import argparse import logging import os import random import sys import numpy as np import torch import torch.backends.cudnn as cudnn import torch.nn as nn from torch.utils.data import DataLoader from tqdm import tqdm from utils.dataset_synapse import Synapse_dataset from utils.utils import test_single_volume from lib.networks import EMCADNet parser = argparse.ArgumentParser() parser.add_argument('--volume_path', type=str, default='./data/synapse/test_vol_h5', help='root dir for validation volume data') parser.add_argument('--dataset', type=str, default='Synapse', help='experiment_name') parser.add_argument('--num_classes', type=int, default=9, help='output channel of network') parser.add_argument('--list_dir', type=str, default='./lists/lists_Synapse', help='list dir') # network related parameters parser.add_argument('--encoder', type=str, default='pvt_v2_b2', help='Name of encoder: pvt_v2_b2, pvt_v2_b0, resnet18, resnet34 ...') parser.add_argument('--expansion_factor', type=int, default=2, help='expansion factor in MSCB block') parser.add_argument('--kernel_sizes', type=int, nargs='+', default=[1, 3, 5], help='multi-scale kernel sizes in MSDC block') parser.add_argument('--lgag_ks', type=int, default=3, help='Kernel size in LGAG') parser.add_argument('--activation_mscb', type=str, default='relu6', help='activation used in MSCB: relu6 or relu') parser.add_argument('--no_dw_parallel', action='store_true', default=False, help='use this flag to disable depth-wise parallel convolutions') parser.add_argument('--concatenation', action='store_true', default=False, help='use this flag to concatenate feature maps in MSDC block') parser.add_argument('--no_pretrain', action='store_true', default=False, help='use this flag to turn off loading pretrained enocder weights') parser.add_argument('--pretrained_dir', type=str, default='./pretrained_pth/pvt/', help='path to pretrained encoder dir') parser.add_argument('--supervision', type=str, default='mutation', help='loss supervision: mutation, deep_supervision or last_layer') parser.add_argument('--max_iterations', type=int, default=30000, help='maximum epoch number to train') parser.add_argument('--max_epochs', type=int, default=300, help='maximum epoch number to train') parser.add_argument('--batch_size', type=int, default=6, help='batch_size per gpu') parser.add_argument('--base_lr', type=float, default=0.0001, help='segmentation network learning rate') parser.add_argument('--img_size', type=int, default=224, help='input patch size of network input') parser.add_argument('--is_savenii', action="store_true", default=True, help='whether to save results during inference') parser.add_argument('--test_save_dir', type=str, 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') args = parser.parse_args() if (args.num_classes == 14): classes = ['spleen', 'right kidney', 'left kidney', 'gallbladder', 'esophagus', 'liver', 'stomach', 'aorta', 'inferior vena cava', 'portal vein and splenic vein', 'pancreas', 'right adrenal gland', 'left adrenal gland'] else: classes = ['spleen', 'right kidney', 'left kidney', 'gallbladder', 'pancreas', 'liver', 'stomach', 'aorta'] def inference(args, model, test_save_path=None): db_test = args.Dataset(base_dir=args.volume_path, split="test_vol", list_dir=args.list_dir, nclass=args.num_classes) testloader = DataLoader(db_test, batch_size=1, shuffle=False, num_workers=1) logging.info("{} test iterations per epoch".format(len(testloader))) model.eval() metric_list = 0.0 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(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=1, class_names=classes) 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(db_test) for i in range(1, args.num_classes): logging.info('Mean class (%d) %s mean_dice %f mean_hd95 %f, mean_jacard %f mean_asd %f' % ( i, classes[i - 1], 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)) return "Testing Finished!" if __name__ == "__main__": 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) dataset_config = { 'Synapse': { 'Dataset': Synapse_dataset, 'volume_path': args.volume_path, 'list_dir': args.list_dir, 'num_classes': args.num_classes, 'z_spacing': 1, }, } dataset_name = args.dataset args.num_classes = dataset_config[dataset_name]['num_classes'] args.volume_path = dataset_config[dataset_name]['volume_path'] args.Dataset = dataset_config[dataset_name]['Dataset'] args.list_dir = dataset_config[dataset_name]['list_dir'] args.z_spacing = dataset_config[dataset_name]['z_spacing'] print(args.no_pretrain) if args.concatenation: aggregation = 'concat' else: aggregation = 'add' if args.no_dw_parallel: dw_mode = 'series' else: dw_mode = 'parallel' run = 1 args.exp = args.encoder + '_EMCAD_kernel_sizes_' + str( args.kernel_sizes) + '_dw_' + dw_mode + '_' + aggregation + '_lgag_ks_' + str(args.lgag_ks) + '_ef' + str( args.expansion_factor) + '_act_mscb_' + args.activation_mscb + '_loss_' + args.supervision + '_output_final_layer_Run' + str( run) + '_' + dataset_name + str(args.img_size) snapshot_path = "model_pth/{}/{}".format(args.exp, args.encoder + '_EMCAD_kernel_sizes_' + str( args.kernel_sizes) + '_dw_' + dw_mode + '_' + aggregation + '_lgag_ks_' + str(args.lgag_ks) + '_ef' + str( args.expansion_factor) + '_act_mscb_' + args.activation_mscb + '_loss_' + args.supervision + '_output_final_layer_Run' + str( run)) snapshot_path = snapshot_path.replace('[', '').replace(']', '').replace(', ', '_') snapshot_path = snapshot_path + '_pretrain' if not args.no_pretrain else snapshot_path snapshot_path = snapshot_path + '_' + str(args.max_iterations)[ 0:2] + 'k' if args.max_iterations != 50000 else snapshot_path snapshot_path = snapshot_path + '_epo' + str(args.max_epochs) if args.max_epochs != 300 else snapshot_path snapshot_path = snapshot_path + '_bs' + str(args.batch_size) snapshot_path = snapshot_path + '_lr' + str(args.base_lr) if args.base_lr != 0.0001 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 = EMCADNet(num_classes=args.num_classes, kernel_sizes=args.kernel_sizes, expansion_factor=args.expansion_factor, dw_parallel=not args.no_dw_parallel, add=not args.concatenation, lgag_ks=args.lgag_ks, activation=args.activation_mscb, encoder=args.encoder, pretrain=not args.no_pretrain, pretrained_dir=args.pretrained_dir) model.cuda() # snapshot_path = 'model_pth/'+args.encoder+'_EMCAD_wi_normal_dw_parallel_add_Conv2D_cec_cdc1x1_dwc_cs_ef2_k_sizes_1_3_5_ag3g_relu6_up3_relu_to1_3ch_relu_loss2p4_w1_out1_nlrd_mutation_True_cds_False_cds_decoder_FalseRun'+str(run)+'_Synapse224/'+args.encoder+'_EMCAD_wi_normal_dw_parallel_add_Conv2D_cec_cdc1x1_dwc_cs_ef2_k_sizes_1_3_5_ag3g_relu6_up3_relu_to1_3ch_relu_loss2p4_w1_out1_nlrd_mutation_True_cds_False_cds_decoder_FalseRun'+str(run)+'_50k_epo300_bs6_lr0.0001_224_s2222' snapshot = os.path.join(snapshot_path, 'best.pth') if not os.path.exists(snapshot): snapshot = snapshot.replace('best', 'epoch_' + str(args.max_epochs - 1)) model.load_state_dict(torch.load(snapshot)) snapshot_name = snapshot_path.split('/')[-1] 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(str(args)) logging.info(snapshot_name) if args.is_savenii: args.test_save_dir = os.path.join(snapshot_path, "predictions") test_save_path = os.path.join(args.test_save_dir, args.exp, snapshot_name + '2') os.makedirs(test_save_path, exist_ok=True) else: test_save_path = None inference(args, model, test_save_path)