Awesome

SST

SST: Single-Stream Temporal Action Proposal

Dependencies

1. pytorch
2. numpy
3. h5py

Data

Currently, the code is setup to work with ActivityNet. The raw ActivityNet version 1.3 must be downloaded as a json in the data/ActivityNet directory. Also, an hdf5 database with the PCA'ed 500 dimensional C3D features availabel here.

Other datasets

If you want to use other datasets, write a new ProposalDataset class that is defined in data.py. Follow the guidelines used in the ActivityNet class.

Training

Run train.py.

arguments:
  -h, --help            show this help message and exit
  --dataset             Name of the data class to use from data.py
  --data                location of the dataset
  --features            location of the video features
  --save                path to folder where to save the final model and log
                        files and corpus
  --save-every          Save the model every x epochs
  --clean               Delete the models and the log files in the folder
  --W                   The rnn kernel size to use to get the proposal
                        features
  --K                   Number of proposals
  --max-W               maximum number of windows to return per video
  --iou-threshold       threshold above which we say something is positive
  --rnn-type            type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU)
  --rnn-num-layers      Number of layers in rnn
  --rnn-dropout         dropout used in rnn
  --video-dim           dimensions of video (C3D) features
  --hidden-dim          dimensions output layer of video network
  --lr LR               initial learning rate
  --dropout             dropout between RNN layers
  --momentum            SGD momentum
  --weight-decay        SGD weight decay
  --epochs              upper epoch limit
  --batch-size          batch size
  --seed                random seed
  --cuda                use CUDA
  --log-interval        report interval
  --debug               Print out debug sentences
  --num-samples         Number of training samples to train with
  --shuffle             whether to shuffle the data
  --nthreads            number of worker threas used to load data
  --resume              reload the model