TensorRTx aims to implement popular deep learning networks with tensorrt network definition APIs. As we know, tensorrt has builtin parsers, including caffeparser, uffparser, onnxparser, etc. But when we use these parsers, 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. 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.
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.
Following models are implemented, each one also has a readme inside.
| Name | Description |
|---|---|
| lenet | the simplest, as a "hello world" of this project |
| alexnet | easy to implement, all layers are supported in tensorrt |
| googlenet | GoogLeNet (Inception v1) |
| inception | Inception v3 |
| mnasnet | MNASNet with depth multiplier of 0.5 from the paper |
| mobilenet | MobileNet V2 |
| resnet | resnet-18 and resnet-50 are implemented |
| shufflenet | ShuffleNetV2 with 0.5x output channels |
| squeezenet | SqueezeNet 1.1 model |
| vgg | VGG 11-layer model |
| yolov3 | darknet-53, weights from yolov3 authors |
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. |