ranftlr

def trimmed_mae_loss(prediction, target, mask, trim=0.2):
    M = torch.sum(mask, (1, 2))
    res = prediction - target

    res = res[mask.bool()].abs()

    trimmed, _ = torch.sort(res.view(-1), descending=False)[
        : int(len(res) * (1.0 - trim))
    ]

dvdhfnr

import torch
import torch.nn as nn


def compute_scale_and_shift(prediction, target, mask):
    # system matrix: A = [[a_00, a_01], [a_10, a_11]]
    a_00 = torch.sum(mask * prediction * prediction, (1, 2))
    a_01 = torch.sum(mask * prediction, (1, 2))
    a_11 = torch.sum(mask, (1, 2))

jamietre

@ECHO OFF

rem usage: transcodemvc input output [-r] [-s]
rem    -r will re-use a previously extracted elementary stream from the temp file location
rem    -s will swap eyes

set TEMP_DIR=c:\temp\recodemvc
set TEMP_FILE=%TEMP_DIR%\temp_mvc.264
set PROFILE=high
set SWAP=false