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

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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

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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.

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Dependencies

you need to install these package to run

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

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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

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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.

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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)

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LICENSE

this repository keep MIT license.