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d03ecd9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 | from torch.cuda.amp import GradScaler
from torch.utils.data import DataLoader
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data.distributed import DistributedSampler
from datasets.dataset import AdvancedMedicalAug
from engine_synapse import *
from models.vmunet.vmunet import LGFVMUNet
import os
import sys
os.environ["CUDA_VISIBLE_DEVICES"] = "0" # "0, 1, 2, 3"
from utils import *
from configs.config_setting_synapse import setting_config
import warnings
warnings.filterwarnings("ignore")
def main(config):
print('#----------Creating logger----------#')
sys.path.append(config.work_dir + '/')
log_dir = os.path.join(config.work_dir, 'log')
checkpoint_dir = os.path.join(config.work_dir, 'checkpoints')
resume_model = os.path.join(checkpoint_dir, 'latest.pth')
outputs = os.path.join(config.work_dir, 'outputs')
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
if not os.path.exists(outputs):
os.makedirs(outputs)
global logger
logger = get_logger('train', log_dir)
log_config_info(config, logger)
print('#----------GPU init----------#')
set_seed(config.seed)
gpu_ids = [0]# [0, 1, 2, 3]
torch.cuda.empty_cache()
gpus_type, gpus_num = torch.cuda.get_device_name(), torch.cuda.device_count()
if config.distributed:
print('#----------Start DDP----------#')
dist.init_process_group(backend='nccl', init_method='env://')
torch.cuda.manual_seed_all(config.seed)
config.local_rank = torch.distributed.get_rank()
print('#----------Preparing dataset----------#')
train_dataset = config.datasets(base_dir=config.data_path, list_dir=config.list_dir, split="train",
transform=AdvancedMedicalAug(), img_size=(config.input_size_h, config.input_size_w))
train_sampler = DistributedSampler(train_dataset, shuffle=True) if config.distributed else None
train_loader = DataLoader(train_dataset,
batch_size=config.batch_size//gpus_num if config.distributed else config.batch_size,
shuffle=(train_sampler is None),
pin_memory=True,
num_workers=config.num_workers,
sampler=train_sampler)
val_dataset = config.datasets(base_dir=config.test_path, split="test", list_dir=config.list_dir, img_size=(config.input_size_h, config.input_size_w))
val_sampler = DistributedSampler(val_dataset, shuffle=False) if config.distributed else None
val_loader = DataLoader(val_dataset,
batch_size=config.batch_size,
shuffle=False,
pin_memory=True,
num_workers=config.num_workers,
sampler=val_sampler,
drop_last=True,
)
print('#----------Prepareing Models----------#')
model_cfg = config.model_config
if config.network == 'LGF-VMUNet':
model = LGFVMUNet(
num_classes=model_cfg['num_classes'],
input_channels=model_cfg['input_channels'],
depths=model_cfg['depths'],
depths_decoder=model_cfg['depths_decoder'],
drop_path_rate=model_cfg['drop_path_rate'],
load_ckpt_path=model_cfg['load_ckpt_path'],
use_full_scale_skip=model_cfg['use_full_scale_skip'],
)
else: raise('Please prepare a right net!')
if config.distributed:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).cuda()
model = DDP(model, device_ids=[config.local_rank], output_device=config.local_rank)
else:
model = torch.nn.DataParallel(model.cuda(), device_ids=gpu_ids, output_device=gpu_ids[0])
print('#----------Prepareing loss, opt, sch and amp----------#')
criterion = config.criterion
optimizer = get_optimizer(config, model)
scheduler = get_scheduler(config, optimizer)
scaler = GradScaler()
print('#----------Set other params----------#')
min_loss = 999
start_epoch = 1
min_epoch = 1
best_dice = 0.0
best_dice_epoch = 1
early_stop_patience = 15
early_stop_counter = 0
if os.path.exists(resume_model):
print('#----------Resume Model and Other params----------#')
checkpoint = torch.load(resume_model, map_location=torch.device('cpu'))
model.module.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
saved_epoch = checkpoint['epoch']
start_epoch += saved_epoch
min_loss, min_epoch, loss = checkpoint['min_loss'], checkpoint['min_epoch'], checkpoint['loss']
best_dice = checkpoint.get('best_dice', 0.0)
best_dice_epoch = checkpoint.get('best_dice_epoch', 1)
early_stop_counter = checkpoint.get('early_stop_counter', 0)
log_info = f'resuming model from {resume_model}. resume_epoch: {saved_epoch}, min_loss: {min_loss:.4f}, best_dice: {best_dice:.4f}'
logger.info(log_info)
print('#----------Training----------#')
for epoch in range(start_epoch, config.epochs + 1):
torch.cuda.empty_cache()
train_sampler.set_epoch(epoch) if config.distributed else None
loss = train_one_epoch(
train_loader,
model,
criterion,
optimizer,
scheduler,
epoch,
logger,
config,
scaler=scaler
)
if loss < min_loss:
min_loss = loss
min_epoch = epoch
if epoch % config.val_interval == 0:
mean_dice, mean_hd95 = val_one_epochV2(val_loader, model, epoch, logger, config)
if mean_dice > best_dice:
best_dice = mean_dice
best_dice_epoch = epoch
early_stop_counter = 0
torch.save(model.module.state_dict(), os.path.join(checkpoint_dir, 'best.pth'))
log_info = f'New best model at epoch {epoch}: mean_dice={mean_dice:.4f}, mean_hd95={mean_hd95:.4f}'
print(log_info)
logger.info(log_info)
else:
early_stop_counter += config.val_interval
log_info = f'No improvement for {early_stop_counter} epochs (best={best_dice:.4f} at epoch {best_dice_epoch})'
print(log_info)
logger.info(log_info)
if early_stop_counter >= early_stop_patience:
log_info = f'Early stopping triggered at epoch {epoch}'
print(log_info)
logger.info(log_info)
break
if epoch % config.save_interval == 0:
torch.save({
'epoch': epoch,
'min_loss': min_loss,
'min_epoch': min_epoch,
'loss': loss,
'best_dice': best_dice,
'best_dice_epoch': best_dice_epoch,
'early_stop_counter': early_stop_counter,
'model_state_dict': model.module.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'scheduler_state_dict': scheduler.state_dict(),
}, os.path.join(checkpoint_dir, f'epoch_{epoch}.pth'))
torch.save(
{
'epoch': epoch,
'min_loss': min_loss,
'min_epoch': min_epoch,
'loss': loss,
'best_dice': best_dice,
'best_dice_epoch': best_dice_epoch,
'early_stop_counter': early_stop_counter,
'model_state_dict': model.module.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'scheduler_state_dict': scheduler.state_dict(),
}, os.path.join(checkpoint_dir, 'latest.pth'))
if os.path.exists(os.path.join(checkpoint_dir, 'best.pth')):
print('#----------Testing----------#')
best_weight = torch.load(config.work_dir + 'checkpoints/best.pth', map_location=torch.device('cpu'))
model.module.load_state_dict(best_weight)
mean_dice, mean_hd95 = val_one_epochV2(val_loader, model, best_dice_epoch, logger, config)
os.rename(
os.path.join(checkpoint_dir, 'best.pth'),
os.path.join(checkpoint_dir, f'best-epoch{best_dice_epoch}-mean_dice{mean_dice:.4f}-mean_hd95{mean_hd95:.4f}.pth')
)
if __name__ == '__main__':
config = setting_config
main(config) |