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189 lines
9.9 KiB
189 lines
9.9 KiB
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 utils.dataset_synapse import Synapse_dataset
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from utils.utils import test_single_volume
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from lib.networks import EMCADNet
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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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# network related parameters
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parser.add_argument('--encoder', type=str,
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default='pvt_v2_b2', help='Name of encoder: pvt_v2_b2, pvt_v2_b0, resnet18, resnet34 ...')
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parser.add_argument('--expansion_factor', type=int,
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default=2, help='expansion factor in MSCB block')
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parser.add_argument('--kernel_sizes', type=int, nargs='+',
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default=[1, 3, 5], help='multi-scale kernel sizes in MSDC block')
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parser.add_argument('--lgag_ks', type=int,
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default=3, help='Kernel size in LGAG')
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parser.add_argument('--activation_mscb', type=str,
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default='relu6', help='activation used in MSCB: relu6 or relu')
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parser.add_argument('--no_dw_parallel', action='store_true',
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default=False, help='use this flag to disable depth-wise parallel convolutions')
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parser.add_argument('--concatenation', action='store_true',
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default=False, help='use this flag to concatenate feature maps in MSDC block')
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parser.add_argument('--no_pretrain', action='store_true',
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default=False, help='use this flag to turn off loading pretrained enocder weights')
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parser.add_argument('--pretrained_dir', type=str,
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default='./pretrained_pth/pvt/', help='path to pretrained encoder dir')
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parser.add_argument('--supervision', type=str,
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default='mutation', help='loss supervision: mutation, deep_supervision or last_layer')
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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=300, help='maximum epoch number to train')
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parser.add_argument('--batch_size', type=int, default=6,
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help='batch_size per gpu')
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parser.add_argument('--base_lr', type=float, default=0.0001, help='segmentation network learning rate')
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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", default=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('--seed', type=int, default=2222, help='random seed')
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args = parser.parse_args()
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if (args.num_classes == 14):
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classes = ['spleen', 'right kidney', 'left kidney', 'gallbladder', 'esophagus', 'liver', 'stomach', 'aorta',
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'inferior vena cava', 'portal vein and splenic vein', 'pancreas', 'right adrenal gland',
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'left adrenal gland']
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else:
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classes = ['spleen', 'right kidney', 'left kidney', 'gallbladder', 'pancreas', 'liver', 'stomach', 'aorta']
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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, nclass=args.num_classes)
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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,
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patch_size=[args.img_size, args.img_size],
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test_save_path=test_save_path, case=case_name, z_spacing=1, class_names=classes)
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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, mean_jacard %f mean_asd %f' % (
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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],
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np.mean(metric_i, axis=0)[3]))
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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) %s mean_dice %f mean_hd95 %f, mean_jacard %f mean_asd %f' % (
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i, classes[i - 1], metric_list[i - 1][0], metric_list[i - 1][1], metric_list[i - 1][2], metric_list[i - 1][3]))
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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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mean_jacard = np.mean(metric_list, axis=0)[2]
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mean_asd = np.mean(metric_list, axis=0)[3]
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logging.info(
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'Testing performance in best val model: mean_dice : %f mean_hd95 : %f, mean_jacard : %f mean_asd : %f' % (
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performance, mean_hd95, mean_jacard, mean_asd))
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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': args.list_dir,
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'num_classes': args.num_classes,
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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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print(args.no_pretrain)
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if args.concatenation:
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aggregation = 'concat'
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else:
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aggregation = 'add'
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if args.no_dw_parallel:
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dw_mode = 'series'
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else:
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dw_mode = 'parallel'
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run = 1
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args.exp = args.encoder + '_EMCAD_kernel_sizes_' + str(
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args.kernel_sizes) + '_dw_' + dw_mode + '_' + aggregation + '_lgag_ks_' + str(args.lgag_ks) + '_ef' + str(
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args.expansion_factor) + '_act_mscb_' + args.activation_mscb + '_loss_' + args.supervision + '_output_final_layer_Run' + str(
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run) + '_' + dataset_name + str(args.img_size)
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snapshot_path = "model_pth/{}/{}".format(args.exp, args.encoder + '_EMCAD_kernel_sizes_' + str(
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args.kernel_sizes) + '_dw_' + dw_mode + '_' + aggregation + '_lgag_ks_' + str(args.lgag_ks) + '_ef' + str(
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args.expansion_factor) + '_act_mscb_' + args.activation_mscb + '_loss_' + args.supervision + '_output_final_layer_Run' + str(
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run))
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snapshot_path = snapshot_path.replace('[', '').replace(']', '').replace(', ', '_')
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snapshot_path = snapshot_path + '_pretrain' if not args.no_pretrain else snapshot_path
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snapshot_path = snapshot_path + '_' + str(args.max_iterations)[
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0:2] + 'k' if args.max_iterations != 50000 else snapshot_path
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snapshot_path = snapshot_path + '_epo' + str(args.max_epochs) if args.max_epochs != 300 else snapshot_path
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snapshot_path = snapshot_path + '_bs' + str(args.batch_size)
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snapshot_path = snapshot_path + '_lr' + str(args.base_lr) if args.base_lr != 0.0001 else snapshot_path
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snapshot_path = snapshot_path + '_' + str(args.img_size)
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snapshot_path = snapshot_path + '_s' + str(args.seed) if args.seed != 1234 else snapshot_path
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model = EMCADNet(num_classes=args.num_classes, kernel_sizes=args.kernel_sizes,
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expansion_factor=args.expansion_factor, dw_parallel=not args.no_dw_parallel,
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add=not args.concatenation, lgag_ks=args.lgag_ks, activation=args.activation_mscb,
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encoder=args.encoder, pretrain=not args.no_pretrain, pretrained_dir=args.pretrained_dir)
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model.cuda()
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# 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'
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snapshot = os.path.join(snapshot_path, 'best.pth')
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if not os.path.exists(snapshot): snapshot = snapshot.replace('best', 'epoch_' + str(args.max_epochs - 1))
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model.load_state_dict(torch.load(snapshot))
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snapshot_name = snapshot_path.split('/')[-1]
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log_folder = 'test_log/test_log_' + args.exp
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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,
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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(snapshot_path, "predictions")
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test_save_path = os.path.join(args.test_save_dir, args.exp, snapshot_name + '2')
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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, model, test_save_path) |