# python train-stacked-residual-aug-pretrain.py --exp_id test --resume_prefix_pose stacked-8-lr-0.00025-10.pth.tar

# Zhiqiang Tang, May 2017

# import sys, warnings, traceback, torch

# def warn_with_traceback(message, category, filename, lineno, file=None, line=None):

# sys.stderr.write(warnings.formatwarning(message, category, filename, lineno, line))

# traceback.print_stack(sys._getframe(2))

# warnings.showwarning = warn_with_traceback; warnings.simplefilter('always', UserWarning);

# torch.utils.backcompat.broadcast_warning.enabled = True

# torch.utils.backcompat.keepdim_warning.enabled = True

import os, time

from PIL import Image, ImageDraw

import numpy as np

from collections import OrderedDict

import torch

import torch.nn as nn

import torch.nn.functional as F

import torch.nn.parallel

import torch.backends.cudnn as cudnn

import torch.optim

import torch.utils.data

import torchvision

import torchvision.transforms as transforms

import torchvision.datasets as datasets

from torch.nn.parameter import Parameter

from options.train_options import TrainOptions

from data.pretrain_s_r_agent import MPII

import models.asn_stacked_hg as model

from utils.util import AverageMeter

from utils.util import ASNTrainHistory

from utils.visualizer import Visualizer

from utils.checkpoint import Checkpoint

from utils.util import adjust_lr

from utils import imutils

from pylib import HumanAcc, HumanPts, HumanAug, Evaluation

from utils.logger import Logger

cudnn.benchmark = True

logsoftmax = nn.LogSoftmax(dim=1)

def main():

# visualizer.display_imgpts(imgs, pred_pts, 4)

def train(train_loader, hg, agent, optimizer, epoch, visualizer, opt):

return losses.avg

def validate(val_loader, hg, agent, epoch, visualizer, opt):

return losses.avg

def read_grnd_distri_from_txt(load_path):

return grnd_distri_list

if __name__ == '__main__':

main()