TensorRT-pytorch2TRT转换工程-优质实用项目.zip

# torch2trt 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.