# 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`