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Update README.md

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Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
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Davide Sapienza
2020-02-10 11:19:39 +01:00
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@@ -27,6 +27,36 @@ make
during the cmake configuration it will be dowloaded the weights needed for running
the tests
## DLA34 and ResNet101 weights
To get weights and outputs needed for running the tests you can use the Python
script and the Anaconda environment included in the repository.
Create Anaconda environment and activate it:
```
conda env create -f file_name.yml
source activate env_name
```
Run the Python script inside the environment.
## CenterNet weights
To get the weights needed for running the tests:
* clone the forked repository by the original CenterNet:
```
git clone https://github.com/sapienzadavide/CenterNet.git
```
* follow the instruction in the README.md and INSTALL.md
* copy the weigths and outputs from /path/to/CenterNet/src/ in ./test/centernet-path/ . For example:
```
cp /path/to/CenterNet/src/layers_dla/* ./test/dla34_cnet/layers/
cp /path/to/CenterNet/src/debug_dla/* ./test/dla34_cnet/debug/
```
or
```
cp /path/to/CenterNet/src/layers_resdcn/* ./test/resnet101_cnet/layers/
cp /path/to/CenterNet/src/debug_resdcn/* ./test/resnet101_cnet/debug/
```
## Test
Assumiung you have correctly builded the library these are the test ready to exec:
* test_simple: a simple convolutional and dense network (CUDNN only)
@@ -35,6 +65,11 @@ Assumiung you have correctly builded the library these are the test ready to exe
* test_yolo: YOLO detection network (CUDNN and TENSORRT)
* test_yolo_tiny: smaller version of YOLO (CUDNN and TENSRRT)
* test_yolo3_berkeley: our yolo3 version trained with BDD100K dateset
* test_resnet101: ResNet101 network (CUDNN and TENSORRT)
* test_resnet101_cnet: CenterNet detection based on ResNet101 (CUDNN and TENSORRT)
* test_dla34: DLA34 network (CUDNN and TENSORRT)
* test_dla34_cnet: CenterNet detection based on DLA34 (CUDNN and TENSORRT)
## yolo3 berkeley demo detection
For the live detection you need to precompile the tensorRT file by luncing the desidered network test, this is the recommended process:
@@ -50,3 +85,31 @@ this will genereate a yolo3_berkeley.rt file that can be used for live detection
./yolo3_demo yolo3_berkeley.rt /dev/video0 # launch detection on device 0
```
![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)
## CenterNet (DLA34, ResNet101) demo detection
For the live detection you need to precompile the tensorRT file by luncing the desidered network test, this is the recommended process:
```
export TKDNN_MODE=FP16 # set the half floating point optimization
```
For CenterNet based on ResNet101:
```
rm resnet101_cnet.rt # be sure to delete(or move) old tensorRT files
./test_resnet101_cnet # run the yolo test (is slow)
# with f16 inference the result will be a bit incorrect
```
For CenterNet based on DLA34:
```
rm dla34_cnet.rt # be sure to delete(or move) old tensorRT files
./test_dla34_cnet # run the yolo test (is slow)
# with f16 inference the result will be a bit incorrect
```
this will genereate resnet101_cnet.rt and dla34_cnet.rt file that can be used for live detection:
```
./centernet_demo # launch detection on a demo video
./centernet_demo resnet101_cnet.rt /dev/video0 # launch detection on device 0
./centernet_demo dla34_cnet.rt /dev/video0 # launch detection on device 0
```