Single-Stage-Multi-person-Pose-Machines

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Unofficial implementation of arxiv paper "Single-Stage Multi-Person Pose Machines", detail imformation can see this paper or check this csdn link only for reference.

TODO

• [x] ~~custom distribute training is not work well, I trained for 10 epochs and nothing can be learned at all. So if anyone is familiar with this, please help me to check it and make it work.~~ The custom distribute training is right, but I write checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model) is different with in spm_model.py. Because I writecheckpoint = tf.train.Checkpoint(optimizer=optimizer, net=model) in spm_model.py, the parameter in tf.train.Checkpoint is different, one is net=model and another is model=model. So, if I use checkpoints saved by checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model), it is impossible using checkpoint = tf.train.Checkpoint(optimizer=optimizer, net=model) to restore it. So, we must keep parameters in tf.train.Checkpoint as same as possible. By the way, it's a good way add checkpoint.restore(ckpt_path).assert_existing_objects_matched() to find restore error as soon as possible.
• [x] using tf.keras to run distribute training
• [ ] add coco eval while training

Requirement

• tensorflow 2.0.0
• python 3.6
• cuda 10
• imgaug == 0.3.0
• pycocotools

About Dataset

we use the first 12 points of ai-challenger format, which can found in this website. Maybe disabled, MSCOCO dataset is ok too, but need to delete five points on head and change its format just like ai-challenger. Note that we still use pycocotools to load data, so if you use ai-challenger, you need to translate its annos file format into coco annos format. here is a convert code just for reference.

Network Structure

In this repo, just use hrnet as for its body network, you can replace this body with any other network as you like. Please check for here: nets/spm_model.py

Single Gpu Training

python3 main.py

All config can be found in config/center_config.py

Multi-GPU Training

python3 distribute/custom_train.py

~~Note that if you have four gpus and its ids is [0, 1, 2, 3], and you want to use gpu id [2, 3] is not work very well for now. You can only use gpu id [0, 1] or [0, 1, 2] will work fine. I didn't know why and wish someone can tell me.~~

The reason why we set os.environ['CUDA_VISIBLE_DEVICES'] = '2, 3' but can not use gpu_ids = [2, 3] is that tensorflow has already make gpu 2/3 on machine re-declear to 0/1. So, if we want to use gpu_ids = [2, 3], just write:

os.environ['CUDA_VISIBLE_DEVICES'] = '2, 3'
gpu_ids = [0, 1]
devices = ['/device:GPU:{}'.format(i) for i in gpu_ids]
strategy = tf.distribute.MirroredStrategy(devices=devices)

in using distribute training.

Test on images

python3 tools/spm_model_test.py

Eval

create predicts json file

python3 tools/model_val.py

eval

python3 tools/ai_format_kps_eval.py --ref true_label.json --submit predict.json

detailed information can be found here

About loss

In spm_loss function, you need carefully to set value of two different kinds of losses in order to make them balanced in numerical.

ai_formate joints:

1. right_shoulder
2. right_elbow
3. right_wrist
4. left_shoulder
5. left_elbow
6. left_wrist
7. right_hip
8. right_knee
9. right_ankle
10. left_hip
11. left_knee
12. left_ankle

Thanks

hrnet tensorlfow implementation

PPN network for SPM author's another work