TensorRT Python Sample for Object Detection
Performance includes memcpy and inference.
| Model | Input Size | TRT Nano |
|---|---|---|
| ssd_inception_v2_coco(2017) | 300x300 | 49ms |
| ssd_mobilenet_v1_coco | 300x300 | 36ms |
| ssd_mobilenet_v2_coco | 300x300 | 46ms |
Since the optimization of preprocessing is not ready yet, we don't include image read/write time here.
Install dependencies
$ sudo apt-get install python3-pip libhdf5-serial-dev hdf5-tools
$ pip3 install --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v42 tensorflow-gpu==1.13.1+nv19.5 --user
$ pip3 install numpy pycuda --userDownload model
Please download the object detection model from TensorFlow model zoo.
$ git clone https://github.com/AastaNV/TRT_object_detection.git
$ cd TRT_object_detection
$ mkdir model
$ cp [model].tar.gz model/
$ tar zxvf model/[model].tar.gz -C model/Supported models:
- ssd_inception_v2_coco_2017_11_17
- ssd_mobilenet_v1_coco
- ssd_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_enumRUN
1. Maximize the Nano performance
$ sudo nvpmodel -m 0
$ sudo jetson_clocks2. 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 model3. Execute
$ python3 main.py [image]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.