cur_shape_iou_tot += I/U
cur_shape_iou_cnt += 1.
if cur_shape_iou_cnt > 0:
cur_shape_miou = cur_shape_iou_tot / cur_shape_iou_cnt
shape_iou_tot += cur_shape_miou
shape_iou_cnt += 1.
shape_mIoU = shape_iou_tot / shape_iou_cnt
part_iou = np.divide(part_intersect[1:], part_union[1:])
mean_part_iou = np.mean(part_iou)
opt.printer.info("===> Category {}-{}, Part mIOU is{:.4f} \t ".format(
opt.category_no, opt.category, mean_part_iou))
if __name__ == '__main__':
opt = OptInit().initialize()
opt.printer.info('===> Creating dataloader ...')
test_dataset = PartNet(opt.data_dir, opt.dataset, opt.category, opt.level, 'val')
test_loader = DenseDataLoader(test_dataset, batch_size=1, shuffle=True, num_workers=1)
opt.n_classes = test_loader.dataset.num_classes
opt.printer.info('===> Loading the network ...')
model = DenseDeepGCN(opt).to(opt.device)
opt.printer.info('===> loading pre-trained ...')
model, opt.best_value, opt.epoch = load_pretrained_models(model, opt.pretrained_model, opt.phase)
test(model, test_loader, opt)
state = {
'epoch': opt.epoch,
'state_dict': model_cpu,
'optimizer_state_dict': optimizer.state_dict(),
'scheduler_state_dict': scheduler.state_dict(),
'best_value': opt.best_value,
}
torch.save(state, filename)
logging.info('save a new best model into {}'.format(filename))
if __name__ == '__main__':
opt = OptInit()._get_args()
logging.info('===> Creating dataloader ...')
train_dataset = PartNet(opt.data_dir, 'sem_seg_h5', opt.category, opt.level, 'train')
train_loader = DenseDataLoader(train_dataset, batch_size=opt.batch_size, shuffle=True, num_workers=8)
test_dataset = PartNet(opt.data_dir, 'sem_seg_h5', opt.category, opt.level, 'test')
test_loader = DenseDataLoader(test_dataset, batch_size=opt.test_batch_size, shuffle=False, num_workers=8)
val_dataset = PartNet(opt.data_dir, 'sem_seg_h5', opt.category, opt.level, 'val')
val_loader = DenseDataLoader(val_dataset, batch_size=opt.test_batch_size, shuffle=False, num_workers=8)
opt.n_classes = train_dataset.num_classes
logging.info('===> Loading PartNet Category {}-{}, Semantic Segmentation level {}. '
'Has classes {}'.format(opt.category_no, opt.category, opt.level, opt.n_classes))
logging.info('===> Loading the network ...')
model = DeepGCN(opt).to(opt.device)
if opt.multi_gpus:
cur_shape_miou = cur_shape_iou_tot / cur_shape_iou_cnt
shape_iou_tot += cur_shape_miou
shape_iou_cnt += 1.
shape_mIoU = shape_iou_tot / shape_iou_cnt
part_iou = np.divide(part_intersect[1:], part_union[1:])
mean_part_iou = np.mean(part_iou)
opt.printer.info(
"===> Category {}-{}, Part mIOU is{:.4f} \t Shape mIoU is{:.4f} ".
format(opt.category_no, opt.category, mean_part_iou, shape_mIoU))
if __name__ == '__main__':
opt = OptInit().initialize()
opt.printer.info('===> Creating dataloader ...')
test_dataset = PartNet(opt.train_path, opt.dataset, opt.category,
opt.level, 'val')
test_loader = DenseDataLoader(test_dataset,
batch_size=1,
shuffle=True,
num_workers=1)
opt.n_classes = test_loader.dataset.num_classes
opt.printer.info('===> Loading the network ...')
model = DenseDeepGCN(opt).to(opt.device)
opt.printer.info('===> loading pre-trained ...')
model, opt.best_value, opt.epoch = load_pretrained_models(
model, opt.pretrained_model, opt.phase)
test(model, test_loader, opt)
cur_shape_iou_tot += I/U
cur_shape_iou_cnt += 1.
if cur_shape_iou_cnt > 0:
cur_shape_miou = cur_shape_iou_tot / cur_shape_iou_cnt
shape_iou_tot += cur_shape_miou
shape_iou_cnt += 1.
shape_mIoU = shape_iou_tot / shape_iou_cnt
part_iou = np.divide(part_intersect[1:], part_union[1:])
mean_part_iou = np.mean(part_iou)
logging.info("===> Finish Testing! Category {}-{}, Part mIOU is {:.4f} \n\n\n ".format(
opt.category_no, opt.category, mean_part_iou))
if __name__ == '__main__':
opt = OptInit()._get_args()
logging.info('===> Creating dataloader ...')
test_dataset = PartNet(opt.data_dir, 'sem_seg_h5', opt.category, opt.level, 'test')
test_loader = DenseDataLoader(test_dataset, batch_size=1, shuffle=True, num_workers=1)
opt.n_classes = test_loader.dataset.num_classes
logging.info('===> Loading the network ...')
model = DeepGCN(opt).to(opt.device)
logging.info('===> loading pre-trained ...')
model, opt.best_value, opt.epoch = load_pretrained_models(model, opt.pretrained_model, opt.phase)
test(model, test_loader, opt)