SST: Single-Stream Temporal Action Proposal
- pytorch
- numpy
- h5py
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.
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.
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