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{
  "Data Augmentation Options": {
    "block": null,
    "gain": null,
    "gamma": null,
    "hflip": false,
    "mean": null,
    "noise_pwr": 0,
    "prob": [1.0, 0, 0, 0, 0],
    "rotate": 15,

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#/usr/bin/env python
# check to see if any image in a directory has a problem

import argparse
import csv
from glob import glob
import os
import sys

import nibabel as nib

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Filename,FiveoClockShadow,ArchedEyebrows,Attractive,BagsUnderEyes,Bald,Bangs,BigLips,BigNose,BlackHair,BlondHair,Blurry,BrownHair,BushyEyebrows,Chubby,DoubleChin,Eyeglasses,Goatee,GrayHair,HeavyMakeup,HighCheekbones,Male,MouthSlightlyOpen,Mustache,NarrowEyes,NoBeard,OvalFace,PaleSkin,PointyNose,RecedingHairline,RosyCheeks,Sideburns,Smiling,StraightHair,WavyHair,WearingEarrings,WearingHat,WearingLipstick,WearingNecklace,WearingNecktie,Young
1,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,1,0,0,1,0,0,1,0,0,0,1,1,0,1,0,1,0,0,1
2,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1
3,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1
4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,1,1,0,1
5,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1
6,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,1,0,1,0,0,1
7,1,0,1,1,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1
8,1,1,

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
tif_to_nii

command line executable to convert a directory of tif images
(from one image) to a nifti image stacked along a user-specified axis

call as: python tif_to_nii.py /path/to/tif/ /path/to/nifti
(append optional arguments to the call as desired)

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class ExtendedMSELoss(nn.Module):
    """ modified MSE loss for variance fitting """
    def forward(self, out:torch.Tensor, y:torch.Tensor) -> torch.Tensor:
        yhat, s = out
        loss = torch.mean(0.5 * (torch.exp(-s) * F.mse_loss(yhat, y, reduction='none') + s))
        return loss

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class ExtendedL1Loss(nn.Module):
    """ modified L1 loss for scale param. fitting """
    def forward(self, out:torch.Tensor, y:torch.Tensor) -> torch.Tensor:
        yhat, s = out
        loss = torch.mean((torch.exp(-s) * F.l1_loss(yhat, y, reduction='none')) + s)
        return loss

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class ExtendedBCELoss(nn.Module):
    """ modified BCE loss for variance fitting """
    def forward(self, out:torch.Tensor, y:torch.Tensor, n_samp:int=10) -> torch.Tensor:
        logit, sigma = out
        dist = torch.distributions.Normal(logit, torch.exp(sigma))
        mc_logs = dist.rsample((n_samp,))
        loss = 0.
        for mc_log in mc_logs:
            loss += F.binary_cross_entropy_with_logits(mc_log, y)
        loss /= n_samp

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def regression_uncertainty(yhat:torch.Tensor, s:torch.Tensor, mse:bool=True) -> Tuple[torch.Tensor, torch.Tensor]:
    """ calculate epistemic and aleatory uncertainty quantities based on whether MSE or L1 loss used """
    # variance over samples (dim=0), mean over channels (dim=1, after reduction by variance calculation)
    epistemic = torch.mean(yhat.var(dim=0, unbiased=True), dim=1, keepdim=True)
    aleatory = torch.mean(torch.exp(s), dim=0) if mse else torch.mean(2*torch.exp(s)**2, dim=0)
    return epistemic, aleatory