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import argparse |
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import logging |
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import os |
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import random |
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import sys |
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import numpy as np |
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import torch |
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import torch.backends.cudnn as cudnn |
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import torch.nn as nn |
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from torch.utils.data import DataLoader |
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from tqdm import tqdm |
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from datasets.dataset_synapse import Synapse_dataset |
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from utils import test_single_volume |
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from networks.vision_transformer import SwinUnet as ViT_seg |
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from trainer import trainer_synapse |
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from config import get_config |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--volume_path', type=str, |
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default='../data/Synapse/test_vol_h5', help='root dir for validation volume data') |
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parser.add_argument('--dataset', type=str, |
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default='Synapse', help='experiment_name') |
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parser.add_argument('--num_classes', type=int, |
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default=9, help='output channel of network') |
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parser.add_argument('--list_dir', type=str, |
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default='./lists/lists_Synapse', help='list dir') |
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parser.add_argument('--output_dir', type=str, help='output dir') |
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parser.add_argument('--max_iterations', type=int,default=30000, help='maximum epoch number to train') |
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parser.add_argument('--max_epochs', type=int, default=150, help='maximum epoch number to train') |
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parser.add_argument('--batch_size', type=int, default=24, |
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help='batch_size per gpu') |
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parser.add_argument('--img_size', type=int, default=224, help='input patch size of network input') |
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parser.add_argument('--is_savenii', action="store_true", help='whether to save results during inference') |
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parser.add_argument('--test_save_dir', type=str, default='../predictions', help='saving prediction as nii!') |
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parser.add_argument('--deterministic', type=int, default=1, help='whether use deterministic training') |
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parser.add_argument('--base_lr', type=float, default=0.01, help='segmentation network learning rate') |
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parser.add_argument('--seed', type=int, default=1234, help='random seed') |
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parser.add_argument('--cfg', type=str, required=True, metavar="FILE", help='path to config file', ) |
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parser.add_argument( |
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"--opts", |
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help="Modify config options by adding 'KEY VALUE' pairs. ", |
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default=None, |
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nargs='+', |
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) |
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parser.add_argument('--zip', action='store_true', help='use zipped dataset instead of folder dataset') |
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parser.add_argument('--cache-mode', type=str, default='part', choices=['no', 'full', 'part'], |
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help='no: no cache, ' |
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'full: cache all data, ' |
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'part: sharding the dataset into nonoverlapping pieces and only cache one piece') |
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parser.add_argument('--resume', help='resume from checkpoint') |
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parser.add_argument('--accumulation-steps', type=int, help="gradient accumulation steps") |
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parser.add_argument('--use-checkpoint', action='store_true', |
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help="whether to use gradient checkpointing to save memory") |
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parser.add_argument('--amp-opt-level', type=str, default='O1', choices=['O0', 'O1', 'O2'], |
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help='mixed precision opt level, if O0, no amp is used') |
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parser.add_argument('--tag', help='tag of experiment') |
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parser.add_argument('--eval', action='store_true', help='Perform evaluation only') |
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parser.add_argument('--throughput', action='store_true', help='Test throughput only') |
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args = parser.parse_args() |
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if args.dataset == "Synapse": |
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args.volume_path = os.path.join(args.volume_path, "test_vol_h5") |
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config = get_config(args) |
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def inference(args, model, test_save_path=None): |
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db_test = args.Dataset(base_dir=args.volume_path, split="test_vol", list_dir=args.list_dir) |
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testloader = DataLoader(db_test, batch_size=1, shuffle=False, num_workers=1) |
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logging.info("{} test iterations per epoch".format(len(testloader))) |
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model.eval() |
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metric_list = 0.0 |
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for i_batch, sampled_batch in tqdm(enumerate(testloader)): |
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h, w = sampled_batch["image"].size()[2:] |
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image, label, case_name = sampled_batch["image"], sampled_batch["label"], sampled_batch['case_name'][0] |
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metric_i = test_single_volume(image, label, model, classes=args.num_classes, patch_size=[args.img_size, args.img_size], |
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test_save_path=test_save_path, case=case_name, z_spacing=args.z_spacing) |
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metric_list += np.array(metric_i) |
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logging.info('idx %d case %s mean_dice %f mean_hd95 %f' % (i_batch, case_name, np.mean(metric_i, axis=0)[0], np.mean(metric_i, axis=0)[1])) |
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metric_list = metric_list / len(db_test) |
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for i in range(1, args.num_classes): |
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logging.info('Mean class %d mean_dice %f mean_hd95 %f' % (i, metric_list[i-1][0], metric_list[i-1][1])) |
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performance = np.mean(metric_list, axis=0)[0] |
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mean_hd95 = np.mean(metric_list, axis=0)[1] |
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logging.info('Testing performance in best val model: mean_dice : %f mean_hd95 : %f' % (performance, mean_hd95)) |
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return "Testing Finished!" |
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if __name__ == "__main__": |
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if not args.deterministic: |
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cudnn.benchmark = True |
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cudnn.deterministic = False |
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else: |
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cudnn.benchmark = False |
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cudnn.deterministic = True |
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random.seed(args.seed) |
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np.random.seed(args.seed) |
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torch.manual_seed(args.seed) |
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torch.cuda.manual_seed(args.seed) |
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dataset_config = { |
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'Synapse': { |
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'Dataset': Synapse_dataset, |
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'volume_path': args.volume_path, |
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'list_dir': './lists/lists_Synapse', |
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'num_classes': 9, |
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'z_spacing': 1, |
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}, |
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} |
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dataset_name = args.dataset |
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args.num_classes = dataset_config[dataset_name]['num_classes'] |
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args.volume_path = dataset_config[dataset_name]['volume_path'] |
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args.Dataset = dataset_config[dataset_name]['Dataset'] |
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args.list_dir = dataset_config[dataset_name]['list_dir'] |
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args.z_spacing = dataset_config[dataset_name]['z_spacing'] |
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args.is_pretrain = True |
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net = ViT_seg(config, img_size=args.img_size, num_classes=args.num_classes).cuda() |
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snapshot = os.path.join(args.output_dir, 'best_model.pth') |
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if not os.path.exists(snapshot): snapshot = snapshot.replace('best_model', 'epoch_'+str(args.max_epochs-1)) |
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msg = net.load_state_dict(torch.load(snapshot)) |
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print("self trained swin unet",msg) |
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snapshot_name = snapshot.split('/')[-1] |
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log_folder = './test_log/test_log_' |
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os.makedirs(log_folder, exist_ok=True) |
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logging.basicConfig(filename=log_folder + '/'+snapshot_name+".txt", level=logging.INFO, format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S') |
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logging.getLogger().addHandler(logging.StreamHandler(sys.stdout)) |
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logging.info(str(args)) |
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logging.info(snapshot_name) |
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if args.is_savenii: |
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args.test_save_dir = os.path.join(args.output_dir, "predictions") |
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test_save_path = args.test_save_dir |
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os.makedirs(test_save_path, exist_ok=True) |
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else: |
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test_save_path = None |
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inference(args, net, test_save_path) |
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