DNVF/opt_grid_diff.py at main · uci-cbcl/DNVF · GitHub

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import numpy as np
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import torch.nn.functional as nnf
import argparse
import os
from torch import optim
import sys
import torch

from utils.csv_logger import infer_csv
from data import datasets
from models.dnvf.networks.grid import Grid
from models.dnvf.networks.siren import VecInt
import utils.losses as losses
import utils.utils as utils
from utils.train_utils import (adjust_learning_rate,
                               save_checkpoint)


'''
parse command line arg
'''
parser = argparse.ArgumentParser()

parser.add_argument('--dataset', type=str, default='OASIS')
parser.add_argument('--save_dir', type=str, default='./infer_results/', help='The directory to save infer results')
parser.add_argument('--optimization_epoch', type=int, default=200)
parser.add_argument('--weight_jdet', type=int, default=50)
parser.add_argument('--downsample_factor', type=int, default=4)
parser.add_argument('--val_iter', type=int, default=5)
parser.add_argument('--lr', type=float, default=1e-3)

args = parser.parse_args()

def main():
    csv_name = 'diff_grid_jDet{}_down_{}_epoch_{}_{}'.format(args.weight_jdet, args.downsample_factor, args.optimization_epoch, args.dataset) + '.csv'

    optimization_epoch = args.optimization_epoch
    val_iter = args.val_iter
    weight_jdet = args.weight_jdet
    lr = args.lr

    save_dir = args.save_dir + args.dataset + '/' + 'grid' + '/'
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)

    '''
    initialize dataset
    '''
    test_dataset, img_size = datasets.load_test_dataset(args.dataset)
    test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=0, pin_memory=False, drop_last=False)


    """Prepare Input Grid"""
    downsample_factor = args.downsample_factor
    grid_input_shape = np.array(img_size) // downsample_factor
    grid_input = torch.zeros(1, 3, grid_input_shape[0], grid_input_shape[1], grid_input_shape[2])
    print("flow field input shape: ", grid_input.shape)

    '''
    initialize spatial transformation function
    '''
    reg_model = utils.register_model(img_size, 'nearest')
    reg_model.cuda()
    reg_model_img = utils.register_model(img_size, 'bilinear')
    reg_model_img.cuda()
    vec_int = VecInt(img_size).cuda()

    '''
    initialize optimizer and loss functions
    '''
    criterion_ncc = losses.NCC_vxm()
    criterion_reg = losses.Grad3d(penalty='l2')
    criterion_jdet = losses.NegJDet(img_size).cuda()

    """start inferring"""
    eval_dsc_def = utils.AverageMeter()
    eval_dsc_raw = utils.AverageMeter()
    eval_det = utils.AverageMeter()

    for idx, data in enumerate(test_loader):

        """Initialize model"""
        vec_field = Grid(grid_input)
        vec_field = vec_field.cuda()
        adam = optim.Adam(vec_field.parameters(), lr=lr, weight_decay=0, amsgrad=True)

        data = [t.cuda() for t in data]
        x, y, x_seg, y_seg = data
        loss_all = utils.AverageMeter()


        for epoch in range(optimization_epoch):
            vec_field.train()
            adjust_learning_rate(adam, epoch, optimization_epoch, lr)
            output_full_vec = nnf.interpolate(vec_field.flow, size=img_size, mode='trilinear', align_corners=True)
            output_full_vec[:, 0] = output_full_vec[:, 0] / 2 * (img_size[0] - 1)
            output_full_vec[:, 1] = output_full_vec[:, 1] / 2 * (img_size[1] - 1)
            output_full_vec[:, 2] = output_full_vec[:, 2] / 2 * (img_size[2] - 1)
            output_full_flow = vec_int(output_full_vec)
            warped_x = reg_model_img([x, output_full_flow])

            '''use ncc loss'''
            loss_ncc = criterion_ncc(warped_x, y)
            loss_reg = criterion_reg(output_full_flow)
            loss_jdet = criterion_jdet(output_full_flow)
            loss = loss_ncc + loss_reg + loss_jdet * weight_jdet
            loss_vals = [loss_ncc, loss_reg, loss_jdet]

            loss_all.update(loss.item(), y.numel())
            adam.zero_grad()
            loss.backward()
            adam.step()

            current_lr = adam.state_dict()['param_groups'][0]['lr']
            sys.stdout.write('\r Pair {}: Epoch [{}/{}] - loss {:.4f}, Img Sim: {:.6f}, Reg: {:.6f}, JDet: {:.6f}, lr: {:.6f}'.format(
                idx, epoch, optimization_epoch, loss.item(), loss_vals[0].item(), loss_vals[1].item(), loss_vals[2].item(), current_lr))
            sys.stdout.flush()

            if epoch % val_iter == 0 or epoch == optimization_epoch - 1:
                with torch.no_grad():
                    def_out = reg_model([x_seg.cuda().float(), output_full_flow.cuda()])
                    '''update DSC'''
                    if args.dataset == "OASIS":
                        dsc_trans = utils.dice_OASIS(def_out.long(), y_seg.long())
                        dsc_raw = utils.dice_OASIS(x_seg.long(), y_seg.long())
                    elif args.dataset == "IXI":
                        dsc_trans = utils.dice_IXI(def_out.long(), y_seg.long())
                        dsc_raw = utils.dice_IXI(x_seg.long(), y_seg.long())
                    elif args.dataset == "MindBoggle":
                        dsc_trans = utils.dice_MindBoggle(def_out.long(), y_seg.long())
                        dsc_raw = utils.dice_MindBoggle(x_seg.long(), y_seg.long())
                    elif args.dataset == "LPBA":
                        dsc_trans = utils.dice_LPBA(def_out.long(), y_seg.long())
                        dsc_raw = utils.dice_LPBA(x_seg.long(), y_seg.long())
                    elif args.dataset == "AbdomenCT":
                        dsc_trans = utils.dice_AbdomenCT(def_out.long(), y_seg.long())
                        dsc_raw = utils.dice_AbdomenCT(x_seg.long(), y_seg.long())

                    '''update Jdet'''
                    jac_det = utils.jacobian_determinant_vxm(output_full_flow.detach().cpu().numpy()[0, :, :, :, :])
                    tar = y.detach().cpu().numpy()[0, 0, :, :, :]
                    Jdet = np.sum(jac_det <= 0) / np.prod(tar.shape)

                    print('\n Val: Pair {}: Epoch [{}/{}] - DSC: {:.6f}, Jdet: {:.8f}, lr: {:.6f}\n'.format(
                idx, epoch, optimization_epoch, dsc_trans, Jdet, current_lr))

                    if epoch == optimization_epoch - 1:

                        eval_det.update(Jdet, x.size(0))
                        eval_dsc_raw.update(dsc_raw, x.size(0))
                        eval_dsc_def.update(dsc_trans, x.size(0))
                        infer_csv(save_dir, csv_name, idx, dsc_raw, dsc_trans, Jdet)
                        print()

        torch.cuda.empty_cache()
    infer_csv(save_dir, csv_name, 'avg', eval_dsc_raw.avg, eval_dsc_def.avg, eval_det.avg)
    infer_csv(save_dir, csv_name, 'std', eval_dsc_raw.std, eval_dsc_def.std, eval_det.std)


if __name__ == '__main__':
    main()