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util.py
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util.py
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#original script: https://github.com/fangchangma/sparse-to-dense/blob/master/utils.lua
import torch
import math
import numpy as np
def lg10(x):
return torch.div(torch.log(x), math.log(10))
def maxOfTwo(x, y):
z = x.clone()
maskYLarger = torch.lt(x, y)
z[maskYLarger.detach()] = y[maskYLarger.detach()]
return z
def nValid(x):
return torch.sum(torch.eq(x, x).float())
def nNanElement(x):
return torch.sum(torch.ne(x, x).float())
def getNanMask(x):
return torch.ne(x, x)
def setNanToZero(input, target):
nanMask = getNanMask(target)
nValidElement = nValid(target)
_input = input.clone()
_target = target.clone()
_input[nanMask] = 0
_target[nanMask] = 0
return _input, _target, nanMask, nValidElement
def evaluateError(output, target):
errors = {'MSE': 0, 'RMSE': 0, 'ABS_REL': 0, 'LG10': 0,
'MAE': 0, 'DELTA1': 0, 'DELTA2': 0, 'DELTA3': 0}
_output, _target, nanMask, nValidElement = setNanToZero(output, target)
if (nValidElement.data.cpu().numpy() > 0):
diffMatrix = torch.abs(_output - _target)
errors['MSE'] = torch.sum(torch.pow(diffMatrix, 2)) / nValidElement
errors['RMSE'] = torch.sqrt(errors['MSE'])
errors['MAE'] = torch.sum(diffMatrix) / nValidElement
realMatrix = torch.div(diffMatrix, _target)
realMatrix[nanMask] = 0
errors['ABS_REL'] = torch.sum(realMatrix) / nValidElement
LG10Matrix = torch.abs(lg10(_output) - lg10(_target))
LG10Matrix[nanMask] = 0
errors['LG10'] = torch.sum(LG10Matrix) / nValidElement
yOverZ = torch.div(_output, _target)
zOverY = torch.div(_target, _output)
maxRatio = maxOfTwo(yOverZ, zOverY)
errors['DELTA1'] = torch.sum(
torch.le(maxRatio, 1.25).float()) / nValidElement
errors['DELTA2'] = torch.sum(
torch.le(maxRatio, math.pow(1.25, 2)).float()) / nValidElement
errors['DELTA3'] = torch.sum(
torch.le(maxRatio, math.pow(1.25, 3)).float()) / nValidElement
errors['MSE'] = float(errors['MSE'].data.cpu().numpy())
errors['RMSE'] = float(errors['RMSE'].data.cpu().numpy())
errors['ABS_REL'] = float(errors['ABS_REL'].data.cpu().numpy())
errors['LG10'] = float(errors['LG10'].data.cpu().numpy())
errors['MAE'] = float(errors['MAE'].data.cpu().numpy())
errors['DELTA1'] = float(errors['DELTA1'].data.cpu().numpy())
errors['DELTA2'] = float(errors['DELTA2'].data.cpu().numpy())
errors['DELTA3'] = float(errors['DELTA3'].data.cpu().numpy())
return errors
def addErrors(errorSum, errors, batchSize):
errorSum['MSE']=errorSum['MSE'] + errors['MSE'] * batchSize
errorSum['RMSE']=errorSum['RMSE'] + errors['RMSE'] * batchSize
errorSum['ABS_REL']=errorSum['ABS_REL'] + errors['ABS_REL'] * batchSize
errorSum['LG10']=errorSum['LG10'] + errors['LG10'] * batchSize
errorSum['MAE']=errorSum['MAE'] + errors['MAE'] * batchSize
errorSum['DELTA1']=errorSum['DELTA1'] + errors['DELTA1'] * batchSize
errorSum['DELTA2']=errorSum['DELTA2'] + errors['DELTA2'] * batchSize
errorSum['DELTA3']=errorSum['DELTA3'] + errors['DELTA3'] * batchSize
return errorSum
def averageErrors(errorSum, N):
averageError={'MSE': 0, 'RMSE': 0, 'ABS_REL': 0, 'LG10': 0,
'MAE': 0, 'DELTA1': 0, 'DELTA2': 0, 'DELTA3': 0}
averageError['MSE'] = errorSum['MSE'] / N
averageError['RMSE'] = errorSum['RMSE'] / N
averageError['ABS_REL'] = errorSum['ABS_REL'] / N
averageError['LG10'] = errorSum['LG10'] / N
averageError['MAE'] = errorSum['MAE'] / N
averageError['DELTA1'] = errorSum['DELTA1'] / N
averageError['DELTA2'] = errorSum['DELTA2'] / N
averageError['DELTA3'] = errorSum['DELTA3'] / N
return averageError