batch_time.update(time.time() - end)
end = time.time()
# print info
if idx % args.print_freq == 0:
logger.info(f'Train: [{epoch}][{idx}/{len(train_loader)}]\t'
f'T {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
f'DT {data_time.val:.3f} ({data_time.avg:.3f})\t'
f'loss {loss_meter.val:.3f} ({loss_meter.avg:.3f})\t'
f'prob {prob_meter.val:.3f} ({prob_meter.avg:.3f})')
return loss_meter.avg, prob_meter.avg
if __name__ == '__main__':
opt = parse_option()
torch.cuda.set_device(opt.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
cudnn.benchmark = True
os.makedirs(opt.output_dir, exist_ok=True)
logger = setup_logger(output=opt.output_dir, distributed_rank=dist.get_rank(), name="moco")
if dist.get_rank() == 0:
path = os.path.join(opt.output_dir, "config.json")
with open(path, 'w') as f:
json.dump(vars(opt), f, indent=2)
logger.info("Full config saved to {}".format(path))
main(opt)
prd_itk = sitk.GetImageFromArray(prediction.astype(np.float32))
lab_itk = sitk.GetImageFromArray(label.astype(np.float32))
img_itk.SetSpacing((1, 1, z_spacing))
prd_itk.SetSpacing((1, 1, z_spacing))
lab_itk.SetSpacing((1, 1, z_spacing))
sitk.WriteImage(prd_itk, test_save_path + '/' + case + "_pred.nii.gz")
sitk.WriteImage(img_itk, test_save_path + '/' + case + "_img.nii.gz")
sitk.WriteImage(lab_itk, test_save_path + '/' + case + "_gt.nii.gz")
return metric_list
if __name__ == '__main__':
opt = parse_option()
# torch.cuda.set_device(opt.local_rank)
# torch.distributed.init_process_group(backend='nccl', init_method='env://')
if not opt.resume:
cudnn.benchmark = True
os.makedirs(opt.output_dir, exist_ok=True)
# logger = setup_logger(output=opt.output_dir, distributed_rank=dist.get_rank(), name="moco")
logger = setup_logger(output=opt.output_dir, name="moco")
# if dist.get_rank() == 0:
path = os.path.join(opt.output_dir, "config.json")
with open(path, 'w') as f:
json.dump(vars(opt), f, indent=2)
logger.info("Full config saved to {}".format(path))
main(opt)
if __name__ == '__main__':
# to suppress annoying warnings
import warnings
warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data",
UserWarning)
opt = parse_option()
torch.cuda.set_device(opt.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
cudnn.benchmark = True
os.makedirs(opt.output_dir, exist_ok=True)
logger = setup_logger(output=opt.output_dir,
distributed_rank=dist.get_rank(),
name="imagenet-x test")
if dist.get_rank() == 0:
path = os.path.join(opt.output_dir, "config.json")
with open(path, "w") as f:
json.dump(vars(opt), f, indent=2)
logger.info("Full config saved to {}".format(path))
logger.info("PyTorch VERSION: {}".format(torch.__version__))
logger.info("CUDA VERSION: {}".format(torch.version.cuda))
logger.info("CUDNN VERSION: {}".format(torch.backends.cudnn.version()))
logger.info("GPU TYPE: {}".format(torch.cuda.get_device_name(0)))
main(opt)
