Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"

Related tags

Deep Learning python optimization pytorch optimization-algorithms few-shot
Overview

Optimization as a Model for Few-Shot Learning

This repo provides a Pytorch implementation for the Optimization as a Model for Few-Shot Learning paper.

Installation of pytorch

The experiments needs installing Pytorch

Data

For the miniImageNet you need to download the ImageNet dataset and execute the script utils.create_miniImagenet.py changing the lines:

pathImageNet = '<path_to_downloaded_ImageNet>/ILSVRC2012_img_train'
pathminiImageNet = '<path_to_save_MiniImageNet>/miniImagenet/'

And also change the main file option.py line or pass it by command line arguments:

parser.add_argument('--dataroot', type=str, default='<path_to_save_MiniImageNet>/miniImagenet/',help='path to dataset')

Installation

$ pip install -r requirements.txt
$ python main.py

Acknowledgements

Special thanks to @sachinravi14 for their Torch implementation. I intend to replicate their code using Pytorch. More details at https://github.com/twitter/meta-learning-lstm

Cite

@inproceedings{Sachin2017,
  title={Optimization as a model for few-shot learning},
  author={Ravi, Sachin and Larochelle, Hugo},
  booktitle={In International Conference on Learning Representations (ICLR)},
  year={2017}
}

Authors

  • Albert Berenguel (@aberenguel) Webpage
Comments
  • Cannot take a larger sample than population when 'replace=False'

    Cannot take a larger sample than population when 'replace=False'

    Hello I have few questions , maybe you could give me some advice. What's the mean of samples_per_class and samples_per_class_eval in miniImagenet.py And I want to used my own data in this program for Binary classification, which parts of the code should I have to alter?

    opened by 378983112 0
  • CUDA out of memory

    CUDA out of memory

    Traceback (most recent call last): File "main.py", line 45, in <module> results = importlib.import_module(opt['metaLearner']).run(opt,data) File "/home/user/myproject/FewShotLearning/model/lstm/train-lstm.py", line 123, in run opt['batchSize'][opt['nTrainShot']]) File "/home/user/anaconda3/envs/FewShotLearning/lib/python2.7/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "/home/user/myproject/FewShotLearning/model/lstm/metaLearner.py", line 149, in forward output, loss = learner(testInput, testTarget) File "/home/user/anaconda3/envs/FewShotLearning/lib/python2.7/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "/home/user/myproject/FewShotLearning/model/lstm/learner.py", line 51, in forward output = self.modelF.net(inputs) File "/home/user/anaconda3/envs/FewShotLearning/lib/python2.7/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "/home/user/myproject/FewShotLearning/model/lstm-classifier.py", line 79, in forward x = self.layer2(x) File "/home/user/anaconda3/envs/FewShotLearning/lib/python2.7/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "/home/user/anaconda3/envs/FewShotLearning/lib/python2.7/site-packages/torch/nn/modules/container.py", line 92, in forward input = module(input) File "/home/user/anaconda3/envs/FewShotLearning/lib/python2.7/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "/home/user/anaconda3/envs/FewShotLearning/lib/python2.7/site-packages/torch/nn/modules/batchnorm.py", line 76, in forward exponential_average_factor, self.eps) File "/home/user/anaconda3/envs/FewShotLearning/lib/python2.7/site-packages/torch/nn/functional.py", line 1623, in batch_norm training, momentum, eps, torch.backends.cudnn.enabled RuntimeError: CUDA out of memory. Tried to allocate 16.25 MiB (GPU 0; 11.91 GiB total capacity; 8.65 GiB already allocated; 17.06 MiB free; 950.50 MiB cached) ` nvidia-smi Wed Apr 3 20:53:46 2019
    +-----------------------------------------------------------------------------+ | NVIDIA-SMI 396.26 Driver Version: 396.26 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 TITAN X (Pascal) Off | 00000000:02:00.0 Off | N/A | | 23% 35C P8 16W / 250W | 1035MiB / 12196MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 1 TITAN X (Pascal) Off | 00000000:03:00.0 Off | N/A | | 23% 36C P8 18W / 250W | 10MiB / 12196MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 2 TITAN X (Pascal) Off | 00000000:82:00.0 Off | N/A | | 23% 30C P8 17W / 250W | 10MiB / 12196MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 3 TITAN X (Pascal) Off | 00000000:83:00.0 Off | N/A | | 23% 33C P8 17W / 250W | 10MiB / 12196MiB | 0% Default | +-------------------------------+----------------------+----------------------+

    +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | 0 10217 C ...qun/anaconda2/envs/pt04_py27/bin/python 1021MiB | +-----------------------------------------------------------------------------+ `

    opened by zihaozhang9 3
  • pytorch version?

    pytorch version?

    Traceback (most recent call last): File "main.py", line 39, in results = importlib.import_module(opt['metaLearner']).run(opt,data) File "/home/user/myproject/FewShotLearning/model/lstm/train-lstm.py", line 121, in run opt['batchSize'][opt['nTrainShot']]) File "/home/user/anaconda3/envs/FewShotLearning/lib/python2.7/site-packages/torch/nn/modules/module.py", line 489, in call result = self.forward(*input, **kwargs) File "/home/user/myproject/FewShotLearning/model/lstm/metaLearner.py", line 145, in forward self.lstm2_fS_iS_cS_deltaS) File "/home/user/anaconda3/envs/FewShotLearning/lib/python2.7/site-packages/torch/nn/modules/module.py", line 489, in call result = self.forward(*input, **kwargs) File "/home/user/myproject/FewShotLearning/model/lstm/metalstm.py", line 166, in forward fS_n = torch.stack(fS_n, 0) TypeError: stack(): argument 'tensors' (position 1) must be tuple of Tensors, not Tensor

    opened by zihaozhang9 0
  • Miniimagenet files

    Miniimagenet files

    Hello,

    Thank you for sharing the code. Now I am preparing for the reproduction of your code using miniimagenet. I am wondering if you could provide me the miniimagenet dataset. I would be very grateful for that.

    Thank you very much! [email protected]

    opened by flexibility2 0
  • How to extract miniimagenet from imagenet train dataset downloaded in a folder?

    How to extract miniimagenet from imagenet train dataset downloaded in a folder?

    I have Imagenet 2012 train dataset downloaded and all the tars are extracted together in a single folder. How do get miniimagenet out of it? There are no zero-padded image names in the original dataset.

    opened by SinghGauravKumar 3
Owner
Albert Berenguel Centeno
Phd student and computer vision enthusiast. "There are no secrets to success. It is the result of preparation, hard work, and learning from failure" ColinPowell
Albert Berenguel Centeno
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