transfer from gitlab

Author: wang-xinyu

.gitignore

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+*/build
+*/*.wts

README.md

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+# TensorRTx
+
+TensorRTx aims to implement popular deep learning networks with tensorrt API. As we know, tensorrt has builtin parsers, including caffeparser, uffparser, onnxparser, etc. But when we use these parses, we often run into some "unsupported operations or layers" problems, especially some state-of-the-art models are using new type of layers, therefore, sometimes we have no choice but to implement the models with tensorrt network definition APIs.
+
+I wrote this project to get familiar with tensorrt API, and also to share and learn from the community.
+
+TensorRTx has a brother project [Pytorchx](https://github.com/wang-xinyu/pytorchx). All the models are implemented in pytorch first, and export a weights file xxx.wts, and then use tensorrt to load weights, define network and do inference.
+
+## Test Environment
+
+Jetson TX1
+
+Ubuntu16.04
+
+cuda9.0
+
+cudnn7.1.5
+
+tensorrt4.0.2/nvinfer4.1.3
+
+Currently, I only test it on TX1, But I think it will be totally OK on TX2 with same software version. And also it will be easy be ported to x86.
+
+## Models
+
+Following models are implemented, each one also has a readme inside.
+
+|Name | Description |
+|-|-|
+|[lenet](./lenet) | the simplest, as a "hello world" of this project |
+|[alexnet](./alexnet)| easy to implement, all layers are supported in tensorrt |
+|[googlenet](./googlenet)| GoogLeNet (Inception v1) |
+|[inception](./inception)| Inception v3 |
+|[mnasnet](./mnasnet)| MNASNet with depth multiplier of 0.5 from the paper |
+|[mobilenet](./mobilenet)| MobileNet V2 |
+|[resnet](./resnet)| resnet-18 and resnet-50 are implemented |
+|[shufflenet](./shufflenet)| ShuffleNetV2 with 0.5x output channels |
+|[squeezenet](./squeezenet)| SqueezeNet 1.1 model |
+|[vgg](./vgg)| VGG 11-layer model |
+|[yolov3](./yolov3)| darknet-53, weights from yolov3 authors |
+
+## Tricky Operations
+
+Some tricky operations encountered in these models, already solved, but might have better solutions.
+
+|Name | Description |
+|-|-|
+|BatchNorm| Implement by a scale layer, used in resnet, googlenet, mobilenet, etc. |
+|MaxPool2d(ceil_mode=True)| use a padding layer before maxpool to solve ceil_mode=True, see googlenet. |
+|average pool with padding| use setAverageCountExcludesPadding() when necessary, see inception. |
+|relu6| use `Relu6(x) = Relu(x) - Relu(x-6)`, see mobilenet. |
+|torch.chunk()| implement the 'chunk(2, dim=C)' by tensorrt plugin, see shufflenet. |
+|channel shuffle| use two shuffle layers to implement `channel_shuffle`, see shufflenet. |
+|adaptive pool| use fixed input dimension, and use regular average pooling, see shufflenet. |
+|leaky relu| I wrote a leaky relu plugin, but PRelu in `NvInferPlugin.h` can be used, see yolov3. |
+|yolo layer| yolo layer is implemented as a plugin, see yolov3. |
+|upsample| replaced by a deconvolution layer, see yolov3. |
+

alexnet/CMakeLists.txt

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+cmake_minimum_required(VERSION 2.6)
+
+project(alexnet)
+
+add_definitions(-std=c++11)
+
+option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
+set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_BUILD_TYPE Debug)
+
+include_directories(${PROJECT_SOURCE_DIR}/include)
+include_directories(/usr/local/cuda-9.0/targets/aarch64-linux/include)
+link_directories(/usr/local/cuda-9.0/targets/aarch64-linux/lib)
+
+add_executable(alexnet ${PROJECT_SOURCE_DIR}/alex.cpp)
+target_link_libraries(alexnet nvinfer)
+target_link_libraries(alexnet cudart)
+
+add_definitions(-O2 -pthread)
+

alexnet/README.md

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+# alexnet
+
+AlexNet model architecture from the "One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
+
+For the details, you can refer to [pytorchx/alexnet](https://github.com/wang-xinyu/pytorchx/tree/master/alexnet)
+
+This alexnet is just several `conv-relu-pool` blocks followed by several `fc-relu`, nothing special. All layers can be implemented by tensorrt api, including `addConvolution`, `addActivation`, `addPooling`, `addFullyConnected`.
+
+```
+// 1. generate alexnet.wts from [pytorchx/alexnet](https://github.com/wang-xinyu/pytorchx/tree/master/alexnet)
+
+// 2. put alexnet.wts into tensorrtx/alexnet
+
+// 3. build and run
+
+cd tensorrtx/alexnet
+
+mkdir build
+
+cd build
+
+cmake ..
+
+make
+
+sudo ./alexnet -s   // serialize model to plan file i.e. 'alexnet.engine'
+
+sudo ./alexnet -d   // deserialize plan file and run inference
+
+// 4. see if the output is same as pytorchx/alexnet
+```
+
+