pytorch-medical-segmentation/test_ACDC.py at main · Lab-LVM/pytorch-medical-segmentation · GitHub

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import os
import sys
import logging
import argparse
import random
import numpy as np
import torch
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
from tqdm import tqdm

from utils.utils import test_single_volume
from utils.dataset_ACDC import ACDCdataset, RandomGenerator
from lib.networks import TransCASCADE, PVT_CASCADE
from lib.factory import create_model
from lib.cnn_vit_backbone import CONFIGS as CONFIGS_ViT_seg

def inference(args, model, testloader, test_save_path=None):
    logging.info("{} test iterations per epoch".format(len(testloader)))
    model.eval()
    metric_list = 0.0
    with torch.no_grad():
        for i_batch, sampled_batch in tqdm(enumerate(testloader)):
            h, w = sampled_batch["image"].size()[2:]
            image, label, case_name = sampled_batch["image"], sampled_batch["label"], sampled_batch['case_name'][0]
            metric_i = test_single_volume(args, image, label, model, classes=args.num_classes, patch_size=[args.img_size, args.img_size],
                                          test_save_path=test_save_path, case=case_name, z_spacing=args.z_spacing)
            metric_list += np.array(metric_i)
            logging.info('idx %d case %s mean_dice %f mean_hd95 %f, mean_jacard %f mean_asd %f' % (i_batch, case_name, np.mean(metric_i, axis=0)[0], np.mean(metric_i, axis=0)[1], np.mean(metric_i, axis=0)[2], np.mean(metric_i, axis=0)[3]))
        metric_list = metric_list / len(testloader)
        for i in range(1, args.num_classes):
            logging.info('Mean class (%d) mean_dice %f mean_hd95 %f, mean_jacard %f mean_asd %f' % (i, metric_list[i-1][0], metric_list[i-1][1], metric_list[i-1][2], metric_list[i-1][3]))
        performance = np.mean(metric_list, axis=0)[0]
        mean_hd95 = np.mean(metric_list, axis=0)[1]
        mean_jacard = np.mean(metric_list, axis=0)[2]
        mean_asd = np.mean(metric_list, axis=0)[3]
        logging.info('Testing performance in best val model: mean_dice : %f mean_hd95 : %f, mean_jacard : %f mean_asd : %f' % (performance, mean_hd95, mean_jacard, mean_asd))
        logging.info("Testing Finished!")
        return performance, mean_hd95, mean_jacard, mean_asd


if __name__ == "__main__":

    parser = argparse.ArgumentParser()
    parser.add_argument("--exp", type=str)
    parser.add_argument("--batch_size", default=12, help="batch size")
    parser.add_argument("--lr", default=0.0001, help="learning rate")
    parser.add_argument("--max_epochs", default=150)
    parser.add_argument("--img_size", default=224)
    parser.add_argument("--save_path", default="./model_pth/ACDC")
    parser.add_argument("--n_gpu", default=1)
    parser.add_argument("--checkpoint", default=None)
    parser.add_argument("--list_dir", default="./data/ACDC_2D/lists_ACDC")
    parser.add_argument("--root_dir", default="./data/ACDC_2D/")
    parser.add_argument("--volume_path", default="./data/ACDC_2D/test")
    parser.add_argument("--z_spacing", default=10)
    parser.add_argument("--num_classes", default=4)
    parser.add_argument('--test_save_dir', default='./predictions', help='saving prediction as nii!')
    parser.add_argument('--deterministic', type=int,  default=1,
                    help='whether use deterministic training')
    parser.add_argument('--seed', type=int,
                    default=2222, help='random seed')
    parser.add_argument('--n_skip', type=int,
                    default=3, help='using number of skip-connect, default is num')
    parser.add_argument('--vit_name', type=str,
                    default='R50-ViT-B_16', help='select one vit model')
    parser.add_argument('--vit_patches_size', type=int,
                    default=16, help='vit_patches_size, default is 16')
    parser.add_argument('--cuda', type=str, default="0")
    args = parser.parse_args()
    os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
    os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda

    if not args.deterministic:
        cudnn.benchmark = True
        cudnn.deterministic = False
    else:
        cudnn.benchmark = False
        cudnn.deterministic = True

    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    torch.cuda.manual_seed(args.seed)

    """ path setting"""
    args.is_pretrain = True
    dataset_name = "ACDC"
    snapshot_path = os.path.join(f"model_pth/{dataset_name}",args.exp)
    # args.exp = 'TransCASCADE_' + str(args.img_size)
    # args.exp = args.model + "_" + dataset_name + str(args.img_size)
    # snapshot_path = "{}/{}/{}".format(args.save_path, args.exp, 'TransCASCADE')
    # snapshot_path = snapshot_path + '_pretrain' if args.is_pretrain else snapshot_path
    # snapshot_path += '_' + args.vit_name
    # snapshot_path = snapshot_path + '_skip' + str(args.n_skip)
    # snapshot_path = snapshot_path + '_vitpatch' + str(args.vit_patches_size) if args.vit_patches_size!=16 else snapshot_path
    # snapshot_path = snapshot_path + '_epo' +str(args.max_epochs) if args.max_epochs != 30 else snapshot_path
    # snapshot_path = snapshot_path+'_bs'+str(args.batch_size)
    # snapshot_path = snapshot_path + '_lr' + str(args.lr) if args.lr != 0.01 else snapshot_path
    # snapshot_path = snapshot_path + '_'+str(args.img_size)
    # snapshot_path = snapshot_path + '_s'+str(args.seed) if args.seed!=1234 else snapshot_path

    ############# model setting #############
    config_vit = CONFIGS_ViT_seg[args.vit_name]
    config_vit.n_classes = args.num_classes
    config_vit.n_skip = args.n_skip
    config_vit.patches.size = (args.vit_patches_size, args.vit_patches_size)
    if args.vit_name.find('R50') !=-1:
        config_vit.patches.grid = (int(args.img_size/args.vit_patches_size), int(args.img_size/args.vit_patches_size))
    args.model = args.exp.split("_")[0]
    net = create_model(args, config_vit)
    # net = TransCASCADE(config_vit, img_size=args.img_size, num_classes=config_vit.n_classes).cuda()
    #net = PVT_CASCADE(n_class=config_vit.n_classes).cuda()
    snapshot = os.path.join(snapshot_path, 'best.pth')
    if not os.path.exists(snapshot): snapshot = snapshot.replace('best', 'epoch_'+str(args.max_epochs-1))
    print("Weights Path: ",snapshot)
    net.load_state_dict(torch.load(snapshot))
    snapshot_name = snapshot_path.split('/')[-1]

    ############# test log setting #############
    log_folder = 'test_log/test_log_' + args.exp
    os.makedirs(log_folder, exist_ok=True)
    logging.basicConfig(filename=log_folder + '/'+snapshot_name+".txt", level=logging.INFO, format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S')
    logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
    logging.info("="*100)
    for key, value in args.__dict__.items():
        if isinstance(value, str) or isinstance(value, int) or isinstance(value, float):
            logging.info("{:30} | {:10}".format(key, value))
    logging.info("="*100)
    logging.info(snapshot_name)
    args.test_save_dir = os.path.join(snapshot_path, args.test_save_dir)
    test_save_path = os.path.join(args.test_save_dir, args.exp, snapshot_name)
    os.makedirs(test_save_path, exist_ok=True)

    ############## datatset, dataloader ###############
    db_test =ACDCdataset(base_dir=args.volume_path,list_dir=args.list_dir, split="test")
    testloader = DataLoader(db_test, batch_size=1, shuffle=False)

    results = inference(args, net, testloader, test_save_path)