TensorRT for Tensorflow Object Detection

Install dependencies

$ sudo apt-get install python3-pip libhdf5-serial-dev hdf5-tools $ pip3 install numpy pycuda --user

Supported models:

• ssd_inception_v2_coco_2017_11_17
• ssd_mobilenet_v1_coco
• ssd_mobilenet_v2_coco #On Jetson TX2:Inference time:18ms.
• ssdlite_mobilenet_v2_coco

We will keep adding new model into our supported list.

Update graphsurgeon converter

Edit /usr/lib/python3.6/dist-packages/graphsurgeon/node_manipulation.py

diff --git a/node_manipulation.py b/node_manipulation.py
index d2d012a..1ef30a0 100644
--- a/node_manipulation.py
+++ b/node_manipulation.py
@@ -30,6 +30,7 @@ def create_node(name, op=None, _do_suffix=False, **kwargs):
     node = NodeDef()
     node.name = name
     node.op = op if op else name
+    node.attr["dtype"].type = 1
     for key, val in kwargs.items():
         if key == "dtype":
             node.attr["dtype"].type = val.as_datatype_enum

## RUN
**1. Maximize the TX2 performance**

$ sudo nvpmodel -m 0 $ sudo jetson_clocks

**2. Update main.py based on the model you used**

from config import model_ssd_inception_v2_coco_2017_11_17 as model from config import model_ssd_mobilenet_v1_coco_2018_01_28 as model from config import model_ssd_mobilenet_v2_coco_2018_03_29 as model from config import model_ssdlite_mobilenet_v2_coco_2018_05_09 as model

**3. Execute**

$ python3 main.py [image]

Notice:
It takes some time to compile a TensorRT model when the first launching.
After that, TensorRT engine can be created directly with the serialized .bin file
To get more memory, it's recommended to turn-off X-server.