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

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

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

you need to install these package to run

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

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

• 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

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

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

• 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)

this repository keep MIT license.