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main_test_avatarposer.py
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main_test_avatarposer.py
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'''
# --------------------------------------------
# main testing code
# --------------------------------------------
# AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing (ECCV 2022)
# https://github.com/eth-siplab/AvatarPoser
# Jiaxi Jiang (jiaxi.jiang@inf.ethz.ch)
# Sensing, Interaction & Perception Lab,
# Department of Computer Science, ETH Zurich
'''
import os.path
import argparse
import numpy as np
import logging
import torch
from torch.utils.data import DataLoader
from utils import utils_logger
from utils import utils_option as option
from data.select_dataset import define_Dataset
from models.select_model import define_Model
from utils import utils_visualize as vis
save_animation = False
resolution = (800,800)
def main(json_path='options/test_avatarposer.json'):
'''
# ----------------------------------------
# Step--1 (prepare opt)
# ----------------------------------------
'''
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, default=json_path, help='Path to option JSON file.')
opt = option.parse(parser.parse_args().opt, is_train=True)
paths = (path for key, path in opt['path'].items() if 'pretrained' not in key)
if isinstance(paths, str):
if not os.path.exists(paths):
os.makedirs(paths)
else:
for path in paths:
if not os.path.exists(path):
os.makedirs(path)
# ----------------------------------------
# update opt
# ----------------------------------------
# -->-->-->-->-->-->-->-->-->-->-->-->-->-
init_iter, init_path_G = option.find_last_checkpoint(opt['path']['models'], net_type='G')
opt['path']['pretrained_netG'] = init_path_G
current_step = init_iter
# --<--<--<--<--<--<--<--<--<--<--<--<--<-
# ----------------------------------------
# save opt to a '../option.json' file
# ----------------------------------------
option.save(opt)
# ----------------------------------------
# return None for missing key
# ----------------------------------------
opt = option.dict_to_nonedict(opt)
# ----------------------------------------
# configure logger
# ----------------------------------------
logger_name = 'train'
utils_logger.logger_info(logger_name, os.path.join(opt['path']['log'], logger_name+'.log'))
logger = logging.getLogger(logger_name)
'''
# ----------------------------------------
# Step--2 (creat dataloader)
# ----------------------------------------
'''
# ----------------------------------------
# 1) create_dataset
# 2) creat_dataloader for train and test
# ----------------------------------------
dataset_type = opt['datasets']['test']['dataset_type']
for phase, dataset_opt in opt['datasets'].items():
if phase == 'test':
test_set = define_Dataset(dataset_opt)
test_loader = DataLoader(test_set, batch_size=dataset_opt['dataloader_batch_size'],
shuffle=False, num_workers=1,
drop_last=False, pin_memory=True)
elif phase == 'train':
continue
else:
raise NotImplementedError("Phase [%s] is not recognized." % phase)
'''
# ----------------------------------------
# Step--3 (initialize model)
# ----------------------------------------
'''
model = define_Model(opt)
if opt['merge_bn'] and current_step > opt['merge_bn_startpoint']:
logger.info('^_^ -----merging bnorm----- ^_^')
model.merge_bnorm_test()
model.init_test()
pos_error = []
vel_error = []
pos_error_hands = []
for index, test_data in enumerate(test_loader):
logger.info("testing the sample {}/{}".format(index, len(test_loader)))
model.feed_data(test_data, test=True)
model.test()
body_parms_pred = model.current_prediction()
body_parms_gt = model.current_gt()
predicted_angle = body_parms_pred['pose_body']
predicted_position = body_parms_pred['position']
predicted_body = body_parms_pred['body']
gt_angle = body_parms_gt['pose_body']
gt_position = body_parms_gt['position']
gt_body = body_parms_gt['body']
if index in [0, 10, 20] and save_animation:
video_dir = os.path.join(opt['path']['images'], str(index))
if not os.path.exists(video_dir):
os.makedirs(video_dir)
save_video_path_gt = os.path.join(video_dir, 'gt.avi')
if not os.path.exists(save_video_path_gt):
vis.save_animation(body_pose=gt_body, savepath=save_video_path_gt, bm = model.bm, fps=60, resolution = resolution)
save_video_path = os.path.join(video_dir, '{:d}.avi'.format(current_step))
vis.save_animation(body_pose=predicted_body, savepath=save_video_path, bm = model.bm, fps=60, resolution = resolution)
predicted_position = predicted_position#.cpu().numpy()
gt_position = gt_position#.cpu().numpy()
predicted_angle = predicted_angle.reshape(body_parms_pred['pose_body'].shape[0],-1,3)
gt_angle = gt_angle.reshape(body_parms_gt['pose_body'].shape[0],-1,3)
pos_error_ = torch.mean(torch.sqrt(torch.sum(torch.square(gt_position-predicted_position),axis=-1)))
pos_error_hands_ = torch.mean(torch.sqrt(torch.sum(torch.square(gt_position-predicted_position),axis=-1))[...,[20,21]])
gt_velocity = (gt_position[1:,...] - gt_position[:-1,...])*60
predicted_velocity = (predicted_position[1:,...] - predicted_position[:-1,...])*60
vel_error_ = torch.mean(torch.sqrt(torch.sum(torch.square(gt_velocity-predicted_velocity),axis=-1)))
pos_error.append(pos_error_)
vel_error.append(vel_error_)
pos_error_hands.append(pos_error_hands_)
pos_error = sum(pos_error)/len(pos_error)
vel_error = sum(vel_error)/len(vel_error)
pos_error_hands = sum(pos_error_hands)/len(pos_error_hands)
# testing log
logger.info('Average positional error [cm]: {:<.5f}, Average velocity error [cm/s]: {:<.5f}, Average positional error at hand [cm]: {:<.5f}\n'.format(pos_error*100, vel_error*100, pos_error_hands*100))
if __name__ == '__main__':
main()