If you are looking for another CenterNet, try this!
This repository is a simple pytorch implementation of Objects as Points, some of the code is taken from the official implementation.
As the name says, this version is simple and easy to read, all the complicated parts (dataloader, hourglass, training loop, etc) are all rewrote in a simpler way.
By the way the support of nn.parallel.DistributedDataParallel is also added, so this implementation trains significantly faster than the official code (~ 75 img/s vs ~36 img/s on 8 GPUs).
Enjoy!
Disable cudnn batch normalization.
Open torch/nn/functional.py
and find the line with torch.batch_norm
and replace the torch.backends.cudnn.enabled
with False
.
Clone this repo:
CenterNet_ROOT=/path/to/clone/CenterNet
git clone https://github.com/zzzxxxttt/pytorch_simple_CenterNet_45 $CenterNet_ROOT
Install COCOAPI (the cocoapi in this repo is modified to work with python3):
cd $CenterNet_ROOT/lib/cocoapi/PythonAPI
make
python setup.py install --user
Compile deformable convolutional (from DCNv2).
If you are using pytorch 0.4.1, rename $CenterNet_ROOT/lib/DCNv2_old
to $CenterNet_ROOT/lib/DCNv2
, otherwise rename $CenterNet_ROOT/lib/DCNv2_new
to $CenterNet_ROOT/lib/DCNv2
.
cd $CenterNet_ROOT/lib/DCNv2
./make.sh
Compile NMS.
cd $CenterNet_ROOT/lib/nms
make
For COCO training, Download COCO dataset and put annotations
, train2017
, val2017
, test2017
(or create symlinks) into $CenterNet_ROOT/data/coco
For Pascal VOC training, download VOC0712 in coco format (password: 4iu2) and put annotations
, images
, VOCdevkit
(or create symlinks) into $CenterNet_ROOT/data/voc
To train Hourglass-104, download CornerNet pretrained weights (password: y1z4) and put checkpoint.t7
into $CenterNet_ROOT/ckpt/pretrain
.
python train.py --log_name coco_hg_512_dp \
--dataset coco \
--arch large_hourglass \
--lr 5e-4 \
--lr_step 90,120 \
--batch_size 48 \
--num_epochs 140 \
--num_workers 10
python -m torch.distributed.launch --nproc_per_node NUM_GPUS train.py --dist \
--log_name coco_hg_512_ddp \
--dataset coco \
--arch large_hourglass \
--lr 5e-4 \
--lr_step 90,120 \
--batch_size 48 \
--num_epochs 140 \
--num_workers 2
python train.py --log_name pascal_resdcn18_384_dp \
--dataset pascal \
--arch resdcn_18 \
--img_size 384 \
--lr 1.25e-4 \
--lr_step 45,60 \
--batch_size 32 \
--num_epochs 70 \
--num_workers 10
python -m torch.distributed.launch --nproc_per_node NUM_GPUS train.py --dist \
--log_name pascal_resdcn18_384_ddp \
--dataset pascal \
--arch resdcn_18 \
--img_size 384 \
--lr 1.25e-4 \
--lr_step 45,60 \
--batch_size 32 \
--num_epochs 70 \
--num_workers 2
python test.py --log_name coco_hg_512_dp \
--dataset coco \
--arch large_hourglass
# flip test
python test.py --log_name coco_hg_512_dp \
--dataset coco \
--arch large_hourglass \
--test_flip
# multi scale test
python test.py --log_name coco_hg_512_dp \
--dataset coco \
--arch large_hourglass \
--test_flip \
--test_scales 0.5,0.75,1,1.25,1.5
python test.py --log_name pascal_resdcn18_384_dp \
--dataset pascal \
--arch resdcn_18 \
--img_size 384
# flip test
python test.py --log_name pascal_resdcn18_384_dp \
--dataset pascal \
--arch resdcn_18 \
--img_size 384 \
--test_flip
Model | Training image size | mAP |
---|---|---|
Hourglass-104 (DP) | 512 | 39.9/42.3/45.0 |
Hourglass-104 (DDP) | 512 | 40.5/42.6/45.3 |
Model | Training image size | mAP | model |
---|---|---|---|
ResDCN-18 (DDP) | 384 | 71.19/72.99 | password: 83rv |
ResDCN-18 (DDP) | 512 | 72.76/75.69 | password: s8d5 |
python demo.py --img_dir ./demo.jpg \
--ckpt_dir ./ckpt/pascal_resdcn18_512/checkpoint.t7 \
--dataset pascal \
--arch resdcn_18 \
--img_size 512 \
Demo results: