From b06dc286e974f1fb9892dba4bf8424aa5dfa2154 Mon Sep 17 00:00:00 2001 From: Davide Sapienza Date: Mon, 10 Feb 2020 11:19:39 +0100 Subject: [PATCH] Update README.md Signed-off-by: Davide Sapienza --- README.md | 63 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 63 insertions(+) diff --git a/README.md b/README.md index 3d956a5..12bb124 100644 --- a/README.md +++ b/README.md @@ -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 +``` \ No newline at end of file