torch cross modal hashing (torchcmh)

torchcmh is a library built on PyTorch for deep learning cross modal hashing.
if you want use the dataset i make, please download mat file and image file by readme file in dataset package.

you need to install these package to run

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

how to using

• create a configuration file as ./script/default_config.yml

training:
  # the name of python file in training
  method: DCMH
  # 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
  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.
    img_dir: I:\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')

how to create your method

• create new method file in folder ./torchcmh/training/
• inherit implement TrainBase
• change the training.method as your python file name in config .yml file and run.

some function in TrainBase

• data visualization

def plot_loss(self, title: str, loss_store=None):
    if loss_store is None:
        loss_store = self.loss_store
    if self.plotter:
        for name, loss in loss_store.items():
            self.plotter.plot(title, name, loss.avg)

• valid

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