# pnnx
python wrapper of pnnx, only support python 3.7+ now.
Install from pip
==================
pnnx is available as wheel packages for macOS, Windows and Linux distributions, you can install with pip:
```
pip install pnnx
```
# Build & Install from source
## Prerequisites
**On Unix (Linux, OS X)**
* A compiler with C++14 support
* CMake >= 3.4
**On Mac**
* A compiler with C++14 support
* CMake >= 3.4
**On Windows**
* Visual Studio 2015 or higher
* CMake >= 3.4
## Build & install
1. clone ncnn.
```bash
git clone https://github.com/Tencent/ncnn.git
```
2. install pytorch
install pytorch according to https://pytorch.org/ . Anaconda is strongly recommended for example:
```bash
conda install pytorch
```
3. install
```bash
cd /pathto/ncnntools/pnnx/python
python setup.py install
```
> **Note:**
> If torchvision and pnnx2onnx are needed, you can set the following environment variables before 'python setup.py install' to enable them. e.g. on ubuntu:
>
> ```
> export TORCHVISION_INSTALL_DIR="/project/torchvision"
> export PROTOBUF_INCLUDE_DIR="/project/protobuf/include"
> export PROTOBUF_LIBRARIES="/project/protobuf/lib64/libprotobuf.a"
> export PROTOBUF_PROTOC_EXECUTABLE="/project/protobuf/bin/protoc"
> ```
>
> To do these, you must install Torchvision and Protobuf first.
## Tests
```bash
cd /pathto/ncnn/tools/pnnx/python
pytest tests
```
## Usage
1. export model to pnnx
```python
import torch
import torchvision.models as models
import pnnx
net = models.resnet18(pretrained=True)
x = torch.rand(1, 3, 224, 224)
# You could try disabling checking when torch tracing raises error
# opt_net = pnnx.export(net, "resnet18.pt", x, check_trace=False)
opt_net = pnnx.export(net, "resnet18.pt", x)
```
2. convert existing model to pnnx
```python
import torch
import pnnx
x = torch.rand(1, 3, 224, 224)
opt_net = pnnx.convert("resnet18.pt", x)
```
## API Reference
1. pnnx.export
`model` (torch.nn.Model): model to be exported.
`ptpath` (str): the torchscript name.
`inputs` (torch.Tensor of list of torch.Tensor) expected inputs of the model.
`inputs2` (torch.Tensor of list of torch.Tensor) alternative inputs of the model. Usually, it is used with input_shapes to resolve dynamic shape.
`input_shapes` (Optional, list of int or list of list with int type inside) shapes of model inputs.
It is used to resolve tensor shapes in model graph. for example, [1,3,224,224] for the model with only
1 input, [[1,3,224,224],[1,3,224,224]] for the model that have 2 inputs.
`input_types` (Optional, str or list of str) types of model inputs, it should have the same length with `input_shapes`.
for example, "f32" for the model with only 1 input, ["f32", "f32"] for the model that have 2 inputs.
| typename | torch type |
|:--------:|:--------------------------------|
| f32 | torch.float32 or torch.float |
| f64 | torch.float64 or torch.double |
| f16 | torch.float16 or torch.half |
| u8 | torch.uint8 |
| i8 | torch.int8 |
| i16 | torch.int16 or torch.short |
| i32 | torch.int32 or torch.int |
| i64 | torch.int64 or torch.long |
| c32 | torch.complex32 |
| c64 | torch.complex64 |
| c128 | torch.complex128 |
`input_shapes2` (Optional, list of int or list of list with int type inside) shapes of alternative model inputs,
the format is identical to `input_shapes`. Usually, it is used with input_shapes to resolve dynamic shape (-1)
in model graph.
`input_types2` (Optional, str or list of str) types of alternative model inputs.
`device` (Optional, str, default="cpu") device type for the input in TorchScript model, cpu or gpu.
`customop` (Optional, str or list of str) list of Torch extensions (dynamic library) for custom operators.
For example, "/home/nihui/.cache/torch_extensions/fused/fused.so" or
["/home/nihui/.cache/torch_extensions/fused/fused.so",...].
`moduleop` (Optional, str or list of str) list of modules to keep as one big operator.
for example, "models.common.Focus" or ["models.common.Focus","models.yolo.Detect"].
`optlevel` (Optional, int, default=2) graph optimization level
| option | optimization level |
|:--------:|:----------------------------------|
| 0 | do not apply optimization |
| 1 | do not apply optimization |
| 2 | optimization more for inference |
`pnnxparam` (Optional, str, default="*.pnnx.param", * is the model name): PNNX graph definition file.
`pnnxbin` (Optional, str, default="*.pnnx.bin"): PNNX model weight.
`pnnxpy` (Optional, str, default="*_pnnx.py"): PyTorch script for inference, including model construction
and weight initialization code.
`pnnxonnx` (Optional, str, default="*.pnnx.onnx"): PNNX model in onnx format.
`ncnnparam` (Optional, str, default="*.ncnn.param"): ncnn graph definition.
`ncnnbin` (Optional, str, default="*.ncnn.bin"): ncnn model weight.
`ncnnpy` (Optional, str, default="*_ncnn.py"): pyncnn script for inference.
2. pnnx.convert
`ptpath` (str): torchscript model to be converted.
Other parameters are consistent with `pnnx.export`
Raw data
{
"_id": null,
"home_page": "https://github.com/Tencent/ncnn/tree/master/tools/pnnx",
"name": "pnnx",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": null,
"author": "nihui",
"author_email": "nihuini@tencent.com",
"download_url": null,
"platform": null,
"description": "# pnnx\npython wrapper of pnnx, only support python 3.7+ now.\n\nInstall from pip\n==================\n\npnnx is available as wheel packages for macOS, Windows and Linux distributions, you can install with pip:\n\n```\npip install pnnx\n```\n\n# Build & Install from source\n\n## Prerequisites\n\n**On Unix (Linux, OS X)**\n\n* A compiler with C++14 support\n* CMake >= 3.4\n\n**On Mac**\n\n* A compiler with C++14 support\n* CMake >= 3.4\n\n**On Windows**\n\n* Visual Studio 2015 or higher\n* CMake >= 3.4\n\n## Build & install\n1. clone ncnn.\n```bash\ngit clone https://github.com/Tencent/ncnn.git\n```\n2. install pytorch \n\ninstall pytorch according to https://pytorch.org/ . Anaconda is strongly recommended for example:\n```bash\nconda install pytorch\n```\n3. install\n```bash\ncd /pathto/ncnntools/pnnx/python\npython setup.py install\n```\n\n> **Note:**\n> If torchvision and pnnx2onnx are needed, you can set the following environment variables before 'python setup.py install' to enable them. e.g. on ubuntu:\n>\n> ```\n> export TORCHVISION_INSTALL_DIR=\"/project/torchvision\"\n> export PROTOBUF_INCLUDE_DIR=\"/project/protobuf/include\"\n> export PROTOBUF_LIBRARIES=\"/project/protobuf/lib64/libprotobuf.a\"\n> export PROTOBUF_PROTOC_EXECUTABLE=\"/project/protobuf/bin/protoc\" \n> ```\n>\n> To do these, you must install Torchvision and Protobuf first.\n\n\n## Tests\n```bash\ncd /pathto/ncnn/tools/pnnx/python\npytest tests\n```\n\n## Usage\n1. export model to pnnx\n```python\nimport torch\nimport torchvision.models as models\nimport pnnx\n\nnet = models.resnet18(pretrained=True)\nx = torch.rand(1, 3, 224, 224)\n\n# You could try disabling checking when torch tracing raises error\n# opt_net = pnnx.export(net, \"resnet18.pt\", x, check_trace=False)\nopt_net = pnnx.export(net, \"resnet18.pt\", x)\n```\n\n2. convert existing model to pnnx\n```python\nimport torch\nimport pnnx\n\nx = torch.rand(1, 3, 224, 224)\nopt_net = pnnx.convert(\"resnet18.pt\", x)\n```\n\n## API Reference\n1. pnnx.export\n\n`model` (torch.nn.Model): model to be exported.\n\n`ptpath` (str): the torchscript name.\n\n`inputs` (torch.Tensor of list of torch.Tensor) expected inputs of the model.\n\n`inputs2` (torch.Tensor of list of torch.Tensor) alternative inputs of the model. Usually, it is used with input_shapes to resolve dynamic shape.\n\n`input_shapes` (Optional, list of int or list of list with int type inside) shapes of model inputs.\nIt is used to resolve tensor shapes in model graph. for example, [1,3,224,224] for the model with only \n1 input, [[1,3,224,224],[1,3,224,224]] for the model that have 2 inputs. \n\n`input_types` (Optional, str or list of str) types of model inputs, it should have the same length with `input_shapes`.\nfor example, \"f32\" for the model with only 1 input, [\"f32\", \"f32\"] for the model that have 2 inputs.\n\n| typename | torch type |\n|:--------:|:--------------------------------|\n| f32 | torch.float32 or torch.float |\n| f64 | torch.float64 or torch.double |\n| f16 | torch.float16 or torch.half |\n| u8 | torch.uint8 |\n| i8 | torch.int8 |\n| i16 | torch.int16 or torch.short |\n| i32 | torch.int32 or torch.int |\n| i64 | torch.int64 or torch.long |\n| c32 | torch.complex32 |\n| c64 | torch.complex64 |\n| c128 | torch.complex128 |\n\n`input_shapes2` (Optional, list of int or list of list with int type inside) shapes of alternative model inputs,\nthe format is identical to `input_shapes`. Usually, it is used with input_shapes to resolve dynamic shape (-1)\nin model graph.\n\n`input_types2` (Optional, str or list of str) types of alternative model inputs.\n\n`device` (Optional, str, default=\"cpu\") device type for the input in TorchScript model, cpu or gpu.\n\n`customop` (Optional, str or list of str) list of Torch extensions (dynamic library) for custom operators.\nFor example, \"/home/nihui/.cache/torch_extensions/fused/fused.so\" or \n[\"/home/nihui/.cache/torch_extensions/fused/fused.so\",...].\n\n`moduleop` (Optional, str or list of str) list of modules to keep as one big operator.\nfor example, \"models.common.Focus\" or [\"models.common.Focus\",\"models.yolo.Detect\"].\n\n`optlevel` (Optional, int, default=2) graph optimization level\n\n| option | optimization level |\n|:--------:|:----------------------------------|\n| 0 | do not apply optimization |\n| 1 | do not apply optimization |\n| 2 | optimization more for inference |\n\n`pnnxparam` (Optional, str, default=\"*.pnnx.param\", * is the model name): PNNX graph definition file.\n\n`pnnxbin` (Optional, str, default=\"*.pnnx.bin\"): PNNX model weight.\n\n`pnnxpy` (Optional, str, default=\"*_pnnx.py\"): PyTorch script for inference, including model construction \nand weight initialization code.\n\n`pnnxonnx` (Optional, str, default=\"*.pnnx.onnx\"): PNNX model in onnx format.\n\n`ncnnparam` (Optional, str, default=\"*.ncnn.param\"): ncnn graph definition.\n\n`ncnnbin` (Optional, str, default=\"*.ncnn.bin\"): ncnn model weight.\n\n`ncnnpy` (Optional, str, default=\"*_ncnn.py\"): pyncnn script for inference.\n\n2. pnnx.convert\n\n`ptpath` (str): torchscript model to be converted.\n\nOther parameters are consistent with `pnnx.export`\n",
"bugtrack_url": null,
"license": "BSD-3",
"summary": "pnnx is an open standard for PyTorch model interoperability.",
"version": "20241223",
"project_urls": {
"Homepage": "https://github.com/Tencent/ncnn/tree/master/tools/pnnx"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "218ecd22212cc28621287308ea3746e2a4ba3947845a0c25a78569015be4dead",
"md5": "4201aff37234dfa63d7625edec1efdc7",
"sha256": "0077f23d69052e32a0579a204e507171d981902432b0167eafc0322bc1802066"
},
"downloads": -1,
"filename": "pnnx-20241223-py3-none-macosx_10_9_universal2.macosx_10_9_x86_64.macosx_11_0_arm64.whl",
"has_sig": false,
"md5_digest": "4201aff37234dfa63d7625edec1efdc7",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 47211191,
"upload_time": "2024-12-23T04:39:54",
"upload_time_iso_8601": "2024-12-23T04:39:54.551659Z",
"url": "https://files.pythonhosted.org/packages/21/8e/cd22212cc28621287308ea3746e2a4ba3947845a0c25a78569015be4dead/pnnx-20241223-py3-none-macosx_10_9_universal2.macosx_10_9_x86_64.macosx_11_0_arm64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "66e9dfc1234c64bba5fb19b2e56a48e63051a385e5284fe9f02fd1c741a23be3",
"md5": "c308d34a8ae0b0f6f81b16fa4bca6280",
"sha256": "2614e36704f92598ba9c675ecdbb78ebb3546ef56aa5d97b3c913c238ed2ecf7"
},
"downloads": -1,
"filename": "pnnx-20241223-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl",
"has_sig": false,
"md5_digest": "c308d34a8ae0b0f6f81b16fa4bca6280",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 21037828,
"upload_time": "2024-12-23T04:40:02",
"upload_time_iso_8601": "2024-12-23T04:40:02.546620Z",
"url": "https://files.pythonhosted.org/packages/66/e9/dfc1234c64bba5fb19b2e56a48e63051a385e5284fe9f02fd1c741a23be3/pnnx-20241223-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "cb725697a79dd51863ba8d831ad9a64e614b433dbe77ad525ec7705384633e86",
"md5": "73343055517bd33931467366f803abbb",
"sha256": "729ab9c0a30d2c059808452e086ce8edd8141244f7133ab8bd6eb3d9f2521d6b"
},
"downloads": -1,
"filename": "pnnx-20241223-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl",
"has_sig": false,
"md5_digest": "73343055517bd33931467366f803abbb",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 23493708,
"upload_time": "2024-12-23T04:40:08",
"upload_time_iso_8601": "2024-12-23T04:40:08.261685Z",
"url": "https://files.pythonhosted.org/packages/cb/72/5697a79dd51863ba8d831ad9a64e614b433dbe77ad525ec7705384633e86/pnnx-20241223-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "fd6d15998476e15bb13ee211c1d9f4b06edc60dd25f0a8e69265aa227d419c3f",
"md5": "dae680ad6b428d844b52261a2984573b",
"sha256": "38b34ac4fb0cb4159417a6d7849015ef3b1e660a3e8f64f9638c5097425b43dd"
},
"downloads": -1,
"filename": "pnnx-20241223-py3-none-win_amd64.whl",
"has_sig": false,
"md5_digest": "dae680ad6b428d844b52261a2984573b",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 17184146,
"upload_time": "2024-12-23T04:40:13",
"upload_time_iso_8601": "2024-12-23T04:40:13.041705Z",
"url": "https://files.pythonhosted.org/packages/fd/6d/15998476e15bb13ee211c1d9f4b06edc60dd25f0a8e69265aa227d419c3f/pnnx-20241223-py3-none-win_amd64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-23 04:39:54",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "Tencent",
"github_project": "ncnn",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "pnnx"
}
JSON
