ncnn/tools/pnnx/python

• pnnx
• Install from pip
• Build & Install from source
• • Prerequisites
  • Build & install
  • Tests
  • Usage
  • API Reference

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.

git clone https://github.com/Tencent/ncnn.git

2. install pytorch

install pytorch according to https://pytorch.org/ . Anaconda is strongly recommended for example:

conda install pytorch

3. install

cd /pathto/ncnntools/pnnx
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

cd /pathto/ncnn/tools/pnnx
pytest python/tests/

Usage

1. export model to pnnx

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

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