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train_semi_XNetv2_3D.py
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from torchvision import transforms, datasets
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
from torch.utils.data import DataLoader
import argparse
import time
import os
import numpy as np
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel
from torch.backends import cudnn
import random
import torchio as tio
import pywt
from torchio import transforms as T
from config.dataset_config.dataset_cfg import dataset_cfg
from config.train_test_config.train_test_config import print_train_loss_XNetv2, print_val_loss_XNetv2, print_train_eval_sup, print_val_eval_sup, save_val_best_sup_3d, print_best_sup
from config.visdom_config.visual_visdom import visdom_initialization_XNetv2, visualization_XNetv2
from config.warmup_config.warmup import GradualWarmupScheduler
from config.augmentation.online_aug import data_transform_3d
from loss.loss_function import segmentation_loss
from models.getnetwork import get_network
from dataload.dataset_3d import dataset_it
from warnings import simplefilter
simplefilter(action='ignore', category=FutureWarning)
def init_seeds(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
random.seed(seed)
np.random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(0)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_name', default='P-CT', help='P-CT, LiTS')
parser.add_argument('--sup_mark', default='20')
parser.add_argument('--unsup_mark', default='80')
parser.add_argument('-b', '--batch_size', default=1, type=int)
parser.add_argument('-e', '--num_epochs', default=300, type=int)
parser.add_argument('-s', '--step_size', default=50, type=int)
parser.add_argument('-l', '--lr', default=0.05, type=float)
parser.add_argument('-g', '--gamma', default=0.5, type=float)
parser.add_argument('-u', '--unsup_weight', default=3, type=float)
parser.add_argument('--loss', default='dice', type=str)
parser.add_argument('-w', '--warm_up_duration', default=20)
parser.add_argument('--momentum', default=0.9, type=float)
parser.add_argument('--wd', default=-5, type=float, help='weight decay pow')
parser.add_argument('--wavelet_type', default='bior1.5', help='haar, db2, bior1.5, coif1, dmey')
parser.add_argument('--train_alpha', default=[0, 0.4])
parser.add_argument('--train_beta', default=[0.5, 0.8])
parser.add_argument('--val_alpha', default=[0.2, 0.2])
parser.add_argument('--val_beta', default=[0.65, 0.65])
parser.add_argument('-i', '--display_iter', default=1, type=int)
parser.add_argument('-n', '--network', default='XNetv2_3D_min', type=str)
parser.add_argument('--local_rank', default=-1, type=int)
parser.add_argument('--rank_index', default=0, help='0, 1, 2, 3')
parser.add_argument('-v', '--vis', default=True, help='need visualization or not')
parser.add_argument('--visdom_port', default=16672, help='16672')
args = parser.parse_args()
torch.cuda.set_device(args.local_rank)
dist.init_process_group(backend='nccl', init_method='env://')
rank = torch.distributed.get_rank()
ngpus_per_node = torch.cuda.device_count()
init_seeds(rank + 1)
dataset_name = args.dataset_name
cfg = dataset_cfg(dataset_name)
print_num = 77 + (cfg['NUM_CLASSES'] - 3) * 14
print_num_minus = print_num - 2
print_num_half = int(print_num / 2 - 1)
path_trained_models = cfg['PATH_TRAINED_MODEL']+'/'+str(dataset_name)
if not os.path.exists(path_trained_models) and rank == args.rank_index:
os.mkdir(path_trained_models)
path_trained_models = path_trained_models+'/'+args.network+'-l='+str(args.lr)+'-e='+str(args.num_epochs)+'-s=' + str(args.step_size)+'-g='+str(args.gamma)+'-b='+str(args.batch_size)+'-uw='+str(args.unsup_weight)+'-w=' + str(args.warm_up_duration)+'-'+str(args.sup_mark)+'-'+str(args.unsup_mark)
if not os.path.exists(path_trained_models) and rank == args.rank_index:
os.mkdir(path_trained_models)
if args.vis and rank == args.rank_index:
visdom_env = str('Semi-XNetv2-' + str(dataset_name) + '-' + args.network + '-l=' + str(args.lr) + '-e=' + str(args.num_epochs) + '-s=' + str(args.step_size) + '-g=' + str(args.gamma) + '-b=' + str(args.batch_size)+'-uw='+str(args.unsup_weight) + '-w=' + str(args.warm_up_duration)+'-'+str(args.sup_mark)+'-'+str(args.unsup_mark))
visdom = visdom_initialization_XNetv2(env=visdom_env, port=args.visdom_port)
# Dataset
data_transform = data_transform_3d(cfg['NORMALIZE'])
dataset_train_unsup = dataset_it(
data_dir=cfg['PATH_DATASET'] + '/train_unsup_' + args.unsup_mark,
transform_1=data_transform['train'],
queue_length=cfg['QUEUE_LENGTH'],
samples_per_volume=cfg['NUM_SAMPLE_TRAIN'],
patch_size=cfg['PATCH_SIZE'],
num_workers=8,
shuffle_subjects=True,
shuffle_patches=True,
sup=False,
num_images=None
)
num_images_unsup = len(dataset_train_unsup.dataset_1)
dataset_train_sup = dataset_it(
data_dir=cfg['PATH_DATASET'] + '/train_sup_' + args.sup_mark,
transform_1=data_transform['train'],
queue_length=cfg['QUEUE_LENGTH'],
samples_per_volume=cfg['NUM_SAMPLE_TRAIN'],
patch_size=cfg['PATCH_SIZE'],
num_workers=8,
shuffle_subjects=True,
shuffle_patches=True,
sup=True,
num_images=num_images_unsup
)
dataset_val = dataset_it(
data_dir=cfg['PATH_DATASET'] + '/val',
transform_1=data_transform['val'],
queue_length=cfg['QUEUE_LENGTH'],
samples_per_volume=cfg['NUM_SAMPLE_VAL'],
patch_size=cfg['PATCH_SIZE'],
num_workers=8,
shuffle_subjects=False,
shuffle_patches=False,
sup=True,
num_images=None
)
train_sampler_unsup = torch.utils.data.distributed.DistributedSampler(dataset_train_unsup.queue_train_set_1, shuffle=True)
train_sampler_sup = torch.utils.data.distributed.DistributedSampler(dataset_train_sup.queue_train_set_1, shuffle=True)
val_sampler = torch.utils.data.distributed.DistributedSampler(dataset_val.queue_train_set_1, shuffle=False)
dataloaders = dict()
dataloaders['train_sup'] = DataLoader(dataset_train_sup.queue_train_set_1, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=0, sampler=train_sampler_sup)
dataloaders['train_unsup'] = DataLoader(dataset_train_unsup.queue_train_set_1, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=0, sampler=train_sampler_unsup)
dataloaders['val'] = DataLoader(dataset_val.queue_train_set_1, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=0, sampler=val_sampler)
num_batches = {'train_sup': len(dataloaders['train_sup']), 'train_unsup': len(dataloaders['train_unsup']), 'val': len(dataloaders['val'])}
# Model
model1 = get_network(args.network, cfg['IN_CHANNELS'], cfg['NUM_CLASSES'])
model1 = model1.cuda()
model1 = DistributedDataParallel(model1, device_ids=[args.local_rank])
dist.barrier()
# Training Strategy
criterion = segmentation_loss(args.loss, False).cuda()
optimizer1 = optim.SGD(model1.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=5*10 ** args.wd)
exp_lr_scheduler1 = lr_scheduler.StepLR(optimizer1, step_size=args.step_size, gamma=args.gamma)
scheduler_warmup1 = GradualWarmupScheduler(optimizer1, multiplier=1.0, total_epoch=args.warm_up_duration, after_scheduler=exp_lr_scheduler1)
# L H augmentation
L_H_aug = T.Compose([T.Resize(cfg['PATCH_SIZE']), T.ZNormalization(masking_method=cfg['NORMALIZE'])])
# Train & Val
since = time.time()
count_iter = 0
best_model = model1
best_result = 'Result1'
best_val_eval_list = [0 for i in range(4)]
for epoch in range(args.num_epochs):
count_iter += 1
if (count_iter - 1) % args.display_iter == 0:
begin_time = time.time()
dataloaders['train_sup'].sampler.set_epoch(epoch)
dataloaders['train_unsup'].sampler.set_epoch(epoch)
model1.train()
train_loss_sup_1 = 0.0
train_loss_sup_2 = 0.0
train_loss_sup_3 = 0.0
train_loss_unsup = 0.0
train_loss = 0.0
val_loss_sup_1 = 0.0
val_loss_sup_2 = 0.0
val_loss_sup_3 = 0.0
unsup_weight = args.unsup_weight * (epoch + 1) / args.num_epochs
dist.barrier()
dataset_train_sup = iter(dataloaders['train_sup'])
dataset_train_unsup = iter(dataloaders['train_unsup'])
for i in range(num_batches['train_sup']):
unsup_index = next(dataset_train_unsup)
img_train_unsup_1 = Variable(unsup_index['image'][tio.DATA].cuda())
img_train_unsup_2 = torch.zeros_like(img_train_unsup_1, device='cpu')
img_train_unsup_3 = torch.zeros_like(img_train_unsup_1, device='cpu')
img_train_unsup_numpy = img_train_unsup_1.cpu().detach().numpy()
for j in range(img_train_unsup_numpy.shape[0]):
img = img_train_unsup_numpy[j, 0]
img_wavelet = pywt.dwtn(img, args.wavelet_type, axes=(-3, -2, -1))
LLL, LLH, LHL, LHH, HLL, HLH, HHL, HHH = img_wavelet['aaa'], img_wavelet['aad'], img_wavelet['ada'], img_wavelet['add'], img_wavelet['daa'], img_wavelet['dad'], img_wavelet['dda'], img_wavelet['ddd']
LLL = (LLL - LLL.min()) / (LLL.max() - LLL.min()) * 255
LLH = (LLH - LLH.min()) / (LLH.max() - LLH.min()) * 255
LHL = (LHL - LHL.min()) / (LHL.max() - LHL.min()) * 255
LHH = (LHH - LHH.min()) / (LHH.max() - LHH.min()) * 255
HLL = (HLL - HLL.min()) / (HLL.max() - HLL.min()) * 255
HLH = (HLH - HLH.min()) / (HLH.max() - HLH.min()) * 255
HHL = (HHL - HHL.min()) / (HHL.max() - HHL.min()) * 255
HHH = (HHH - HHH.min()) / (HHH.max() - HHH.min()) * 255
H_ = LLH + LHL + LHH + HLL + HLH + HHL + HHH
H_ = (H_ - H_.min()) / (H_.max() - H_.min()) * 255
L_alpha = random.uniform(args.train_alpha[0], args.train_alpha[1])
L = LLL + L_alpha * H_
L = (L - L.min()) / (L.max() - L.min()) * 255
H_beta = random.uniform(args.train_beta[0], args.train_beta[1])
H = H_ + H_beta * LLL
H = (H - H.min()) / (H.max() - H.min()) * 255
L = torch.tensor(L).unsqueeze(0)
H = torch.tensor(H).unsqueeze(0)
L = L_H_aug(L)
H = L_H_aug(H)
img_train_unsup_2[j] = L
img_train_unsup_3[j] = H
img_train_unsup_2 = Variable(img_train_unsup_2.cuda())
img_train_unsup_3 = Variable(img_train_unsup_3.cuda())
torch.cuda.empty_cache()
optimizer1.zero_grad()
pred_train_unsup1, pred_train_unsup2, pred_train_unsup3 = model1(img_train_unsup_1, img_train_unsup_2, img_train_unsup_3)
max_train_unsup1 = torch.max(pred_train_unsup1, dim=1)[1].long()
max_train_unsup2 = torch.max(pred_train_unsup2, dim=1)[1].long()
max_train_unsup3 = torch.max(pred_train_unsup3, dim=1)[1].long()
loss_train_unsup = criterion(pred_train_unsup1, max_train_unsup2) + \
criterion(pred_train_unsup2, max_train_unsup1) + \
criterion(pred_train_unsup1, max_train_unsup3) + \
criterion(pred_train_unsup3, max_train_unsup1)
loss_train_unsup = loss_train_unsup * unsup_weight
loss_train_unsup.backward(retain_graph=True)
torch.cuda.empty_cache()
sup_index = next(dataset_train_sup)
img_train_sup_1 = Variable(sup_index['image'][tio.DATA].cuda())
img_train_sup_2 = torch.zeros_like(img_train_sup_1, device='cpu')
img_train_sup_3 = torch.zeros_like(img_train_sup_1, device='cpu')
img_train_sup_numpy = img_train_sup_1.cpu().detach().numpy()
for j in range(img_train_sup_numpy.shape[0]):
img = img_train_sup_numpy[j, 0]
img_wavelet = pywt.dwtn(img, args.wavelet_type)
LLL, LLH, LHL, LHH, HLL, HLH, HHL, HHH = img_wavelet['aaa'], img_wavelet['aad'], img_wavelet['ada'], img_wavelet['add'], img_wavelet['daa'], img_wavelet['dad'], img_wavelet['dda'], img_wavelet['ddd']
LLL = (LLL - LLL.min()) / (LLL.max() - LLL.min()) * 255
LLH = (LLH - LLH.min()) / (LLH.max() - LLH.min()) * 255
LHL = (LHL - LHL.min()) / (LHL.max() - LHL.min()) * 255
LHH = (LHH - LHH.min()) / (LHH.max() - LHH.min()) * 255
HLL = (HLL - HLL.min()) / (HLL.max() - HLL.min()) * 255
HLH = (HLH - HLH.min()) / (HLH.max() - HLH.min()) * 255
HHL = (HHL - HHL.min()) / (HHL.max() - HHL.min()) * 255
HHH = (HHH - HHH.min()) / (HHH.max() - HHH.min()) * 255
H_ = LLH + LHL + LHH + HLL + HLH + HHL + HHH
H_ = (H_ - H_.min()) / (H_.max() - H_.min()) * 255
L_alpha = random.uniform(args.train_alpha[0], args.train_alpha[1])
L = LLL + L_alpha * H_
L = (L - L.min()) / (L.max() - L.min()) * 255
H_beta = random.uniform(args.train_beta[0], args.train_beta[1])
H = H_ + H_beta * LLL
H = (H - H.min()) / (H.max() - H.min()) * 255
L = torch.tensor(L).unsqueeze(0)
H = torch.tensor(H).unsqueeze(0)
L = L_H_aug(L)
H = L_H_aug(H)
img_train_sup_2[j] = L
img_train_sup_3[j] = H
img_train_sup_2 = Variable(img_train_sup_2.cuda())
img_train_sup_3 = Variable(img_train_sup_3.cuda())
mask_train_sup = Variable(sup_index['mask'][tio.DATA].squeeze(1).long().cuda())
torch.cuda.empty_cache()
pred_train_sup1, pred_train_sup2, pred_train_sup3 = model1(img_train_sup_1, img_train_sup_2, img_train_sup_3)
if count_iter % args.display_iter == 0:
if i == 0:
score_list_train1 = pred_train_sup1
mask_list_train = mask_train_sup
# else:
elif 0 < i <= num_batches['train_sup'] / 64:
score_list_train1 = torch.cat((score_list_train1, pred_train_sup1), dim=0)
mask_list_train = torch.cat((mask_list_train, mask_train_sup), dim=0)
loss_train_sup1 = criterion(pred_train_sup1, mask_train_sup)
loss_train_sup2 = criterion(pred_train_sup2, mask_train_sup)
loss_train_sup3 = criterion(pred_train_sup3, mask_train_sup)
loss_train_sup = loss_train_sup1 + loss_train_sup2 + loss_train_sup3
loss_train_sup.backward()
optimizer1.step()
torch.cuda.empty_cache()
loss_train = loss_train_unsup + loss_train_sup
train_loss_unsup += loss_train_unsup.item()
train_loss_sup_1 += loss_train_sup1.item()
train_loss_sup_2 += loss_train_sup2.item()
train_loss_sup_3 += loss_train_sup3.item()
train_loss += loss_train.item()
scheduler_warmup1.step()
torch.cuda.empty_cache()
if count_iter % args.display_iter == 0:
score_gather_list_train1 = [torch.zeros_like(score_list_train1) for _ in range(ngpus_per_node)]
torch.distributed.all_gather(score_gather_list_train1, score_list_train1)
score_list_train1 = torch.cat(score_gather_list_train1, dim=0)
mask_gather_list_train = [torch.zeros_like(mask_list_train) for _ in range(ngpus_per_node)]
torch.distributed.all_gather(mask_gather_list_train, mask_list_train)
mask_list_train = torch.cat(mask_gather_list_train, dim=0)
if rank == args.rank_index:
torch.cuda.empty_cache()
print('=' * print_num)
print('| Epoch {}/{}'.format(epoch + 1, args.num_epochs).ljust(print_num_minus, ' '), '|')
train_epoch_loss_sup_1, train_epoch_loss_sup_2, train_epoch_loss_sup_3, train_epoch_loss_unsup, train_epoch_loss = print_train_loss_XNetv2(train_loss_sup_1, train_loss_sup_2, train_loss_sup_3, train_loss_unsup, train_loss, num_batches, print_num, print_num_minus)
train_eval_list_1, train_m_jc_1 = print_train_eval_sup(cfg['NUM_CLASSES'], score_list_train1, mask_list_train, print_num_minus)
torch.cuda.empty_cache()
with torch.no_grad():
model1.eval()
for i, data in enumerate(dataloaders['val']):
# if 0 <= i <= num_batches['val']:
inputs_val_1 = Variable(data['image'][tio.DATA].cuda().cuda())
inputs_val_2 = torch.zeros_like(inputs_val_1, device='cpu')
inputs_val_3 = torch.zeros_like(inputs_val_1, device='cpu')
inputs_val_numpy = inputs_val_1.cpu().detach().numpy()
for j in range(inputs_val_numpy.shape[0]):
img = inputs_val_numpy[j, 0]
img_wavelet = pywt.dwtn(img, args.wavelet_type)
LLL, LLH, LHL, LHH, HLL, HLH, HHL, HHH = img_wavelet['aaa'], img_wavelet['aad'], img_wavelet['ada'], img_wavelet['add'], img_wavelet['daa'], img_wavelet['dad'], img_wavelet['dda'], img_wavelet['ddd']
LLL = (LLL - LLL.min()) / (LLL.max() - LLL.min()) * 255
LLH = (LLH - LLH.min()) / (LLH.max() - LLH.min()) * 255
LHL = (LHL - LHL.min()) / (LHL.max() - LHL.min()) * 255
LHH = (LHH - LHH.min()) / (LHH.max() - LHH.min()) * 255
HLL = (HLL - HLL.min()) / (HLL.max() - HLL.min()) * 255
HLH = (HLH - HLH.min()) / (HLH.max() - HLH.min()) * 255
HHL = (HHL - HHL.min()) / (HHL.max() - HHL.min()) * 255
HHH = (HHH - HHH.min()) / (HHH.max() - HHH.min()) * 255
H_ = LLH + LHL + LHH + HLL + HLH + HHL + HHH
H_ = (H_ - H_.min()) / (H_.max() - H_.min()) * 255
L_alpha = random.uniform(args.val_alpha[0], args.val_alpha[1])
L = LLL + L_alpha * H_
L = (L - L.min()) / (L.max() - L.min()) * 255
H_beta = random.uniform(args.val_beta[0], args.val_beta[1])
H = H_ + H_beta * LLL
H = (H - H.min()) / (H.max() - H.min()) * 255
L = torch.tensor(L).unsqueeze(0)
H = torch.tensor(H).unsqueeze(0)
L = L_H_aug(L)
H = L_H_aug(H)
inputs_val_2[j] = L
inputs_val_3[j] = H
inputs_val_2 = Variable(inputs_val_2.cuda())
inputs_val_3 = Variable(inputs_val_3.cuda())
mask_val = Variable(data['mask'][tio.DATA].squeeze(1).long().cuda())
torch.cuda.empty_cache()
optimizer1.zero_grad()
outputs_val_1, outputs_val_2, outputs_val_3 = model1(inputs_val_1, inputs_val_2, inputs_val_3)
torch.cuda.empty_cache()
if i == 0:
score_list_val_1 = outputs_val_1
mask_list_val = mask_val
else:
score_list_val_1 = torch.cat((score_list_val_1, outputs_val_1), dim=0)
mask_list_val = torch.cat((mask_list_val, mask_val), dim=0)
loss_val_sup_1 = criterion(outputs_val_1, mask_val)
loss_val_sup_2 = criterion(outputs_val_2, mask_val)
loss_val_sup_3 = criterion(outputs_val_3, mask_val)
val_loss_sup_1 += loss_val_sup_1.item()
val_loss_sup_2 += loss_val_sup_2.item()
val_loss_sup_3 += loss_val_sup_3.item()
torch.cuda.empty_cache()
score_gather_list_val_1 = [torch.zeros_like(score_list_val_1) for _ in range(ngpus_per_node)]
torch.distributed.all_gather(score_gather_list_val_1, score_list_val_1)
score_list_val_1 = torch.cat(score_gather_list_val_1, dim=0)
mask_gather_list_val = [torch.zeros_like(mask_list_val) for _ in range(ngpus_per_node)]
torch.distributed.all_gather(mask_gather_list_val, mask_list_val)
mask_list_val = torch.cat(mask_gather_list_val, dim=0)
torch.cuda.empty_cache()
if rank == args.rank_index:
val_epoch_loss_sup_1, val_epoch_loss_sup_2, val_epoch_loss_sup_3 = print_val_loss_XNetv2(val_loss_sup_1, val_loss_sup_2, val_loss_sup_3, num_batches, print_num, print_num_minus)
val_eval_list_1, val_m_jc_1 = print_val_eval_sup(cfg['NUM_CLASSES'], score_list_val_1, mask_list_val, print_num_minus)
best_val_eval_list = save_val_best_sup_3d(best_val_eval_list, model1, val_eval_list_1, path_trained_models, 'XNetv2')
torch.cuda.empty_cache()
if args.vis:
visualization_XNetv2(visdom, epoch + 1, train_epoch_loss, train_epoch_loss_sup_1, train_epoch_loss_sup_2, train_epoch_loss_sup_3, train_epoch_loss_unsup, train_m_jc_1, val_epoch_loss_sup_1, val_epoch_loss_sup_2, val_epoch_loss_sup_3, val_m_jc_1)
print('-' * print_num)
print('| Epoch Time: {:.4f}s'.format((time.time() - begin_time) / args.display_iter).ljust(print_num_minus, ' '), '|')
torch.cuda.empty_cache()
torch.cuda.empty_cache()
if rank == args.rank_index:
time_elapsed = time.time() - since
m, s = divmod(time_elapsed, 60)
h, m = divmod(m, 60)
print('=' * print_num)
print('| Training Completed In {:.0f}h {:.0f}mins {:.0f}s'.format(h, m, s).ljust(print_num_minus, ' '), '|')
print('-' * print_num)
print_best_sup(cfg['NUM_CLASSES'], best_val_eval_list, print_num_minus)
print('=' * print_num)