import argparse import logging import os import random import numpy as np import torch import torch.nn as nn import torch.backends.cudnn as cudnn from lib.networks import EMCADNet from trainer import trainer_synapse parser = argparse.ArgumentParser() parser.add_argument('--root_path', type=str, default='./data/synapse/train_npz', help='root dir for data') 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('--list_dir', type=str, default='./lists/lists_Synapse', help='list dir') parser.add_argument('--num_classes', type=int, default=9, help='output channel of network') # 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=50000, 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('--n_gpu', type=int, default=1, help='total gpu') 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 __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_name = args.dataset dataset_config = { 'Synapse': { 'root_path': args.root_path, 'volume_path': args.volume_path, 'list_dir': args.list_dir, 'num_classes': args.num_classes, 'z_spacing': 1, }, } args.num_classes = dataset_config[dataset_name]['num_classes'] args.root_path = dataset_config[dataset_name]['root_path'] args.volume_path = dataset_config[dataset_name]['volume_path'] args.z_spacing = dataset_config[dataset_name]['z_spacing'] args.list_dir = dataset_config[dataset_name]['list_dir'] 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 if not os.path.exists(snapshot_path): os.makedirs(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() print('Model successfully created.') trainer = {'Synapse': trainer_synapse,} trainer[dataset_name](args, model, snapshot_path)