The implementation for the paper "TTNet: Real-time temporal and spatial video analysis of table tennis"
An introduction of the project could be found here (from the authors)
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Ball detection global stage
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Ball detection local stage (refinement)
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Events Spotting detection (Bounce and Net hit)
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Semantic Segmentation (Human, table, and scoreboard)
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Enable/Disable modules in the TTNet model
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Smooth labeling for event spotting
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TensorboardX
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(Update 2020.06.23): Training much faster, achieve > 120 FPS in the inference phase on a single GPU (GTX1080Ti).
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(Update 2020.07.03): The implementation could achieve comparative results with the reported results in the TTNet paper.
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(Update 2020.07.06): There are several limitations of the TTNet Paper (hints: Loss function, input size, and 2 more). I have implemented the task with a new approach and a new model. Now the new model could achieve:
>130FPS inference,- ~0.96 IoU score for the segmentation task
<4 pixels (in the full HD resolution (1920x1080)) of Root Mean Square Error (RMSE) for the ball detection task- ~97% percentage of correction events (PCE) and smooth PCE (SPCE).
pip install -U -r requirement.txtYou will also need PyTurboJPEG:
$ sudo apt-get install libturbojpeg
...
$ pip install PyTurboJPEG
...Other instruction for setting up virtual environments is here
The instruction for the dataset preparation is here
TTNet model architecture:
Input tensor structure
python main.py --gpu_idx 0By default (as the above command), there are 4 modules in the TTNet model: global stage, local stage, event spotting, segmentation.
You can disable one of the modules, except the global stage module.
An important note is if you disable the local stage module, the event spotting module will be also disabled.
- You can disable the segmentation stage:
python main.py --gpu_idx 0 --no_seg- You can disable the event spotting module:
python main.py --gpu_idx 0 --no_event- You can disable the local stage, event spotting, segmentation modules:
python main.py --gpu_idx 0 --no_local --no_seg --no_eventWe should always use the nccl backend for multi-processing distributed training since it currently provides the best
distributed training performance.
- Single machine (node), multiple GPUs
python main.py --dist-url 'tcp://127.0.0.1:29500' --dist-backend 'nccl' --multiprocessing-distributed --world-size 1 --rank 0- Two machines (two nodes), multiple GPUs
First machine
python main.py --dist-url 'tcp://IP_OF_NODE1:FREEPORT' --dist-backend 'nccl' --multiprocessing-distributed --world-size 2 --rank 0Second machine
python main.py --dist-url 'tcp://IP_OF_NODE2:FREEPORT' --dist-backend 'nccl' --multiprocessing-distributed --world-size 2 --rank 1The performance of the TTNet strongly depends on the global stage for ball detection. Hence, It's necessary to train the
global ball stage module of the TTNet model first.
- 1st phase: Train the global and segmentation modules with 30 epochs
./train_1st_phase.sh- 2nd phase: Load the trained weights to the global and the segmentation part, initialize the weight of the local stage with the weights of the global stage. In this phase, we train and just update weights of the local and the event modules. (30 epochs)
./train_2nd_phase.sh- 3rd phase: Fine tune all modules. Train the network with only 30 epochs
./train_3rd_phase.shThe Tensorboard was used to save loss values on the training set and the validation set. Execute the below command on the working terminal:
cd logs/<task directory>/tensorboard/
tensorboard --logdir=./
Then open the web browser and go to: http://localhost:6006/
The thresholds of the segmentation and event spotting tasks could be set in test.sh bash shell scripts.
./test_3rd_phase.shRun a demonstration with an input video:
./demo.shIf you think this work is useful, please give me a star! If you find any errors or have any suggestions, please contact me. Thank you!
Email: [email protected]
@article{TTNet,
author = {Roman Voeikov, Nikolay Falaleev, Ruslan Baikulov},
title = {TTNet: Real-time temporal and spatial video analysis of table tennis},
year = {2020},
conference = {CVPR 2020},
}usage: main.py [-h] [--seed SEED] [--saved_fn FN] [-a ARCH] [--dropout_p P]
[--multitask_learning] [--no_local] [--no_event] [--no_seg]
[--pretrained_path PATH] [--overwrite_global_2_local]
[--no-val] [--no-test] [--val-size VAL_SIZE]
[--smooth-labelling] [--num_samples NUM_SAMPLES]
[--num_workers NUM_WORKERS] [--batch_size BATCH_SIZE]
[--print_freq N] [--checkpoint_freq N] [--sigma SIGMA]
[--thresh_ball_pos_mask THRESH] [--start_epoch N]
[--num_epochs N] [--lr LR] [--minimum_lr MIN_LR] [--momentum M]
[-wd WD] [--optimizer_type OPTIMIZER] [--lr_type SCHEDULER]
[--lr_factor FACTOR] [--lr_step_size STEP_SIZE]
[--lr_patience N] [--earlystop_patience N] [--freeze_global]
[--freeze_local] [--freeze_event] [--freeze_seg]
[--bce_weight BCE_WEIGHT] [--global_weight GLOBAL_WEIGHT]
[--local_weight LOCAL_WEIGHT] [--event_weight EVENT_WEIGHT]
[--seg_weight SEG_WEIGHT] [--world-size N] [--rank N]
[--dist-url DIST_URL] [--dist-backend DIST_BACKEND]
[--gpu_idx GPU_IDX] [--no_cuda] [--multiprocessing-distributed]
[--evaluate] [--resume_path PATH] [--use_best_checkpoint]
[--seg_thresh SEG_THRESH] [--event_thresh EVENT_THRESH]
[--save_test_output] [--video_path PATH] [--output_format PATH]
[--show_image] [--save_demo_output]
TTNet Implementation
optional arguments:
-h, --help show this help message and exit
--seed SEED re-produce the results with seed random
--saved_fn FN The name using for saving logs, models,...
-a ARCH, --arch ARCH The name of the model architecture
--dropout_p P The dropout probability of the model
--multitask_learning If true, the weights of different losses will be
learnt (train).If false, a regular sum of different
losses will be applied
--no_local If true, no local stage for ball detection.
--no_event If true, no event spotting detection.
--no_seg If true, no segmentation module.
--pretrained_path PATH
the path of the pretrained checkpoint
--overwrite_global_2_local
If true, the weights of the local stage will be
overwritten by the global stage.
--no-val If true, use all data for training, no validation set
--no-test If true, dont evaluate the model on the test set
--val-size VAL_SIZE The size of validation set
--smooth-labelling If true, smoothly make the labels of event spotting
--num_samples NUM_SAMPLES
Take a subset of the dataset to run and debug
--num_workers NUM_WORKERS
Number of threads for loading data
--batch_size BATCH_SIZE
mini-batch size (default: 16), this is the totalbatch
size of all GPUs on the current node when usingData
Parallel or Distributed Data Parallel
--print_freq N print frequency (default: 10)
--checkpoint_freq N frequency of saving checkpoints (default: 3)
--sigma SIGMA standard deviation of the 1D Gaussian for the ball
position target
--thresh_ball_pos_mask THRESH
the lower thresh for the 1D Gaussian of the ball
position target
--start_epoch N the starting epoch
--num_epochs N number of total epochs to run
--lr LR initial learning rate
--minimum_lr MIN_LR minimum learning rate during training
--momentum M momentum
-wd WD, --weight_decay WD
weight decay (default: 1e-6)
--optimizer_type OPTIMIZER
the type of optimizer, it can be sgd or adam
--lr_type SCHEDULER the type of the learning rate scheduler (steplr or
ReduceonPlateau)
--lr_factor FACTOR reduce the learning rate with this factor
--lr_step_size STEP_SIZE
step_size of the learning rate when using steplr
scheduler
--lr_patience N patience of the learning rate when using
ReduceoPlateau scheduler
--earlystop_patience N
Early stopping the training process if performance is
not improved within this value
--freeze_global If true, no update/train weights for the global stage
of ball detection.
--freeze_local If true, no update/train weights for the local stage
of ball detection.
--freeze_event If true, no update/train weights for the event module.
--freeze_seg If true, no update/train weights for the segmentation
module.
--bce_weight BCE_WEIGHT
The weight of BCE loss in segmentation module, the
dice_loss weight = 1- bce_weight
--global_weight GLOBAL_WEIGHT
The weight of loss of the global stage for ball
detection
--local_weight LOCAL_WEIGHT
The weight of loss of the local stage for ball
detection
--event_weight EVENT_WEIGHT
The weight of loss of the event spotting module
--seg_weight SEG_WEIGHT
The weight of BCE loss in segmentation module
--world-size N number of nodes for distributed training
--rank N node rank for distributed training
--dist-url DIST_URL url used to set up distributed training
--dist-backend DIST_BACKEND
distributed backend
--gpu_idx GPU_IDX GPU index to use.
--no_cuda If true, cuda is not used.
--multiprocessing-distributed
Use multi-processing distributed training to launch N
processes per node, which has N GPUs. This is the
fastest way to use PyTorch for either single node or
multi node data parallel training
--evaluate only evaluate the model, not training
--resume_path PATH the path of the resumed checkpoint
--use_best_checkpoint
If true, choose the best model on val set, otherwise
choose the last model
--seg_thresh SEG_THRESH
threshold of the segmentation output
--event_thresh EVENT_THRESH
threshold of the event spotting output
--save_test_output If true, the image of testing phase will be saved
--video_path PATH the path of the video that needs to demo
--output_format PATH the type of the demo output
--show_image If true, show the image during demostration
--save_demo_output If true, the image of demonstration phase will be
saved