mcflow's Introduction

MCFlow

A PyTorch implementation of our CVPR 2020 conference paper "MCFlow: Monte Carlo Flow Models for Data Imputation."

By Trevor W. Richardson, Wencheng Wu, Lei Lin, Beilei Xu and Edgar A. Bernal.

Install and Run Code

Setup repo

  git clone https://github.com/trevor-richardson/MCFlow

  cd MCFlow

Setup environment

  conda env create -f env.yml

  conda activate mcflow

Run experiment

  python main.py

Citation

Please cite the following if you find our work useful in your research:

@InProceedings{Rich_2020_CVPR,
author = {Richardson, Trevor W. and Wu, Wencheng and Lin, Lei and Xu, Beilei and Bernal, Edgar A.},
title = {MCFlow: Monte Carlo Flow Models for Data Imputation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

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benchmark on CIFAR-10 dataset

Hi,

Thanks for your work! I wanted to reproduce the results of your paper on CIFAR-10 dataset, so I added the following code to the path_to_matrix function:

def path_to_matrix(path):
    ## other codes omitted
    elif path == 'cifar':
        trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True)
        cifar_train = []
        for idx in range(len(trainset)):
            cifar_train.append(np.moveaxis(np.array(trainset[idx][0]), 2, 0).flatten() / 255)
        
        testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True)
        cifar_test = []
        for idx in range(len(testset)):
            cifar_test.append(np.moveaxis(np.array(testset[idx][0]), 2, 0).flatten() / 255)
        return cifar_train, cifar_test, (3, 32, 32)
    ## other codes omitted

However, the RMSE is quite different from what was reported. For example, RMSE is about 0.11 when the missing rate is 0.1. So I guess that some preprocessing steps are needed to reproduce the results. Could you please share those steps or the codes to run the CIFAR experiments?

Thanks,

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mcflow's Introduction

MCFlow

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benchmark on CIFAR-10 dataset

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