "mode": mode, "sort": sort, "index_element_type": index_element_type, "stable": stable},
|
||||
)
|
||||
@ -0,0 +1,118 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from openvino.runtime.opset1.ops import absolute
|
||||
from openvino.runtime.opset1.ops import absolute as abs
|
||||
from openvino.runtime.opset1.ops import acos
|
||||
from openvino.runtime.opset1.ops import add
|
||||
from openvino.runtime.opset1.ops import asin
|
||||
from openvino.runtime.opset1.ops import atan
|
||||
from openvino.runtime.opset1.ops import avg_pool
|
||||
from openvino.runtime.opset1.ops import batch_norm_inference
|
||||
from openvino.runtime.opset2.ops import batch_to_space
|
||||
from openvino.runtime.opset1.ops import binary_convolution
|
||||
from openvino.runtime.opset1.ops import broadcast
|
||||
from openvino.runtime.opset1.ops import ceiling
|
||||
from openvino.runtime.opset1.ops import ceiling as ceil
|
||||
from openvino.runtime.opset1.ops import clamp
|
||||
from openvino.runtime.opset1.ops import concat
|
||||
from openvino.runtime.opset1.ops import constant
|
||||
from openvino.runtime.opset1.ops import convert
|
||||
from openvino.runtime.opset1.ops import convert_like
|
||||
from openvino.runtime.opset1.ops import convolution
|
||||
from openvino.runtime.opset1.ops import convolution_backprop_data
|
||||
from openvino.runtime.opset1.ops import cos
|
||||
from openvino.runtime.opset1.ops import cosh
|
||||
from openvino.runtime.opset1.ops import ctc_greedy_decoder
|
||||
from openvino.runtime.opset1.ops import deformable_convolution
|
||||
from openvino.runtime.opset1.ops import deformable_psroi_pooling
|
||||
from openvino.runtime.opset1.ops import depth_to_space
|
||||
from openvino.runtime.opset1.ops import detection_output
|
||||
from openvino.runtime.opset1.ops import divide
|
||||
from openvino.runtime.opset1.ops import elu
|
||||
from openvino.runtime.opset1.ops import equal
|
||||
from openvino.runtime.opset1.ops import erf
|
||||
from openvino.runtime.opset1.ops import exp
|
||||
from openvino.runtime.opset1.ops import fake_quantize
|
||||
from openvino.runtime.opset1.ops import floor
|
||||
from openvino.runtime.opset1.ops import floor_mod
|
||||
from openvino.runtime.opset1.ops import gather
|
||||
from openvino.runtime.opset1.ops import gather_tree
|
||||
from openvino.runtime.opset2.ops import gelu
|
||||
from openvino.runtime.opset1.ops import greater
|
||||
from openvino.runtime.opset1.ops import greater_equal
|
||||
from openvino.runtime.opset1.ops import grn
|
||||
from openvino.runtime.opset1.ops import group_convolution
|
||||
from openvino.runtime.opset1.ops import group_convolution_backprop_data
|
||||
from openvino.runtime.opset1.ops import hard_sigmoid
|
||||
from openvino.runtime.opset1.ops import interpolate
|
||||
from openvino.runtime.opset1.ops import less
|
||||
from openvino.runtime.opset1.ops import less_equal
|
||||
from openvino.runtime.opset1.ops import log
|
||||
from openvino.runtime.opset1.ops import logical_and
|
||||
from openvino.runtime.opset1.ops import logical_not
|
||||
from openvino.runtime.opset1.ops import logical_or
|
||||
from openvino.runtime.opset1.ops import logical_xor
|
||||
from openvino.runtime.opset1.ops import lrn
|
||||
from openvino.runtime.opset1.ops import lstm_cell
|
||||
from openvino.runtime.opset1.ops import lstm_sequence
|
||||
from openvino.runtime.opset1.ops import matmul
|
||||
from openvino.runtime.opset1.ops import max_pool
|
||||
from openvino.runtime.opset1.ops import maximum
|
||||
from openvino.runtime.opset1.ops import minimum
|
||||
from openvino.runtime.opset1.ops import mod
|
||||
from openvino.runtime.opset1.ops import multiply
|
||||
from openvino.runtime.opset2.ops import mvn
|
||||
from openvino.runtime.opset1.ops import negative
|
||||
from openvino.runtime.opset1.ops import non_max_suppression
|
||||
from openvino.runtime.opset1.ops import normalize_l2
|
||||
from openvino.runtime.opset1.ops import not_equal
|
||||
from openvino.runtime.opset1.ops import one_hot
|
||||
from openvino.runtime.opset1.ops import pad
|
||||
from openvino.runtime.opset1.ops import parameter
|
||||
from openvino.runtime.opset1.ops import power
|
||||
from openvino.runtime.opset1.ops import prelu
|
||||
from openvino.runtime.opset1.ops import prior_box
|
||||
from openvino.runtime.opset1.ops import prior_box_clustered
|
||||
from openvino.runtime.opset1.ops import psroi_pooling
|
||||
from openvino.runtime.opset1.ops import proposal
|
||||
from openvino.runtime.opset1.ops import range
|
||||
from openvino.runtime.opset1.ops import reduce_logical_and
|
||||
from openvino.runtime.opset1.ops import reduce_logical_or
|
||||
from openvino.runtime.opset1.ops import reduce_max
|
||||
from openvino.runtime.opset1.ops import reduce_mean
|
||||
from openvino.runtime.opset1.ops import reduce_min
|
||||
from openvino.runtime.opset1.ops import reduce_prod
|
||||
from openvino.runtime.opset1.ops import reduce_sum
|
||||
from openvino.runtime.opset1.ops import region_yolo
|
||||
from openvino.runtime.opset2.ops import reorg_yolo
|
||||
from openvino.runtime.opset1.ops import relu
|
||||
from openvino.runtime.opset1.ops import reshape
|
||||
from openvino.runtime.opset1.ops import result
|
||||
from openvino.runtime.opset1.ops import reverse_sequence
|
||||
from openvino.runtime.opset2.ops import roi_pooling
|
||||
from openvino.runtime.opset1.ops import select
|
||||
from openvino.runtime.opset1.ops import selu
|
||||
from openvino.runtime.opset1.ops import shape_of
|
||||
from openvino.runtime.opset1.ops import sigmoid
|
||||
from openvino.runtime.opset1.ops import sign
|
||||
from openvino.runtime.opset1.ops import sin
|
||||
from openvino.runtime.opset1.ops import sinh
|
||||
from openvino.runtime.opset1.ops import softmax
|
||||
from openvino.runtime.opset2.ops import space_to_batch
|
||||
from openvino.runtime.opset1.ops import space_to_depth
|
||||
from openvino.runtime.opset1.ops import split
|
||||
from openvino.runtime.opset1.ops import sqrt
|
||||
from openvino.runtime.opset1.ops import squared_difference
|
||||
from openvino.runtime.opset1.ops import squeeze
|
||||
from openvino.runtime.opset1.ops import strided_slice
|
||||
from openvino.runtime.opset1.ops import subtract
|
||||
from openvino.runtime.opset1.ops import tan
|
||||
from openvino.runtime.opset1.ops import tanh
|
||||
from openvino.runtime.opset1.ops import tensor_iterator
|
||||
from openvino.runtime.opset1.ops import tile
|
||||
from openvino.runtime.opset1.ops import topk
|
||||
from openvino.runtime.opset1.ops import transpose
|
||||
from openvino.runtime.opset1.ops import unsqueeze
|
||||
from openvino.runtime.opset1.ops import variadic_split
|
||||
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@ -0,0 +1,182 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""Factory functions for all openvino ops."""
|
||||
from typing import Callable, Iterable, List, Optional, Set, Union
|
||||
|
||||
import numpy as np
|
||||
from functools import partial
|
||||
|
||||
from openvino.runtime import Node, Shape
|
||||
from openvino.runtime.op import Constant, Parameter
|
||||
from openvino.runtime.opset_utils import _get_node_factory
|
||||
from openvino.runtime.utils.decorators import binary_op, nameable_op, unary_op
|
||||
from openvino.runtime.utils.input_validation import (
|
||||
assert_list_of_ints,
|
||||
check_valid_attributes,
|
||||
is_non_negative_value,
|
||||
is_positive_value,
|
||||
)
|
||||
from openvino.runtime.utils.node_factory import NodeFactory
|
||||
from openvino.runtime.utils.types import (
|
||||
NodeInput,
|
||||
NumericData,
|
||||
NumericType,
|
||||
ScalarData,
|
||||
TensorShape,
|
||||
as_node,
|
||||
as_nodes,
|
||||
get_dtype,
|
||||
get_element_type,
|
||||
get_element_type_str,
|
||||
make_constant_node,
|
||||
)
|
||||
|
||||
_get_node_factory_opset2 = partial(_get_node_factory, "opset2")
|
||||
|
||||
# -------------------------------------------- ops ------------------------------------------------
|
||||
|
||||
|
||||
@nameable_op
|
||||
def batch_to_space(
|
||||
data: NodeInput,
|
||||
block_shape: NodeInput,
|
||||
crops_begin: NodeInput,
|
||||
crops_end: NodeInput,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Perform BatchToSpace operation on the input tensor.
|
||||
|
||||
BatchToSpace permutes data from the batch dimension of the data tensor into spatial dimensions.
|
||||
|
||||
:param data: Node producing the data tensor.
|
||||
:param block_shape: The sizes of the block of values to be moved.
|
||||
:param crops_begin: Specifies the amount to crop from the beginning along each axis of `data`.
|
||||
:param crops_end: Specifies the amount to crop from the end along each axis of `data`.
|
||||
:param name: Optional output node name.
|
||||
:return: The new node performing a BatchToSpace operation.
|
||||
"""
|
||||
return _get_node_factory_opset2().create(
|
||||
"BatchToSpace",
|
||||
as_nodes(data, block_shape, crops_begin, crops_end),
|
||||
)
|
||||
|
||||
|
||||
@unary_op
|
||||
def gelu(node: NodeInput, name: Optional[str] = None) -> Node:
|
||||
r"""Perform Gaussian Error Linear Unit operation element-wise on data from input node.
|
||||
|
||||
Computes GELU function:
|
||||
|
||||
\f[ f(x) = 0.5\cdot x\cdot(1 + erf( \dfrac{x}{\sqrt{2}}) \f]
|
||||
|
||||
For more information refer to [Gaussian Error Linear Unit (GELU)](https://arxiv.org/pdf/1606.08415.pdf>)
|
||||
|
||||
:param node: Input tensor. One of: input node, array or scalar.
|
||||
:param name: Optional output node name.
|
||||
:return: The new node performing a GELU operation on its input data element-wise.
|
||||
"""
|
||||
return _get_node_factory_opset2().create("Gelu", [node])
|
||||
|
||||
|
||||
@nameable_op
|
||||
def mvn(
|
||||
data: Node,
|
||||
across_channels: bool = False,
|
||||
normalize_variance: bool = False,
|
||||
eps: float = 1e-9,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
r"""Perform Mean Variance Normalization operation on data from input node.
|
||||
|
||||
Computes MVN on the input tensor `data` (called `X`) using formula:
|
||||
|
||||
\f[ Y = \dfrac{X-EX}{\sqrt{E(X-EX)^2}} \f]
|
||||
|
||||
:param data: The node with data tensor.
|
||||
:param across_channels: Denotes if mean values are shared across channels.
|
||||
:param normalize_variance: Denotes whether to perform variance normalization.
|
||||
:param eps: The number added to the variance to avoid division by zero
|
||||
when normalizing the value. Scalar value.
|
||||
:param name: Optional output node name.
|
||||
:return: The new node performing a MVN operation on input tensor.
|
||||
"""
|
||||
return _get_node_factory_opset2().create(
|
||||
"MVN",
|
||||
[data],
|
||||
{
|
||||
"across_channels": across_channels,
|
||||
"normalize_variance": normalize_variance,
|
||||
"eps": eps,
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def reorg_yolo(input: Node, stride: List[int], name: Optional[str] = None) -> Node:
|
||||
"""Return a node which produces the ReorgYolo operation.
|
||||
|
||||
:param input: Input data.
|
||||
:param stride: Stride to reorganize input by.
|
||||
:param name: Optional name for output node.
|
||||
:return: ReorgYolo node.
|
||||
"""
|
||||
return _get_node_factory_opset2().create("ReorgYolo", [input], {"stride": stride})
|
||||
|
||||
|
||||
@nameable_op
|
||||
def roi_pooling(
|
||||
input: NodeInput,
|
||||
coords: NodeInput,
|
||||
output_size: TensorShape,
|
||||
spatial_scale: NumericData,
|
||||
method: str,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return a node which produces an ROIPooling operation.
|
||||
|
||||
:param input: Input feature map `{N, C, ...}`.
|
||||
:param coords: Coordinates of bounding boxes.
|
||||
:param output_size: Height/Width of ROI output features (shape).
|
||||
:param spatial_scale: Ratio of input feature map over input image size (float).
|
||||
:param method: Method of pooling - string: "max" or "bilinear".
|
||||
:return: ROIPooling node.
|
||||
"""
|
||||
method = method.lower()
|
||||
return _get_node_factory_opset2().create(
|
||||
"ROIPooling",
|
||||
as_nodes(input, coords),
|
||||
{
|
||||
"output_size": Shape(output_size),
|
||||
"spatial_scale": spatial_scale,
|
||||
"method": method,
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def space_to_batch(
|
||||
data: NodeInput,
|
||||
block_shape: NodeInput,
|
||||
pads_begin: NodeInput,
|
||||
pads_end: NodeInput,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Perform SpaceToBatch operation on the input tensor.
|
||||
|
||||
SpaceToBatch permutes data tensor blocks of spatial data into batch dimension.
|
||||
The operator returns a copy of the input tensor where values from spatial blocks dimensions
|
||||
are moved in the batch dimension
|
||||
|
||||
:param data: Node producing the data tensor.
|
||||
:param block_shape: The sizes of the block of values to be moved.
|
||||
:param pads_begin: Specifies the padding for the beginning along each axis of `data`.
|
||||
:param pads_end: Specifies the padding for the ending along each axis of `data`.
|
||||
:param name: Optional output node name.
|
||||
:return: The new node performing a SpaceToBatch operation.
|
||||
"""
|
||||
return _get_node_factory_opset2().create(
|
||||
"SpaceToBatch",
|
||||
as_nodes(data, block_shape, pads_begin, pads_end),
|
||||
)
|
||||
@ -0,0 +1,134 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from openvino.runtime.opset1.ops import absolute
|
||||
from openvino.runtime.opset1.ops import absolute as abs
|
||||
from openvino.runtime.opset1.ops import acos
|
||||
from openvino.runtime.opset1.ops import add
|
||||
from openvino.runtime.opset1.ops import asin
|
||||
from openvino.runtime.opset3.ops import assign
|
||||
from openvino.runtime.opset1.ops import atan
|
||||
from openvino.runtime.opset1.ops import avg_pool
|
||||
from openvino.runtime.opset1.ops import batch_norm_inference
|
||||
from openvino.runtime.opset2.ops import batch_to_space
|
||||
from openvino.runtime.opset1.ops import binary_convolution
|
||||
from openvino.runtime.opset3.ops import broadcast
|
||||
from openvino.runtime.opset3.ops import bucketize
|
||||
from openvino.runtime.opset1.ops import ceiling
|
||||
from openvino.runtime.opset1.ops import ceiling as ceil
|
||||
from openvino.runtime.opset1.ops import clamp
|
||||
from openvino.runtime.opset1.ops import concat
|
||||
from openvino.runtime.opset1.ops import constant
|
||||
from openvino.runtime.opset1.ops import convert
|
||||
from openvino.runtime.opset1.ops import convert_like
|
||||
from openvino.runtime.opset1.ops import convolution
|
||||
from openvino.runtime.opset1.ops import convolution_backprop_data
|
||||
from openvino.runtime.opset1.ops import cos
|
||||
from openvino.runtime.opset1.ops import cosh
|
||||
from openvino.runtime.opset1.ops import ctc_greedy_decoder
|
||||
from openvino.runtime.opset3.ops import cum_sum
|
||||
from openvino.runtime.opset3.ops import cum_sum as cumsum
|
||||
from openvino.runtime.opset1.ops import deformable_convolution
|
||||
from openvino.runtime.opset1.ops import deformable_psroi_pooling
|
||||
from openvino.runtime.opset1.ops import depth_to_space
|
||||
from openvino.runtime.opset1.ops import detection_output
|
||||
from openvino.runtime.opset1.ops import divide
|
||||
from openvino.runtime.opset1.ops import elu
|
||||
from openvino.runtime.opset3.ops import embedding_bag_offsets_sum
|
||||
from openvino.runtime.opset3.ops import embedding_bag_packed_sum
|
||||
from openvino.runtime.opset3.ops import embedding_segments_sum
|
||||
from openvino.runtime.opset3.ops import extract_image_patches
|
||||
from openvino.runtime.opset1.ops import equal
|
||||
from openvino.runtime.opset1.ops import erf
|
||||
from openvino.runtime.opset1.ops import exp
|
||||
from openvino.runtime.opset1.ops import fake_quantize
|
||||
from openvino.runtime.opset1.ops import floor
|
||||
from openvino.runtime.opset1.ops import floor_mod
|
||||
from openvino.runtime.opset1.ops import gather
|
||||
from openvino.runtime.opset1.ops import gather_tree
|
||||
from openvino.runtime.opset2.ops import gelu
|
||||
from openvino.runtime.opset1.ops import greater
|
||||
from openvino.runtime.opset1.ops import greater_equal
|
||||
from openvino.runtime.opset1.ops import grn
|
||||
from openvino.runtime.opset1.ops import group_convolution
|
||||
from openvino.runtime.opset1.ops import group_convolution_backprop_data
|
||||
from openvino.runtime.opset3.ops import gru_cell
|
||||
from openvino.runtime.opset1.ops import hard_sigmoid
|
||||
from openvino.runtime.opset1.ops import interpolate
|
||||
from openvino.runtime.opset1.ops import less
|
||||
from openvino.runtime.opset1.ops import less_equal
|
||||
from openvino.runtime.opset1.ops import log
|
||||
from openvino.runtime.opset1.ops import logical_and
|
||||
from openvino.runtime.opset1.ops import logical_not
|
||||
from openvino.runtime.opset1.ops import logical_or
|
||||
from openvino.runtime.opset1.ops import logical_xor
|
||||
from openvino.runtime.opset1.ops import lrn
|
||||
from openvino.runtime.opset1.ops import lstm_cell
|
||||
from openvino.runtime.opset1.ops import lstm_sequence
|
||||
from openvino.runtime.opset1.ops import matmul
|
||||
from openvino.runtime.opset1.ops import max_pool
|
||||
from openvino.runtime.opset1.ops import maximum
|
||||
from openvino.runtime.opset1.ops import minimum
|
||||
from openvino.runtime.opset1.ops import mod
|
||||
from openvino.runtime.opset1.ops import multiply
|
||||
from openvino.runtime.opset2.ops import mvn
|
||||
from openvino.runtime.opset1.ops import negative
|
||||
from openvino.runtime.opset3.ops import non_max_suppression
|
||||
from openvino.runtime.opset3.ops import non_zero
|
||||
from openvino.runtime.opset1.ops import normalize_l2
|
||||
from openvino.runtime.opset1.ops import not_equal
|
||||
from openvino.runtime.opset1.ops import one_hot
|
||||
from openvino.runtime.opset1.ops import pad
|
||||
from openvino.runtime.opset1.ops import parameter
|
||||
from openvino.runtime.opset1.ops import power
|
||||
from openvino.runtime.opset1.ops import prelu
|
||||
from openvino.runtime.opset1.ops import prior_box
|
||||
from openvino.runtime.opset1.ops import prior_box_clustered
|
||||
from openvino.runtime.opset1.ops import psroi_pooling
|
||||
from openvino.runtime.opset1.ops import proposal
|
||||
from openvino.runtime.opset1.ops import range
|
||||
from openvino.runtime.opset3.ops import read_value
|
||||
from openvino.runtime.opset1.ops import reduce_logical_and
|
||||
from openvino.runtime.opset1.ops import reduce_logical_or
|
||||
from openvino.runtime.opset1.ops import reduce_max
|
||||
from openvino.runtime.opset1.ops import reduce_mean
|
||||
from openvino.runtime.opset1.ops import reduce_min
|
||||
from openvino.runtime.opset1.ops import reduce_prod
|
||||
from openvino.runtime.opset1.ops import reduce_sum
|
||||
from openvino.runtime.opset1.ops import region_yolo
|
||||
from openvino.runtime.opset2.ops import reorg_yolo
|
||||
from openvino.runtime.opset1.ops import relu
|
||||
from openvino.runtime.opset1.ops import reshape
|
||||
from openvino.runtime.opset1.ops import result
|
||||
from openvino.runtime.opset1.ops import reverse_sequence
|
||||
from openvino.runtime.opset3.ops import rnn_cell
|
||||
from openvino.runtime.opset3.ops import roi_align
|
||||
from openvino.runtime.opset2.ops import roi_pooling
|
||||
from openvino.runtime.opset3.ops import scatter_elements_update
|
||||
from openvino.runtime.opset3.ops import scatter_update
|
||||
from openvino.runtime.opset1.ops import select
|
||||
from openvino.runtime.opset1.ops import selu
|
||||
from openvino.runtime.opset3.ops import shape_of
|
||||
from openvino.runtime.opset3.ops import shuffle_channels
|
||||
from openvino.runtime.opset1.ops import sigmoid
|
||||
from openvino.runtime.opset1.ops import sign
|
||||
from openvino.runtime.opset1.ops import sin
|
||||
from openvino.runtime.opset1.ops import sinh
|
||||
from openvino.runtime.opset1.ops import softmax
|
||||
from openvino.runtime.opset2.ops import space_to_batch
|
||||
from openvino.runtime.opset1.ops import space_to_depth
|
||||
from openvino.runtime.opset1.ops import split
|
||||
from openvino.runtime.opset1.ops import sqrt
|
||||
from openvino.runtime.opset1.ops import squared_difference
|
||||
from openvino.runtime.opset1.ops import squeeze
|
||||
from openvino.runtime.opset1.ops import strided_slice
|
||||
from openvino.runtime.opset1.ops import subtract
|
||||
from openvino.runtime.opset1.ops import tan
|
||||
from openvino.runtime.opset1.ops import tanh
|
||||
from openvino.runtime.opset1.ops import tensor_iterator
|
||||
from openvino.runtime.opset1.ops import tile
|
||||
from openvino.runtime.opset3.ops import topk
|
||||
from openvino.runtime.opset1.ops import transpose
|
||||
from openvino.runtime.opset1.ops import unsqueeze
|
||||
from openvino.runtime.opset1.ops import variadic_split
|
||||
Binary file not shown.
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@ -0,0 +1,638 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""Factory functions for all openvino ops."""
|
||||
from typing import Callable, Iterable, List, Optional, Set, Union
|
||||
|
||||
import numpy as np
|
||||
from functools import partial
|
||||
|
||||
from openvino.runtime import Node, Shape
|
||||
from openvino.runtime.op import Constant, Parameter
|
||||
from openvino.runtime.opset_utils import _get_node_factory
|
||||
from openvino.runtime.utils.decorators import binary_op, nameable_op, unary_op
|
||||
from openvino.runtime.utils.input_validation import (
|
||||
assert_list_of_ints,
|
||||
check_valid_attributes,
|
||||
is_non_negative_value,
|
||||
is_positive_value,
|
||||
)
|
||||
from openvino.runtime.utils.node_factory import NodeFactory
|
||||
from openvino.runtime.utils.types import (
|
||||
NodeInput,
|
||||
NumericData,
|
||||
NumericType,
|
||||
ScalarData,
|
||||
TensorShape,
|
||||
as_node,
|
||||
as_nodes,
|
||||
get_dtype,
|
||||
get_element_type,
|
||||
get_element_type_str,
|
||||
make_constant_node,
|
||||
)
|
||||
|
||||
_get_node_factory_opset3 = partial(_get_node_factory, "opset3")
|
||||
|
||||
# -------------------------------------------- ops ------------------------------------------------
|
||||
|
||||
|
||||
@nameable_op
|
||||
def assign(new_value: NodeInput, variable_id: str, name: Optional[str] = None) -> Node:
|
||||
"""Return a node which produces the Assign operation.
|
||||
|
||||
:param new_value: Node producing a value to be assigned to a variable.
|
||||
:param variable_id: Id of a variable to be updated.
|
||||
:param name: Optional name for output node.
|
||||
:return: Assign node
|
||||
"""
|
||||
return _get_node_factory_opset3().create(
|
||||
"Assign",
|
||||
[as_node(new_value)],
|
||||
{"variable_id": variable_id},
|
||||
)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def broadcast(
|
||||
data: NodeInput,
|
||||
target_shape: NodeInput,
|
||||
axes_mapping: Optional[NodeInput] = None,
|
||||
broadcast_spec: str = "NUMPY",
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Create a node which broadcasts the input node's values along specified axes to a desired shape.
|
||||
|
||||
:param data: The node with input tensor data.
|
||||
:param target_shape: The node with a new shape we want to broadcast tensor to.
|
||||
:param axes_mapping: The node with a axis positions (0-based) in the result
|
||||
that are being broadcast.
|
||||
:param broadcast_spec: The type of broadcasting that specifies mapping of input tensor axes
|
||||
to output shape axes. Range of values: NUMPY, EXPLICIT, BIDIRECTIONAL.
|
||||
:param name: Optional new name for output node.
|
||||
:return: New node with broadcast shape.
|
||||
"""
|
||||
inputs = as_nodes(data, target_shape)
|
||||
if broadcast_spec.upper() == "EXPLICIT":
|
||||
inputs.append(as_node(axes_mapping))
|
||||
return _get_node_factory_opset3().create(
|
||||
"Broadcast",
|
||||
inputs,
|
||||
{"mode": broadcast_spec.upper()},
|
||||
)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def bucketize(
|
||||
data: Node,
|
||||
buckets: NodeInput,
|
||||
output_type: str = "i64",
|
||||
with_right_bound: bool = True,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return a node which produces the Bucketize operation.
|
||||
|
||||
:param data: Input data to bucketize
|
||||
:param buckets: 1-D of sorted unique boundaries for buckets
|
||||
:param output_type: Output tensor type, "i64" or "i32", defaults to i64
|
||||
:param with_right_bound: indicates whether bucket includes the right or left
|
||||
edge of interval. default true = includes right edge
|
||||
:param name: Optional name for output node.
|
||||
:return: Bucketize node
|
||||
"""
|
||||
return _get_node_factory_opset3().create(
|
||||
"Bucketize",
|
||||
[data, as_node(buckets)],
|
||||
{"output_type": output_type, "with_right_bound": with_right_bound},
|
||||
)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def cum_sum(
|
||||
arg: NodeInput,
|
||||
axis: NodeInput,
|
||||
exclusive: bool = False,
|
||||
reverse: bool = False,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Construct a cumulative summation operation.
|
||||
|
||||
:param arg: The tensor to be summed.
|
||||
:param axis: zero dimension tensor specifying axis position along which sum will be performed.
|
||||
:param exclusive: if set to true, the top element is not included
|
||||
:param reverse: if set to true, will perform the sums in reverse direction
|
||||
:return: New node performing the operation
|
||||
"""
|
||||
return _get_node_factory_opset3().create(
|
||||
"CumSum",
|
||||
as_nodes(arg, axis),
|
||||
{"exclusive": exclusive, "reverse": reverse},
|
||||
)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def embedding_bag_offsets_sum(
|
||||
emb_table: Node,
|
||||
indices: NodeInput,
|
||||
offsets: NodeInput,
|
||||
default_index: Optional[NodeInput] = None,
|
||||
per_sample_weights: Optional[NodeInput] = None,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return a node which performs sums of bags of embeddings without the intermediate embeddings.
|
||||
|
||||
:param emb_table: Tensor containing the embedding lookup table.
|
||||
:param indices: Tensor with indices.
|
||||
:param offsets: Tensor containing the starting index positions of each bag in indices.
|
||||
:param per_sample_weights: Tensor with weights for each sample.
|
||||
:param default_index: Scalar containing default index in embedding table to fill empty bags.
|
||||
:param name: Optional name for output node.
|
||||
:return: The new node which performs EmbeddingBagOffsetsSum
|
||||
"""
|
||||
inputs = [emb_table, as_node(indices), as_node(offsets)]
|
||||
if per_sample_weights is not None:
|
||||
inputs.append(default_index)
|
||||
inputs.append(per_sample_weights)
|
||||
elif default_index is not None:
|
||||
inputs.append(default_index)
|
||||
|
||||
return _get_node_factory_opset3().create("EmbeddingBagOffsetsSum", inputs, {})
|
||||
|
||||
|
||||
@nameable_op
|
||||
def embedding_bag_packed_sum(
|
||||
emb_table: NodeInput,
|
||||
indices: NodeInput,
|
||||
per_sample_weights: Optional[NodeInput] = None,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return an EmbeddingBagPackedSum node.
|
||||
|
||||
EmbeddingSegmentsSum constructs an output tensor by replacing every index in a given
|
||||
input tensor with a row (from the weights matrix) at that index
|
||||
|
||||
:param emb_table: Tensor containing the embedding lookup table.
|
||||
:param indices: Tensor with indices.
|
||||
:param per_sample_weights: Weights to be multiplied with embedding table.
|
||||
:param name: Optional name for output node.
|
||||
:return: EmbeddingBagPackedSum node
|
||||
"""
|
||||
inputs = [as_node(emb_table), as_node(indices)]
|
||||
if per_sample_weights is not None:
|
||||
inputs.append(as_node(per_sample_weights))
|
||||
|
||||
return _get_node_factory_opset3().create("EmbeddingBagPackedSum", inputs, {})
|
||||
|
||||
|
||||
@nameable_op
|
||||
def embedding_segments_sum(
|
||||
emb_table: Node,
|
||||
indices: NodeInput,
|
||||
segment_ids: NodeInput,
|
||||
num_segments: Optional[NodeInput] = None,
|
||||
default_index: Optional[NodeInput] = None,
|
||||
per_sample_weights: Optional[NodeInput] = None,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return an EmbeddingSegmentsSum node.
|
||||
|
||||
EmbeddingSegmentsSum constructs an output tensor by replacing every index in a given
|
||||
input tensor with a row (from the weights matrix) at that index
|
||||
|
||||
:param emb_table: Tensor containing the embedding lookup table.
|
||||
:param indices: Tensor with indices.
|
||||
:param segment_ids: Tensor with indices into the output Tensor
|
||||
:param num_segments: Tensor with number of segments.
|
||||
:param default_index: Scalar containing default index in embedding table to fill empty bags.
|
||||
:param per_sample_weights: Weights to be multiplied with embedding table.
|
||||
:param name: Optional name for output node.
|
||||
:return: EmbeddingSegmentsSum node
|
||||
"""
|
||||
inputs = [as_node(emb_table), as_node(indices), as_node(segment_ids)]
|
||||
if per_sample_weights is not None:
|
||||
inputs.append(as_node(num_segments))
|
||||
inputs.append(as_node(default_index))
|
||||
inputs.append(as_node(per_sample_weights))
|
||||
elif default_index is not None:
|
||||
inputs.append(as_node(num_segments))
|
||||
inputs.append(as_node(default_index))
|
||||
elif num_segments is not None:
|
||||
inputs.append(as_node(num_segments))
|
||||
|
||||
return _get_node_factory_opset3().create("EmbeddingSegmentsSum", inputs, {})
|
||||
|
||||
|
||||
@nameable_op
|
||||
def extract_image_patches(
|
||||
image: NodeInput,
|
||||
sizes: TensorShape,
|
||||
strides: List[int],
|
||||
rates: TensorShape,
|
||||
auto_pad: str,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return a node which produces the ExtractImagePatches operation.
|
||||
|
||||
:param image: 4-D Input data to extract image patches.
|
||||
:param sizes: Patch size in the format of [size_rows, size_cols].
|
||||
:param strides: Patch movement stride in the format of [stride_rows, stride_cols]
|
||||
:param rates: Element seleciton rate for creating a patch.
|
||||
:param auto_pad: Padding type.
|
||||
:param name: Optional name for output node.
|
||||
:return: ExtractImagePatches node
|
||||
"""
|
||||
return _get_node_factory_opset3().create(
|
||||
"ExtractImagePatches",
|
||||
[as_node(image)],
|
||||
{"sizes": sizes, "strides": strides, "rates": rates, "auto_pad": auto_pad},
|
||||
)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def gru_cell(
|
||||
X: NodeInput,
|
||||
initial_hidden_state: NodeInput,
|
||||
W: NodeInput,
|
||||
R: NodeInput,
|
||||
B: NodeInput,
|
||||
hidden_size: int,
|
||||
activations: Optional[List[str]] = None,
|
||||
activations_alpha: Optional[List[float]] = None,
|
||||
activations_beta: Optional[List[float]] = None,
|
||||
clip: float = 0.0,
|
||||
linear_before_reset: bool = False,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Perform GRUCell operation on the tensor from input node.
|
||||
|
||||
GRUCell represents a single GRU Cell that computes the output
|
||||
using the formula described in the paper: https://arxiv.org/abs/1406.1078
|
||||
|
||||
Note this class represents only single *cell* and not whole *layer*.
|
||||
|
||||
:param X: The input tensor with shape: [batch_size, input_size].
|
||||
:param initial_hidden_state: The hidden state tensor at current time step with shape:
|
||||
[batch_size, hidden_size].
|
||||
:param W: The weights for matrix multiplication, gate order: zrh.
|
||||
Shape: [3*hidden_size, input_size].
|
||||
:param R: The recurrence weights for matrix multiplication.
|
||||
Shape: [3*hidden_size, hidden_size].
|
||||
:param B: The sum of biases (weight and recurrence).
|
||||
For linear_before_reset set True the shape is [4*hidden_size].
|
||||
Otherwise the shape is [3*hidden_size].
|
||||
:param hidden_size: The number of hidden units for recurrent cell.
|
||||
Specifies hidden state size.
|
||||
:param activations: The vector of activation functions used inside recurrent cell.
|
||||
:param activation_alpha: The vector of alpha parameters for activation functions in
|
||||
order respective to activation list.
|
||||
:param activation_beta: The vector of beta parameters for activation functions in order
|
||||
respective to activation list.
|
||||
:param clip: The value defining clipping range [-clip, clip] on input of
|
||||
activation functions.
|
||||
:param linear_before_reset: Flag denotes if the layer behaves according to the modification
|
||||
of GRUCell described in the formula in the ONNX documentation.
|
||||
:param name: Optional output node name.
|
||||
:return: The new node performing a GRUCell operation on tensor from input node.
|
||||
"""
|
||||
if activations is None:
|
||||
activations = ["sigmoid", "tanh"]
|
||||
if activations_alpha is None:
|
||||
activations_alpha = []
|
||||
if activations_beta is None:
|
||||
activations_beta = []
|
||||
|
||||
input_nodes = as_nodes(X, initial_hidden_state, W, R, B)
|
||||
attributes = {
|
||||
"hidden_size": hidden_size,
|
||||
"activations": activations,
|
||||
"activations_alpha": activations_alpha,
|
||||
"activations_beta": activations_beta,
|
||||
"linear_before_reset": linear_before_reset,
|
||||
"clip": clip,
|
||||
}
|
||||
return _get_node_factory_opset3().create("GRUCell", input_nodes, attributes)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def non_max_suppression(
|
||||
boxes: NodeInput,
|
||||
scores: NodeInput,
|
||||
max_output_boxes_per_class: Optional[NodeInput] = None,
|
||||
iou_threshold: Optional[NodeInput] = None,
|
||||
score_threshold: Optional[NodeInput] = None,
|
||||
box_encoding: str = "corner",
|
||||
sort_result_descending: bool = True,
|
||||
output_type: str = "i64",
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return a node which performs NonMaxSuppression.
|
||||
|
||||
:param boxes: Tensor with box coordinates.
|
||||
:param scores: Tensor with box scores.
|
||||
:param max_output_boxes_per_class: Tensor Specifying maximum number of boxes
|
||||
to be selected per class.
|
||||
:param iou_threshold: Tensor specifying intersection over union threshold
|
||||
:param score_threshold: Tensor specifying minimum score to consider box for the processing.
|
||||
:param box_encoding: Format of boxes data encoding.
|
||||
:param sort_result_descending: Flag that specifies whenever it is necessary to sort selected
|
||||
boxes across batches or not.
|
||||
:param output_type: Output element type.
|
||||
:return: The new node which performs NonMaxSuppression
|
||||
"""
|
||||
if max_output_boxes_per_class is None:
|
||||
max_output_boxes_per_class = make_constant_node(0, np.int64)
|
||||
if iou_threshold is None:
|
||||
iou_threshold = make_constant_node(0, np.float32)
|
||||
if score_threshold is None:
|
||||
score_threshold = make_constant_node(0, np.float32)
|
||||
|
||||
inputs = as_nodes(boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold)
|
||||
attributes = {
|
||||
"box_encoding": box_encoding,
|
||||
"sort_result_descending": sort_result_descending,
|
||||
"output_type": output_type,
|
||||
}
|
||||
|
||||
return _get_node_factory_opset3().create("NonMaxSuppression", inputs, attributes)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def non_zero(data: NodeInput, output_type: str = "i64", name: Optional[str] = None) -> Node:
|
||||
"""Return the indices of the elements that are non-zero.
|
||||
|
||||
:param data: Input data.
|
||||
:param output_type: Output tensor type.
|
||||
|
||||
:return: The new node which performs NonZero
|
||||
"""
|
||||
return _get_node_factory_opset3().create(
|
||||
"NonZero",
|
||||
[as_node(data)],
|
||||
{"output_type": output_type},
|
||||
)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def read_value(init_value: NodeInput, variable_id: str, name: Optional[str] = None) -> Node:
|
||||
"""Return a node which produces the Assign operation.
|
||||
|
||||
:param init_value: Node producing a value to be returned instead of an unassigned variable.
|
||||
:param variable_id: Id of a variable to be read.
|
||||
:param name: Optional name for output node.
|
||||
:return: ReadValue node
|
||||
"""
|
||||
return _get_node_factory_opset3().create(
|
||||
"ReadValue",
|
||||
[as_node(init_value)],
|
||||
{"variable_id": variable_id},
|
||||
)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def rnn_cell(
|
||||
X: NodeInput,
|
||||
initial_hidden_state: NodeInput,
|
||||
W: NodeInput,
|
||||
R: NodeInput,
|
||||
B: NodeInput,
|
||||
hidden_size: int,
|
||||
activations: List[str],
|
||||
activations_alpha: List[float],
|
||||
activations_beta: List[float],
|
||||
clip: float = 0.0,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Perform RNNCell operation on tensor from input node.
|
||||
|
||||
It follows notation and equations defined as in ONNX standard:
|
||||
https://github.com/onnx/onnx/blob/master/docs/Operators.md#RNN
|
||||
|
||||
Note this class represents only single *cell* and not whole RNN *layer*.
|
||||
|
||||
:param X: The input tensor with shape: [batch_size, input_size].
|
||||
:param initial_hidden_state: The hidden state tensor at current time step with shape:
|
||||
[batch_size, hidden_size].
|
||||
:param W: The weight tensor with shape: [hidden_size, input_size].
|
||||
:param R: The recurrence weight tensor with shape: [hidden_size,
|
||||
hidden_size].
|
||||
:param B: The sum of biases (weight and recurrence) with shape: [hidden_size].
|
||||
:param hidden_size: The number of hidden units for recurrent cell.
|
||||
Specifies hidden state size.
|
||||
:param activations: The vector of activation functions used inside recurrent cell.
|
||||
:param activation_alpha: The vector of alpha parameters for activation functions in
|
||||
order respective to activation list.
|
||||
:param activation_beta: The vector of beta parameters for activation functions in order
|
||||
respective to activation list.
|
||||
:param clip: The value defining clipping range [-clip, clip] on input of
|
||||
activation functions.
|
||||
:param name: Optional output node name.
|
||||
:return: The new node performing a RNNCell operation on tensor from input node.
|
||||
"""
|
||||
if activations is None:
|
||||
activations = ["tanh"]
|
||||
if activations_alpha is None:
|
||||
activations_alpha = []
|
||||
if activations_beta is None:
|
||||
activations_beta = []
|
||||
|
||||
input_nodes = as_nodes(X, initial_hidden_state, W, R, B)
|
||||
attributes = {
|
||||
"hidden_size": hidden_size,
|
||||
"activations": activations,
|
||||
"activations_alpha": activations_alpha,
|
||||
"activations_beta": activations_beta,
|
||||
"clip": clip,
|
||||
}
|
||||
return _get_node_factory_opset3().create("RNNCell", input_nodes, attributes)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def roi_align(
|
||||
data: NodeInput,
|
||||
rois: NodeInput,
|
||||
batch_indices: NodeInput,
|
||||
pooled_h: int,
|
||||
pooled_w: int,
|
||||
sampling_ratio: int,
|
||||
spatial_scale: float,
|
||||
mode: str,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return a node which performs ROIAlign.
|
||||
|
||||
:param data: Input data.
|
||||
:param rois: RoIs (Regions of Interest) to pool over.
|
||||
:param batch_indices: Tensor with each element denoting the index of
|
||||
the corresponding image in the batch.
|
||||
:param pooled_h: Height of the ROI output feature map.
|
||||
:param pooled_w: Width of the ROI output feature map.
|
||||
:param sampling_ratio: Number of bins over height and width to use to calculate
|
||||
each output feature map element.
|
||||
:param spatial_scale: Multiplicative spatial scale factor to translate ROI coordinates.
|
||||
:param mode: Method to perform pooling to produce output feature map elements.
|
||||
|
||||
:return: The new node which performs ROIAlign
|
||||
"""
|
||||
inputs = as_nodes(data, rois, batch_indices)
|
||||
attributes = {
|
||||
"pooled_h": pooled_h,
|
||||
"pooled_w": pooled_w,
|
||||
"sampling_ratio": sampling_ratio,
|
||||
"spatial_scale": spatial_scale,
|
||||
"mode": mode,
|
||||
}
|
||||
return _get_node_factory_opset3().create("ROIAlign", inputs, attributes)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def scatter_elements_update(
|
||||
data: NodeInput,
|
||||
indices: NodeInput,
|
||||
updates: NodeInput,
|
||||
axis: NodeInput,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return a node which produces a ScatterElementsUpdate operation.
|
||||
|
||||
:param data: The input tensor to be updated.
|
||||
:param indices: The tensor with indexes which will be updated.
|
||||
:param updates: The tensor with update values.
|
||||
:param axis: The axis for scatter.
|
||||
:return: ScatterElementsUpdate node
|
||||
|
||||
ScatterElementsUpdate creates a copy of the first input tensor with updated elements
|
||||
specified with second and third input tensors.
|
||||
|
||||
For each entry in `updates`, the target index in `data` is obtained by combining
|
||||
the corresponding entry in `indices` with the index of the entry itself: the
|
||||
index-value for dimension equal to `axis` is obtained from the value of the
|
||||
corresponding entry in `indices` and the index-value for dimension not equal
|
||||
to `axis` is obtained from the index of the entry itself.
|
||||
|
||||
"""
|
||||
return _get_node_factory_opset3().create(
|
||||
"ScatterElementsUpdate",
|
||||
as_nodes(data, indices, updates, axis),
|
||||
)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def scatter_update(
|
||||
data: Node,
|
||||
indices: NodeInput,
|
||||
updates: NodeInput,
|
||||
axis: NodeInput,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return a node which produces a ScatterUpdate operation.
|
||||
|
||||
ScatterUpdate sets new values to slices from data addressed by indices.
|
||||
|
||||
:param data: The input tensor to be updated.
|
||||
:param indices: The tensor with indexes which will be updated.
|
||||
:param updates: The tensor with update values.
|
||||
:param axis: The axis at which elements will be updated.
|
||||
:return: ScatterUpdate node
|
||||
"""
|
||||
return _get_node_factory_opset3().create(
|
||||
"ScatterUpdate",
|
||||
as_nodes(data, indices, updates, axis),
|
||||
)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def shape_of(data: NodeInput, output_type: str = "i64", name: Optional[str] = None) -> Node:
|
||||
"""Return a node which produces a tensor containing the shape of its input data.
|
||||
|
||||
:param data: The tensor containing the input data.
|
||||
:param output_type: Output element type.
|
||||
:return: ShapeOf node
|
||||
"""
|
||||
return _get_node_factory_opset3().create(
|
||||
"ShapeOf",
|
||||
[as_node(data)],
|
||||
{"output_type": output_type},
|
||||
)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def shuffle_channels(data: Node, axis: int, group: int, name: Optional[str] = None) -> Node:
|
||||
"""Perform permutation on data in the channel dimension of the input tensor.
|
||||
|
||||
:param data: The node with input tensor.
|
||||
:param axis: Channel dimension index in the data tensor.
|
||||
A negative value means that the index should be calculated
|
||||
from the back of the input data shape.
|
||||
:param group: The channel dimension specified by the axis parameter
|
||||
should be split into this number of groups.
|
||||
:param name: Optional output node name.
|
||||
:return: The new node performing a permutation on data in the channel dimension
|
||||
of the input tensor.
|
||||
|
||||
The operation is the equivalent with the following transformation of the input tensor
|
||||
`data` of shape [N, C, H, W]:
|
||||
|
||||
`data_reshaped` = reshape(`data`, [N, group, C / group, H * W])
|
||||
|
||||
`data_trnasposed` = transpose(`data_reshaped`, [0, 2, 1, 3])
|
||||
|
||||
`output` = reshape(`data_trnasposed`, [N, C, H, W])
|
||||
|
||||
For example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
Inputs: tensor of shape [1, 6, 2, 2]
|
||||
|
||||
data = [[[[ 0., 1.], [ 2., 3.]],
|
||||
[[ 4., 5.], [ 6., 7.]],
|
||||
[[ 8., 9.], [10., 11.]],
|
||||
[[12., 13.], [14., 15.]],
|
||||
[[16., 17.], [18., 19.]],
|
||||
[[20., 21.], [22., 23.]]]]
|
||||
|
||||
axis = 1
|
||||
groups = 3
|
||||
|
||||
Output: tensor of shape [1, 6, 2, 2]
|
||||
|
||||
output = [[[[ 0., 1.], [ 2., 3.]],
|
||||
[[ 8., 9.], [10., 11.]],
|
||||
[[16., 17.], [18., 19.]],
|
||||
[[ 4., 5.], [ 6., 7.]],
|
||||
[[12., 13.], [14., 15.]],
|
||||
[[20., 21.], [22., 23.]]]]
|
||||
"""
|
||||
return _get_node_factory_opset3().create(
|
||||
"ShuffleChannels",
|
||||
[as_node(data)],
|
||||
{"axis": axis, "group": group},
|
||||
)
|
||||
|
||||
|
||||
@nameable_op
|
||||
def topk(
|
||||
data: NodeInput,
|
||||
k: NodeInput,
|
||||
axis: int,
|
||||
mode: str,
|
||||
sort: str,
|
||||
index_element_type: str = "i32",
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return a node which performs TopK.
|
||||
|
||||
:param data: Input data.
|
||||
:param k: K.
|
||||
:param axis: TopK Axis.
|
||||
:param mode: Compute TopK largest ('max') or smallest ('min')
|
||||
:param sort: Order of output elements (sort by: 'none', 'index' or 'value')
|
||||
:param index_element_type: Type of output tensor with indices.
|
||||
:return: The new node which performs TopK (both indices and values)
|
||||
"""
|
||||
return _get_node_factory_opset3().create(
|
||||
"TopK",
|
||||
as_nodes(data, k),
|
||||
{"axis": axis, "mode": mode, "sort": sort, "index_element_type": index_element_type},
|
||||
)
|
||||
@ -0,0 +1,144 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from openvino.runtime.opset1.ops import absolute
|
||||
from openvino.runtime.opset1.ops import absolute as abs
|
||||
from openvino.runtime.opset1.ops import acos
|
||||
from openvino.runtime.opset4.ops import acosh
|
||||
from openvino.runtime.opset1.ops import add
|
||||
from openvino.runtime.opset1.ops import asin
|
||||
from openvino.runtime.opset4.ops import asinh
|
||||
from openvino.runtime.opset3.ops import assign
|
||||
from openvino.runtime.opset1.ops import atan
|
||||
from openvino.runtime.opset4.ops import atanh
|
||||
from openvino.runtime.opset1.ops import avg_pool
|
||||
from openvino.runtime.opset1.ops import batch_norm_inference
|
||||
from openvino.runtime.opset2.ops import batch_to_space
|
||||
from openvino.runtime.opset1.ops import binary_convolution
|
||||
from openvino.runtime.opset3.ops import broadcast
|
||||
from openvino.runtime.opset3.ops import bucketize
|
||||
from openvino.runtime.opset1.ops import ceiling
|
||||
from openvino.runtime.opset1.ops import ceiling as ceil
|
||||
from openvino.runtime.opset1.ops import clamp
|
||||
from openvino.runtime.opset1.ops import concat
|
||||
from openvino.runtime.opset1.ops import constant
|
||||
from openvino.runtime.opset1.ops import convert
|
||||
from openvino.runtime.opset1.ops import convert_like
|
||||
from openvino.runtime.opset1.ops import convolution
|
||||
from openvino.runtime.opset1.ops import convolution_backprop_data
|
||||
from openvino.runtime.opset1.ops import cos
|
||||
from openvino.runtime.opset1.ops import cosh
|
||||
from openvino.runtime.opset1.ops import ctc_greedy_decoder
|
||||
from openvino.runtime.opset4.ops import ctc_loss
|
||||
from openvino.runtime.opset3.ops import cum_sum
|
||||
from openvino.runtime.opset3.ops import cum_sum as cumsum
|
||||
from openvino.runtime.opset1.ops import deformable_convolution
|
||||
from openvino.runtime.opset1.ops import deformable_psroi_pooling
|
||||
from openvino.runtime.opset1.ops import depth_to_space
|
||||
from openvino.runtime.opset1.ops import detection_output
|
||||
from openvino.runtime.opset1.ops import divide
|
||||
from openvino.runtime.opset1.ops import elu
|
||||
from openvino.runtime.opset3.ops import embedding_bag_offsets_sum
|
||||
from openvino.runtime.opset3.ops import embedding_bag_packed_sum
|
||||
from openvino.runtime.opset3.ops import embedding_segments_sum
|
||||
from openvino.runtime.opset3.ops import extract_image_patches
|
||||
from openvino.runtime.opset1.ops import equal
|
||||
from openvino.runtime.opset1.ops import erf
|
||||
from openvino.runtime.opset1.ops import exp
|
||||
from openvino.runtime.opset1.ops import fake_quantize
|
||||
from openvino.runtime.opset1.ops import floor
|
||||
from openvino.runtime.opset1.ops import floor_mod
|
||||
from openvino.runtime.opset1.ops import gather
|
||||
from openvino.runtime.opset1.ops import gather_tree
|
||||
from openvino.runtime.opset2.ops import gelu
|
||||
from openvino.runtime.opset1.ops import greater
|
||||
from openvino.runtime.opset1.ops import greater_equal
|
||||
from openvino.runtime.opset1.ops import grn
|
||||
from openvino.runtime.opset1.ops import group_convolution
|
||||
from openvino.runtime.opset1.ops import group_convolution_backprop_data
|
||||
from openvino.runtime.opset3.ops import gru_cell
|
||||
from openvino.runtime.opset1.ops import hard_sigmoid
|
||||
from openvino.runtime.opset4.ops import hswish
|
||||
from openvino.runtime.opset1.ops import interpolate
|
||||
from openvino.runtime.opset1.ops import less
|
||||
from openvino.runtime.opset1.ops import less_equal
|
||||
from openvino.runtime.opset1.ops import log
|
||||
from openvino.runtime.opset1.ops import logical_and
|
||||
from openvino.runtime.opset1.ops import logical_not
|
||||
from openvino.runtime.opset1.ops import logical_or
|
||||
from openvino.runtime.opset1.ops import logical_xor
|
||||
from openvino.runtime.opset1.ops import lrn
|
||||
from openvino.runtime.opset4.ops import lstm_cell
|
||||
from openvino.runtime.opset1.ops import lstm_sequence
|
||||
from openvino.runtime.opset1.ops import matmul
|
||||
from openvino.runtime.opset1.ops import max_pool
|
||||
from openvino.runtime.opset1.ops import maximum
|
||||
from openvino.runtime.opset1.ops import minimum
|
||||
from openvino.runtime.opset4.ops import mish
|
||||
from openvino.runtime.opset1.ops import mod
|
||||
from openvino.runtime.opset1.ops import multiply
|
||||
from openvino.runtime.opset2.ops import mvn
|
||||
from openvino.runtime.opset1.ops import negative
|
||||
from openvino.runtime.opset4.ops import non_max_suppression
|
||||
from openvino.runtime.opset3.ops import non_zero
|
||||
from openvino.runtime.opset1.ops import normalize_l2
|
||||
from openvino.runtime.opset1.ops import not_equal
|
||||
from openvino.runtime.opset1.ops import one_hot
|
||||
from openvino.runtime.opset1.ops import pad
|
||||
from openvino.runtime.opset1.ops import parameter
|
||||
from openvino.runtime.opset1.ops import power
|
||||
from openvino.runtime.opset1.ops import prelu
|
||||
from openvino.runtime.opset1.ops import prior_box
|
||||
from openvino.runtime.opset1.ops import prior_box_clustered
|
||||
from openvino.runtime.opset1.ops import psroi_pooling
|
||||
from openvino.runtime.opset4.ops import proposal
|
||||
from openvino.runtime.opset1.ops import range
|
||||
from openvino.runtime.opset3.ops import read_value
|
||||
from openvino.runtime.opset4.ops import reduce_l1
|
||||
from openvino.runtime.opset4.ops import reduce_l2
|
||||
from openvino.runtime.opset1.ops import reduce_logical_and
|
||||
from openvino.runtime.opset1.ops import reduce_logical_or
|
||||
from openvino.runtime.opset1.ops import reduce_max
|
||||
from openvino.runtime.opset1.ops import reduce_mean
|
||||
from openvino.runtime.opset1.ops import reduce_min
|
||||
from openvino.runtime.opset1.ops import reduce_prod
|
||||
from openvino.runtime.opset1.ops import reduce_sum
|
||||
from openvino.runtime.opset1.ops import region_yolo
|
||||
from openvino.runtime.opset2.ops import reorg_yolo
|
||||
from openvino.runtime.opset1.ops import relu
|
||||
from openvino.runtime.opset1.ops import reshape
|
||||
from openvino.runtime.opset1.ops import result
|
||||
from openvino.runtime.opset1.ops import reverse_sequence
|
||||
from openvino.runtime.opset3.ops import rnn_cell
|
||||
from openvino.runtime.opset3.ops import roi_align
|
||||
from openvino.runtime.opset2.ops import roi_pooling
|
||||
from openvino.runtime.opset3.ops import scatter_elements_update
|
||||
from openvino.runtime.opset3.ops import scatter_update
|
||||
from openvino.runtime.opset1.ops import select
|
||||
from openvino.runtime.opset1.ops import selu
|
||||
from openvino.runtime.opset3.ops import shape_of
|
||||
from openvino.runtime.opset3.ops import shuffle_channels
|
||||
from openvino.runtime.opset1.ops import sigmoid
|
||||
from openvino.runtime.opset1.ops import sign
|
||||
from openvino.runtime.opset1.ops import sin
|
||||
from openvino.runtime.opset1.ops import sinh
|
||||
from openvino.runtime.opset1.ops import softmax
|
||||
from openvino.runtime.opset4.ops import softplus
|
||||
from openvino.runtime.opset2.ops import space_to_batch
|
||||
from openvino.runtime.opset1.ops import space_to_depth
|
||||
from openvino.runtime.opset1.ops import split
|
||||
from openvino.runtime.opset1.ops import sqrt
|
||||
from openvino.runtime.opset1.ops import squared_difference
|
||||
from openvino.runtime.opset1.ops import squeeze
|
||||
from openvino.runtime.opset1.ops import strided_slice
|
||||
from openvino.runtime.opset1.ops import subtract
|
||||
from openvino.runtime.opset4.ops import swish
|
||||
from openvino.runtime.opset1.ops import tan
|
||||
from openvino.runtime.opset1.ops import tanh
|
||||
from openvino.runtime.opset1.ops import tensor_iterator
|
||||
from openvino.runtime.opset1.ops import tile
|
||||
from openvino.runtime.opset3.ops import topk
|
||||
from openvino.runtime.opset1.ops import transpose
|
||||
from openvino.runtime.opset1.ops import unsqueeze
|
||||
from openvino.runtime.opset1.ops import variadic_split
|
||||
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Loading…
Reference in New Issue
Block a user