Implementation of Generating Videos with Scene Dynamics.
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- numpy
- imageio
- opencv-python
- tensorflow-gpu(version1.2)
- Train the model of videoGan(that is train).
- Generating videos using videoGan(that is test).
- download the data for training. click → link.
for example, you can download:
<Golf Only> → http://data.csail.mit.edu/videogan/golf.tar.bz2
<List of Golf Files> → http://data.csail.mit.edu/videogan/golf.txt
- Unzip the folders that you have downloaded.
for example, if you have downloaded the data of golf, then you can execute:
"bunzip2 golf.tar.bz2"
"tar xvf golf.tar"
- modify config.py according to your needs.
options explain:
-info:
Introduce options.
-batch_size:
Number of videos to process in a batch.
-lr_g:
Learning rate for generator.
-lr_d:
Learning rate for discriminator.
-beta1_g:
Beta1 for generator.
-beta1_d:
Beta1 for discriminator.
-dis_dim:
Scale for discriminator-channel.
-gen_dim:
Scale for generator-channel.
-gen_scale:
Scale for generator-width and height.
-pic_dim:
Dimension of image color.
-noise_dim:
Dimension of initial noise vector.
-sample_size:
Number of samples to be generated at once(for evaluate).
-mask_L1_lambda:
Weight for L1 regularizer of mask.
-trainlogfile:
Record the train info(filename).
-modelSaved:
Save the trained model(path).
-samplesSaved:
Save the sample videos from generator(path).
-max_epoch:
Number of training epochs.
-save_interval:
Save and test the model each save_interval epochs.
-trainSet:
The paths of the videos for training.
-imgSize:
The size of img(each frame) in the videos(trainSet).
- run "python3 train.py"
make sure that you have installed all of the dependencies mentioned in Dependency.
you can run "pip install -r requirements.txt" to install these dependencies.
preparing