Use TensorRT API to implement Caffe-SSD, SSD(channel pruning), Mobilenet-SSD

#Introduction:

1. The original Caffe-SSD can run 3-5fps on my jetson tx2.
2. TensorRT-SSD can run 8-10fps on my jetson tx2.
3. TensorRT-SSD(channel pruning) can run 16-17fps on my jetson tx2.
4. TensorRT-Mobilenet-SSD can run 40-43fps on my jetson tx2(it‘s cool!), and run 100+fps on gtx1060.

#Requirements:

1. TensorRT3.0
2. Cuda8.0 or Cuda9.0
3. OpenCV

The code will be published shortly...

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In the Other_layer_tensorRT folder, there are the implementation of some other layers with TensorRT api, including:

1. PReLU

Continuously updated...

1. 2018年02月06日, update detection_out layer

2. 2018年03月07日, add the common.cpp file

3. 2018年04月21日, TensorFlow 1.7 wheel with JetPack 3.2.(enable TensorRT support)

   Python2.7:https://nvidia.app.box.com/v/TF170-py27-wTRT

   Python3.5:https://nvidia.app.box.com/v/TF170-py35-wTRT

4. 2018年05月07日, TensorRT parse two(many) models, see sample_parse_two_models.txt

5. 2018年05月30日, add MobileNet-SSD_iplugin.prototxt (21 classes)

6. 2018年07月19日, fix the error of Concat layer in pluginIplement.cpp