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https://github.com/nvidia-ai-iot/torch2trt

An easy to use PyTorch to TensorRT converter
https://github.com/nvidia-ai-iot/torch2trt

classification inference jetson-nano jetson-tx2 jetson-xavier pytorch tensorrt

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An easy to use PyTorch to TensorRT converter

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README 

        
# torch2trt



> What models are you using, or hoping to use, with TensorRT?  Feel free to join the discussion [here](https://github.com/NVIDIA-AI-IOT/torch2trt/discussions/531).

 




torch2trt is a PyTorch to TensorRT converter which utilizes the 

TensorRT Python API.  The converter is



* Easy to use - Convert modules with a single function call ``torch2trt``



* Easy to extend - Write your own layer converter in Python and register it with ``@tensorrt_converter``



If you find an issue, please [let us know](../..//issues)!



> Please note, this converter has limited coverage of TensorRT / PyTorch.  We created it primarily

> to easily optimize the models used in the [JetBot](https://github.com/NVIDIA-AI-IOT/jetbot) project.  If you find the converter helpful with other models, please [let us know](../..//issues).



## Usage



Below are some usage examples, for more check out the [notebooks](notebooks).



### Convert



```python

import torch

from torch2trt import torch2trt

from torchvision.models.alexnet import alexnet



# create some regular pytorch model...

model = alexnet(pretrained=True).eval().cuda()



# create example data

x = torch.ones((1, 3, 224, 224)).cuda()



# convert to TensorRT feeding sample data as input

model_trt = torch2trt(model, [x])

```



### Execute



We can execute the returned ``TRTModule`` just like the original PyTorch model



```python

y = model(x)

y_trt = model_trt(x)



# check the output against PyTorch

print(torch.max(torch.abs(y - y_trt)))

```



### Save and load



We can save the model as a ``state_dict``.



```python

torch.save(model_trt.state_dict(), 'alexnet_trt.pth')

```



We can load the saved model into a ``TRTModule``



```python

from torch2trt import TRTModule



model_trt = TRTModule()



model_trt.load_state_dict(torch.load('alexnet_trt.pth'))

```



## Models



We tested the converter against these models using the [test.sh](test.sh) script.  You can generate the results by calling



```bash

./test.sh TEST_OUTPUT.md

```



> The results below show the throughput in FPS.  You can find the raw output, which includes latency, in the [benchmarks folder](benchmarks).



| Model | Nano (PyTorch) | Nano (TensorRT) | Xavier (PyTorch) | Xavier (TensorRT) |

|-------|:--------------:|:---------------:|:----------------:|:-----------------:|

| alexnet | 46.4 | 69.9 | 250 | 580 |

| squeezenet1_0 | 44 | 137 | 130 | 890 |

| squeezenet1_1 | 76.6 | 248 | 132 | 1390 |

| resnet18 | 29.4 | 90.2 | 140 | 712 |

| resnet34 | 15.5 | 50.7 | 79.2 | 393 |

| resnet50 | 12.4 | 34.2 | 55.5 | 312 |

| resnet101 | 7.18 | 19.9 | 28.5 | 170 |

| resnet152 | 4.96 | 14.1 | 18.9 | 121 |

| densenet121 | 11.5 | 41.9 | 23.0 | 168 |

| densenet169 | 8.25 | 33.2 | 16.3 | 118 |

| densenet201 | 6.84 | 25.4 | 13.3 | 90.9 |

| densenet161 | 4.71 | 15.6 | 17.2 | 82.4 |

| vgg11 | 8.9 | 18.3 | 85.2 | 201 |

| vgg13 | 6.53 | 14.7 | 71.9 | 166 |

| vgg16 | 5.09 | 11.9 | 61.7 | 139 |

| vgg19 |  |  | 54.1 | 121 |

| vgg11_bn | 8.74 | 18.4 | 81.8 | 201 |

| vgg13_bn | 6.31 | 14.8 | 68.0 | 166 |

| vgg16_bn | 4.96 | 12.0 | 58.5 | 140 |

| vgg19_bn |  |  | 51.4 | 121 |



## Setup



> Note: torch2trt depends on the TensorRT Python API.  On Jetson, this is included with the latest JetPack.  For desktop, please follow the [TensorRT Installation Guide](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html).  You may also try installing torch2trt inside one of the NGC PyTorch docker containers for [Desktop](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) or [Jetson](https://ngc.nvidia.com/catalog/containers/nvidia:l4t-pytorch).



### Step 1 - Install the torch2trt Python library



To install the torch2trt Python library, call the following



```bash

git clone https://github.com/NVIDIA-AI-IOT/torch2trt

cd torch2trt

python setup.py install

```



### Step 2 (optional) - Install the torch2trt plugins library



To install the torch2trt plugins library, call the following



```bash

cmake -B build . && cmake --build build --target install && ldconfig

```



This includes support for some layers which may not be supported natively by TensorRT.  Once this library is found in the system, the associated layer converters in torch2trt are implicitly enabled.



> Note: torch2trt now maintains plugins as an independent library compiled with CMake.  This makes compiled TensorRT engines more portable.  If needed, the deprecated plugins (which depend on PyTorch) may still be installed by calling ``python setup.py install --plugins``.



### Step 3 (optional) - Install experimental community contributed features



To install torch2trt with experimental community contributed features under ``torch2trt.contrib``, like Quantization Aware Training (QAT)(`requires TensorRT>=7.0`), call the following,      



```bash

git clone https://github.com/NVIDIA-AI-IOT/torch2trt

cd torch2trt/scripts    

bash build_contrib.sh   

```

  

This enables you to run the QAT example located [here](examples/contrib/quantization_aware_training).   

    



## How does it work?



This converter works by attaching conversion functions (like ``convert_ReLU``) to the original 

PyTorch functional calls (like ``torch.nn.ReLU.forward``).  The sample input data is passed

through the network, just as before, except now whenever a registered function (``torch.nn.ReLU.forward``)

is encountered, the corresponding converter (``convert_ReLU``) is also called afterwards.  The converter

is passed the arguments and return statement of the original PyTorch function, as well as the TensorRT

network that is being constructed.  The input tensors to the original PyTorch function are modified to

have an attribute ``_trt``, which is the TensorRT counterpart to the PyTorch tensor.  The conversion function

uses this ``_trt`` to add layers to the TensorRT network, and then sets the ``_trt`` attribute for

relevant output tensors.  Once the model is fully executed, the final tensors returns are marked as outputs

of the TensorRT network, and the optimized TensorRT engine is built.



## How to add (or override) a converter



Here we show how to add a converter for the ``ReLU`` module using the TensorRT

python API.



```python

import tensorrt as trt

from torch2trt import tensorrt_converter



@tensorrt_converter('torch.nn.ReLU.forward')

def convert_ReLU(ctx):

    input = ctx.method_args[1]

    output = ctx.method_return

    layer = ctx.network.add_activation(input=input._trt, type=trt.ActivationType.RELU)  

    output._trt = layer.get_output(0)

```



The converter takes one argument, a ``ConversionContext``, which will contain

the following



* ``ctx.network`` - The TensorRT network that is being constructed.



* ``ctx.method_args`` - Positional arguments that were passed to the specified PyTorch function.  The ``_trt`` attribute is set for relevant input tensors.

* ``ctx.method_kwargs`` - Keyword arguments that were passed to the specified PyTorch function.

* ``ctx.method_return`` - The value returned by the specified PyTorch function.  The converter must set the ``_trt`` attribute where relevant.



Please see [this folder](torch2trt/converters) for more examples.



## See also



- [JetBot](http://github.com/NVIDIA-AI-IOT/jetbot) - An educational AI robot based on NVIDIA Jetson Nano



- [JetRacer](http://github.com/NVIDIA-AI-IOT/jetracer) - An educational AI racecar using NVIDIA Jetson Nano

- [JetCam](http://github.com/NVIDIA-AI-IOT/jetcam) - An easy to use Python camera interface for NVIDIA Jetson

- [JetCard](http://github.com/NVIDIA-AI-IOT/jetcard) - An SD card image for web programming AI projects with NVIDIA Jetson Nano