EMCAD/test_synapse.py

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_new', 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'
        # === 强行指定模型路径 ===
    snapshot = "/EMCAD/EMCAD-main/model_pth/pvt_v2_b2_EMCAD_kernel_sizes_1_3_5_dw_parallel_add_lgag_ks_3_ef2_act_mscb_relu6_loss_mutation_output_final_layer_Run1_Synapse224/pvt_v2_b2_EMCAD_kernel_sizes_1_3_5_dw_parallel_add_lgag_ks_3_ef2_act_mscb_relu6_loss_mutation_output_final_layer_Run1_pretrain_bs24_224_s2222/best.pth"
    # ======================
    
    print(f"正在加载模型: {snapshot}")
    model.load_state_dict(torch.load(snapshot))
    snapshot_name = "test_result" # 给结果起个名字

    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)