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deep cross modal hashing (torchcmh)

Chinese readme

torchcmh is a library built on PyTorch for deep learning cross modal hashing.
Including:

  • data visualization
  • baseline methods
  • multiple data reading API
  • loss function API
  • config call

Dataset

There are four datasets(Mirflickr25k, Nus Wide, MS coco, IAPR TC-12) sort out by myself, if you want use these datasets, please download mat file and image file by readme file in dataset package.
Please read "readme" in dataset package

Model

You can crate model or use existing model. We support some pre-train models, you can check out the README.md file in details.

Dependencies

you need to install these package to run

  • visdom 0.1.8+
  • pytorch 1.0.0+
  • tqdm 4.0+

Logs and checkpoints

All method training will create a log and checkpoint to store the model.
you can find log in ./logs/{method_name}/{dataset_name}/date.txt
you can find checkpoints in ./checkpoints/{method_name}/{dataset_name}/{bit}-{model_name}.pth

How to using

  • create a configuration file as ./script/default_config.yml
training:
  # the name of python file in training
  method: SCAHN
  # the data set name, you can choose mirflickr25k, nus wide, ms coco, iapr tc-12
  dataName: Mirflickr25K
  batchSize: 64
  # the bit of hash codes
  bit: 64
  # if true, the program will be run on gpu. Of course, you need install 'cuda' and 'cudnn' better.
  cuda: True
  # the device id you want to use, if you want to multi gpu, you can use [id1, id2]
  device: 0
datasetPath:
  Mirflickr25k:
    # the path you download the image of data set. Attention: image files, not mat file.
    img_dir: \dataset\mirflickr25k\mirflickr
  • run ./script/main.py and input configuration file path.
from torchcmh.run import run
if __name__ == '__main__':
    run(config_path='default_config.yml')
  • data visualization Before you start trainer, please use command as follows to open visdom server.
python -m visdom.server

Or you can run script/visdom_open.py

python script/visdom_open.py

Then you can see the charts in browser in special port.

How to create your method

  • create new method file in folder ./torchcmh/training/
  • inherit implement TrainBase
  • change config.yml file and run.

A easy way to create your method

run script create_algorithm.py as following:

python create_algorithm.py ${method_name}

You can assign the method name by command or in create_algorithm.py

After running create_algorithm.py, you should get a new python file with your method name in "torchcmh/training/". And you should also get a directory with your method name in "torchcmh/models".

Finish your algorithm and run it.

some function in TrainBase

  • loss variable
    In your method, some variables you need to store and check, such as loss and acc. You can use var in TrainBase "loss_store" to store:
self.loss_store = ["log loss", 'quantization loss', 'balance loss', 'loss']

"loss_store" is a list, push the name and update value by "loss_store[name].update()":

value = 1000    # the value to update 
n = 10          # the number of instance for current value
self.loss_store['log loss'].update(value, n)

For print and visualization the loss, you can use:

epoch = 1       # current epoch
self.print_loss(epoch)  # print loss
self.plot_loss("img loss")  # visualization img loss is the name of chart

clean "loss_store"

self.reset_loss()   # reset loss_store
  • parameters In your method, all parameters can be stored as follows:
self.parameters = {'gamma': 1, 'eta': 1}    # {name: value}

when method training, log will record the parameters and learning rate.

  • valid
for epoch in range(self.max_epoch):
    # training codes
    self.valid(epoch)

LICENSE

this repository keep MIT license.