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runMRBrain.py
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runMRBrain.py
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# from __future__ import print_function
import argparse, os
import random
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import torch.optim as optim
import torch
import torch.utils.data as data_utils
from utils import *
from ganComponents import *
from nnBuildUnits import CrossEntropy2d
from nnBuildUnits import computeSampleAttentionWeight
from nnBuildUnits import adjust_learning_rate
import time
from morpologicalTransformation import denoiseImg_closing,denoiseImg_isolation
import ast
'''
This copy of code is a comprehensive gan (cnn or fcn for discriminator) for segmentation,
part of the work have been submitted to TNNLS, and the fully convolutional discriminator one is new
I also include the semi/weakly supervised learning into the framework
The Generator is the one I depicted in the TNNLS paper:
with enhanced residual module,
with view expansion,
with spatially-region convolution
with deeply supervsied mechanism
The Discriminator is the one I have described in the TNNLS paper:
A typical CNN which force the fake (segmented maps) maps' distribution to be close with the real segmented maps
In addition, I also design a sample attention mechanism which wants to play same role as focal loss, this is not
included in the TNNLS paper
I also implemented a fully convolutional network as the discriminator, in which, I injected a semi-supervised module
and the weakly-supervised module.
by Dong Nie
Jan., 2017 - present
This specific copy is for MRBrain segmentation challenge
July, 2018
'''
# Training settings
parser = argparse.ArgumentParser(description="PyTorch InfantSeg")
parser.add_argument("--gpuID",default='7',type=str, help="the GPU ID")
parser.add_argument("--isSemiSupervised", action="store_true", help="is the training semi-supervised?", default=False)
parser.add_argument("--NDim", type=int, default=3, help="the dimension of the shape, 1D, 2D or 3D?")
parser.add_argument("--in_modalities", type=int, default=3, help="the number of input modalities?")
parser.add_argument("--in_channels", type=int, default=3, help="the input channels ?")
parser.add_argument("--out_channels", type=int, default=2, help="the output channels (num of classes)?")
parser.add_argument("--isNetDFullyConv", action="store_true", help="is the netD fully convolutional network?", default=False)
parser.add_argument("--isNetDInputIncludeSource", action="store_true", help="is the input of netD including the source image or not?", default=False)
parser.add_argument("--in_channels_netD", type=int, default=2, help="the input channels for netD?")
parser.add_argument("--out_channels_netD", type=int, default=2, help="the output channels for netD?")
parser.add_argument("--given_weight", type=arg_as_list, default=[1,8], help="the given weight for each organ (bg included)")
parser.add_argument("--given_ids", type=arg_as_list, default=[0,1], help="the given organ id for each organ (bg included)")
parser.add_argument("--input_sz", type=arg_as_list, default=[16,64,64], help="the input patch size of list")
parser.add_argument("--output_sz", type=arg_as_list, default=[16,64,64], help="the output patch size of list")
parser.add_argument("--test_step_sz", type=arg_as_list, default=[2,16,16], help="the step size at testing one subject")
parser.add_argument("--isSegReg", action="store_true", help="is Seg and Reg?", default=False)
parser.add_argument("--isDiceLoss", action="store_true", help="is Dice Loss used?", default=True)
parser.add_argument("--isGeneDiceLoss", action="store_true", help="is Generalized Dice Loss used?", default=True)
parser.add_argument("--isSoftmaxLoss", action="store_true", help="is Softmax Loss used?", default=False)
parser.add_argument("--isContourLoss", action="store_true", help="is Contour Loss used?", default=False)
parser.add_argument("--isDeeplySupervised", action="store_true", help="is deeply supervised mechanism used?", default=True)
parser.add_argument("--lambda_ds1", type=float, default=0.4, help="loss coefficient for AD loss. Default=0.1")
parser.add_argument("--lambda_ds2", type=float, default=0.2, help="loss coefficient for AD loss. Default=0.1")
parser.add_argument("--isHighResolution", action="store_true", help="is high resolution used?", default=True)
parser.add_argument("--isLongConcatConnection", action="store_true", help="is the long skip connection concatenated?", default=True)
parser.add_argument("--isAttentionConcat", action="store_true", help="do we use attention based concatenation?", default=True)
parser.add_argument("--isResidualEnhancement", action="store_true", help="is residual learning operation enhanced?", default=False)
parser.add_argument("--isViewExpansion", action="store_true", help="is view expanded?", default=True)
parser.add_argument("--isSpatialDropOut", action="store_true", help="is spatial dropout used?", default=True)
parser.add_argument("--isFocalLoss", action="store_true", help="is focal loss used?", default=False)
parser.add_argument("--isAdLoss", action="store_true", help="is adversarial loss used?", default=False)
parser.add_argument("--isSampleImportanceFromAd", action="store_true", help="is sample importance from adversarial network used?", default=False)
parser.add_argument("--isConfidenceRegionAware", action="store_true", help="is voxel-wise (region) confidence-aware (like focal loss) loss used?", default=False)
parser.add_argument("--dropoutRate", type=float, default=0.2, help="Spatial Dropout Rate. Default=0.25")
parser.add_argument("--lambdaAD", type=float, default=0.05, help="loss coefficient for AD loss. Default=0.1")
parser.add_argument("--lambdaSEMI", type=float, default=0.5, help="loss coefficient for SEMI loss. Default=0.1")
parser.add_argument("--adImportance", type=float, default=0, help="Sample importance from AD network. Default=0")
parser.add_argument("--th_semi", type=float, default=0.5, help="threshold for semi-supervised learning. Default=0.2")
parser.add_argument("--adaptive_semi", action="store_true", help="adaptive thresholding for the semi-supervised learning?", default=False)
parser.add_argument("--how2normalize", type=int, default=6, help="how to normalize the data")
parser.add_argument("--batchSize", type=int, default=3, help="training batch size")
parser.add_argument("--iter_size", type=int, default=3, help="number of iterations(actually, it is batches) to update the network parameters")
parser.add_argument("--numofIters", type=int, default=100000, help="number of iterations to train for")
parser.add_argument("--showTrainLossEvery", type=int, default=100, help="number of iterations to show train loss")
parser.add_argument("--saveModelEvery", type=int, default=5000, help="number of iterations to save the model")
parser.add_argument("--showTestPerformanceEvery", type=int, default=1000, help="number of iterations to show test performance")
parser.add_argument("--lr", type=float, default=1e-3, help="Learning Rate. Default=1e-4")
parser.add_argument("--lr_netD", type=float, default=1e-4, help="Learning Rate. Default=1e-4")
parser.add_argument("--decLREvery", type=int, default=10000, help="Sets the learning rate to the initial LR decayed by momentum every n iterations, Default: n=40000")
parser.add_argument("--cuda", action="store_true", help="Use cuda?", default=True)
parser.add_argument("--resume", default="", type=str, help="Path to checkpoint (default: none)")
parser.add_argument("--resume_netD", default="", type=str, help="Path to checkpoint of discrimnator (default: none)")
parser.add_argument("--start_epoch", default=1, type=int, help="Manual epoch number (useful on restarts)")
parser.add_argument("--threads", type=int, default=1, help="Number of threads for data loader to use, Default: 1")
parser.add_argument("--momentum", default=0.9, type=float, help="Momentum, Default: 0.9")
parser.add_argument("--weight-decay", "--wd", default=1e-4, type=float, help="weight decay, Default: 1e-4")
parser.add_argument("--pretrained", default="", type=str, help="path to pretrained model (default: none)")
#parser.add_argument("--prefixModelName", default="/home/dongnie/Desktop/myPyTorch/pytorch-SRResNet23D/SegCha_3D_wce_wdice_viewExp_resEnhance_lrdcr_fullAd_0116_", type=str, help="prefix of the to-be-saved model name")
parser.add_argument("--prefixModelName", default="/shenlab/lab_stor5/dongnie/MRBRAIN18/modelFiles/SegMRBrain_3D_onlyGenDice_lr1e3_lrdce_viewExp_DS_Dp_HR_0728_", type=str, help="prefix of the to-be-saved model name")
parser.add_argument("--prefixPredictedFN", default="preSub1_MRBrain_3D_onlyGenDice_lr1e3_lrdce_viewExp_DS_Dp_HR_0728_", type=str, help="prefix of the to-be-saved predicted filename")
parser.add_argument("--isTestonAttentionRegion", action="store_true", help="Test on the attention region?", default=False)
parser.add_argument("--test_input_file_name",default='reg_T1.nii.gz',type=str, help="the input file name for testing subject")
parser.add_argument("--test_input_file_name1",default='FLAIR.nii.gz',type=str, help="the input file name for testing subject")
parser.add_argument("--test_input_file_name2",default='reg_IR.nii.gz',type=str, help="the input file name for testing subject")
parser.add_argument("--test_gt_file_name",default='segm.nii.gz',type=str, help="the ground-truth file name for testing subject")
parser.add_argument("--path_test",default='/shenlab/lab_stor5/dongnie/MRBRAIN18/training/1/pre/',type=str, help="the path for the testing nii.gz files")
parser.add_argument("--path_patients_h5",default='/shenlab/lab_stor5/dongnie/MRBRAIN18/h5data_snorm_64/h5data_train/',type=str, help="the path for the training hdf5 files")
parser.add_argument("--path_patients_h5_test",default='/shenlab/lab_stor5/dongnie/MRBRAIN18/h5data_snorm_64/h5data_val/',type=str, help="the path for the testing hdf5 files")
parser.add_argument("--path_patients_unlabeled_h5",default='/shenlab/lab_stor5/dongnie/challengeData/pelvicSegUnlabeledH5',type=str, help="the path for the training unlabeled hdf5 files")
#parser.add_argument("--path_test",default='/home/dongnie/warehouse/pelvicSeg/prostateChallenge/data',type=str, help="the path for the testing nii.gz files")
#parser.add_argument("--path_patients_h5",default='/home/dongnie/warehouse/pelvicSeg/prostateChallenge/pelvic3DSegRegContourBatchH5',type=str, help="the path for the training hdf5 files")
#parser.add_argument("--path_patients_unlabeled_h5",default='/home/dongnie/warehouse/pelvicSeg/prostateChallenge/pelvic3DSegRegContourBatchH5',type=str, help="the path for the training unlabeled hdf5 files")
#parser.add_argument("--path_patients_h5_test",default='/home/dongnie/warehouse/pelvicSeg/prostateChallenge/pelvic3DSegRegContourH5Test',type=str, help="the path for the testing hdf5 files")
global opt, model, running_loss, start, criterion_dice, criterion_CEND, data_generator_test, path_test
opt = parser.parse_args()
running_loss = 0
def main():
########################################configs####################################
#opt = parser.parse_args()
print opt
print 'test my list, opt.input_sz: ',opt.input_sz
if opt.isSemiSupervised:
trainSemiSupervisedNet()
else:
trainSupervisedNet()
'''
train supervised network
'''
def trainSupervisedNet():
given_weight = torch.FloatTensor(opt.given_weight) #note, weights for each organ
given_ids = torch.FloatTensor(opt.given_ids)
given_weight = given_weight.cuda()
given_ids = given_ids.cuda()
path_test = opt.path_test
path_patients_h5 = opt.path_patients_h5
path_patients_h5_test = opt.path_patients_h5_test
########################################configs####################################
# running_loss = 0
## step.1 prepare data flow
global data_generator_test,path_test,criterion_CEND,criterion_dice
if opt.NDim == 3:
if opt.in_modalities == 1:
data_generator = Generator_3D_patches(path_patients_h5,opt.batchSize,inputKey='dataT1',outputKey='dataSeg')
data_generator_test = Generator_3D_patches(path_patients_h5_test,opt.batchSize,inputKey='dataT1',outputKey='dataSeg')
elif opt.in_modalities == 2:
data_generator = Generator_3D_slices_variousKeys(path_patients_h5,opt.batchSize,['dataT1','dataFlair', 'dataSeg'])
data_generator_test = Generator_3D_slices_variousKeys(path_patients_h5_test,opt.batchSize,['dataT1','dataFlair','dataSeg'])
elif opt.in_modalities == 3:
data_generator = Generator_3D_slices_variousKeys(path_patients_h5, opt.batchSize, ['dataT1','dataFlair','dataIR','dataSeg'])
data_generator_test = Generator_3D_slices_variousKeys(path_patients_h5_test, opt.batchSize, ['dataT1','dataFlair','dataIR','dataSeg'])
else:
data_generator_test = Generator_2D_slices(path_patients_h5_test,opt.batchSize,inputKey='dataMR2D',outputKey='dataSeg2D')
if opt.isSegReg:
data_generator = Generator_2D_slices_variousKeys(path_patients_h5,opt.batchSize,inputKey='dataMR2D',outputKey='dataSeg2D',regKey1='dataBladder2D',regKey2='dataProstate2D',regKey3='dataRectum2D')
elif opt.isContourLoss:
data_generator = Generator_2D_slicesV1(path_patients_h5,opt.batchSize,inputKey='dataMR2D',segKey='dataSeg2D',contourKey='dataContour2D')
else:
data_generator = Generator_2D_slices(path_patients_h5,opt.batchSize,inputKey='dataMR2D',outputKey='dataSeg2D')
# inputs=Variable(torch.randn(1000,1,32,32)) #here should be tensor instead of variable
# targets=Variable(torch.randn(1000,10,1,1)) #here should be tensor instead of variable
# trainset=data_utils.TensorDataset(inputs, targets)
# trainloader = data_utils.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
# inputs=torch.randn(1000,1,32,32)
# targets=torch.LongTensor(1000)
## step.2 prepare network architecture
if opt.isSegReg:
netG = ResSegRegNet(opt.in_channels, opt.out_channels, nd=opt.NDim)
elif opt.isContourLoss:
netG = ResSegContourNet(opt.in_channels, opt.out_channels, nd=opt.NDim,
isRandomConnection=opt.isResidualEnhancement, isSmallDilation=opt.isViewExpansion,
isSpatialDropOut=opt.isSpatialDropOut, dropoutRate=opt.dropoutRate)
elif opt.isDeeplySupervised and opt.isHighResolution:
netG = HRResSegNet_DS(opt.in_channels, opt.out_channels, nd=opt.NDim,
isRandomConnection=opt.isResidualEnhancement, isSmallDilation=opt.isViewExpansion,
isSpatialDropOut=opt.isSpatialDropOut, dropoutRate=opt.dropoutRate)
elif opt.isDeeplySupervised:
netG = ResSegNet_DS(opt.in_channels, opt.out_channels, nd=opt.NDim,
isRandomConnection=opt.isResidualEnhancement, isSmallDilation=opt.isViewExpansion,
isSpatialDropOut=opt.isSpatialDropOut, dropoutRate=opt.dropoutRate)
elif opt.isHighResolution:
netG = HRResSegNet(opt.in_channels, opt.out_channels, nd=opt.NDim, isRandomConnection=opt.isResidualEnhancement,
isSmallDilation=opt.isViewExpansion, isSpatialDropOut=opt.isSpatialDropOut,
dropoutRate=opt.dropoutRate)
elif opt.isLongConcatConnection and opt.isDeeplySupervised and opt.isHighResolution:
netG = HRResUNet_DS(opt.in_channels, opt.out_channels, isRandomConnection=opt.isResidualEnhancement,
isSmallDilation=opt.isViewExpansion, isSpatialDropOut=opt.isSpatialDropOut,
dropoutRate=opt.dropoutRate, TModule='HR', FModule=opt.isAttentionConcat, nd=opt.NDim)
elif opt.isLongConcatConnection and opt.isHighResolution:
netG = UNet(opt.in_channels, opt.out_channels, TModule='HR', FModule=opt.isAttentionConcat, nd=opt.NDim)
elif opt.isLongConcatConnection:
netG = UNet(opt.in_channels, opt.out_channels, TModule=None, FModule=opt.isAttentionConcat, nd=opt.NDim)
else:
netG = ResSegNet(opt.in_channels, opt.out_channels, nd=opt.NDim, isRandomConnection=opt.isResidualEnhancement,
isSmallDilation=opt.isViewExpansion, isSpatialDropOut=opt.isSpatialDropOut,
dropoutRate=opt.dropoutRate)
# netG.apply(weights_init)
netG = netG.cuda()
if opt.isAdLoss:
if opt.isNetDFullyConv:
netD = Discriminator_my23DLRResFCN(opt.in_channels_netD, opt.out_channels_netD, nd=opt.NDim)
else:
netD = Discriminator(opt.in_channels_netD, nd=opt.NDim)
netD.apply(weights_init)
netD = netD.cuda()
optimizerD =optim.Adam(netD.parameters(),lr=opt.lr_netD)
params = list(netG.parameters())
print('len of params is ')
print(len(params))
print('size of params is ')
print(params[0].size())
optimizerG =optim.Adam(netG.parameters(),lr=opt.lr)
## step.3 prepare criterion (loss function)
# criterion_NLL2D = nn.NLLLoss2d(weight=given_weight)
if opt.NDim==2:
criterion_CEND = CrossEntropy2d(weight=given_weight)
elif opt.NDim==3:
criterion_CEND = CrossEntropy3d(weight=given_weight)
criterion_WCEND = WeightedCrossEntropy3d(weight=given_weight)
criterion_BCEND = CrossEntropy3d()
criterion_BCE2D = CrossEntropy2d()#for contours
# criterion_dice = DiceLoss4Organs(organIDs=[1,2,3], organWeights=[1,1,1])
# criterion_dice = WeightedDiceLoss4Organs()
criterion_dice = myWeightedDiceLoss4Organs(organIDs=given_ids, organWeights = given_weight)
criterion = nn.BCELoss()
criterion = criterion.cuda()
criterion_dice = criterion_dice.cuda()
criterion_CEND = criterion_CEND.cuda()
criterion_BCEND = criterion_BCEND.cuda()
criterion_BCE2D = criterion_BCE2D.cuda()
criterion_WCEND = criterion_WCEND.cuda()
if opt.isSegReg:
criterion_MSE = nn.MSELoss()
criterion_MSE = criterion_MSE.cuda()
if opt.isFocalLoss:
criterion_focal = myFocalLoss(4, alpha=given_weight, gamma=2)
criterion_focal = criterion_focal.cuda()
# softmax2d = nn.Softmax2d()
if opt.isGeneDiceLoss:
criterion_dice = GeneralizedDiceLoss4Organs(organIDs=given_ids)
criterion_dice = criterion_dice.cuda()
elif opt.isDiceLoss:
criterion_dice = myWeightedDiceLoss4Organs(organIDs=given_ids, organWeights = given_weight)
criterion_dice = criterion_dice.cuda()
## step.4 check if we should resume training using the existing models
if opt.resume:
if os.path.isfile(opt.resume):
print("=> loading checkpoint '{}'".format(opt.resume))
checkpoint = torch.load(opt.resume)
opt.start_epoch = checkpoint["epoch"] + 1
netG.load_state_dict(checkpoint["model"])
if opt.isAdLoss:
checkpoint = torch.load(opt.resume_netD)
netD.load_state_dict(checkpoint)
else:
print("=> no checkpoint found at '{}'".format(opt.resume))
########### We'd better use dataloader to load a lot of data,and we also should train several epoches###############
# running_loss = 0.0
global start
start = time.time()
for iter in range(opt.start_epoch, opt.numofIters+1):
## step.5.1 prepare training data (input, labels)
#we should consider different data to train
if opt.isAdLoss:
netD.zero_grad()
netG.zero_grad()
lossG_G = Variable(torch.FloatTensor([0]).cuda())
loss_dice = Variable(torch.FloatTensor([0]).cuda())
lossG_D = Variable(torch.FloatTensor([0]).cuda())
lossG_focal = Variable(torch.FloatTensor([0]).cuda())
lossG_contour = Variable(torch.FloatTensor([0]).cuda())
lossG_Reg = Variable(torch.FloatTensor([0]).cuda())
lossD = Variable(torch.FloatTensor([0]).cuda())
for m in range(0,opt.iter_size):
if opt.isSegReg:
inputs, labels, regGT1, regGT2, regGT3 = data_generator.next()
elif opt.isContourLoss:
inputs, labels,contours = data_generator.next()
elif opt.in_modalities==1:
inputs, labels = data_generator.next()
elif opt.in_modalities==2:
input1, input2, labels = data_generator.next()
inputs = np.concatenate((input1, input2),axis=1)
elif opt.in_modalities==3:
input1, input2, input3, labels = data_generator.next()
inputs = np.concatenate((input1, input2, input3), axis=1)
else:
assert opt.in_modalities<=3, 'ERROR: WE NEED TO NOTIFY CORRECT NUMBER OF INPUT MODALITIES'
#print inputs.size,labels.size
labels = np.squeeze(labels,axis=1)
labels = labels.astype(int)
if opt.isContourLoss:
contours = np.squeeze(contours,axis=1)
contours = contours.astype(int)
contours = torch.from_numpy(contours)
contours = contours.cuda()
contours = Variable(contours)
inputs = torch.from_numpy(inputs)
labels = torch.from_numpy(labels)
inputs = inputs.cuda()
labels = labels.cuda()
#wrap them into Variable
inputs,labels = Variable(inputs),Variable(labels)
## step.5.2 prepare the training data for adversarial training
if opt.isAdLoss:
if opt.isSegReg:
outputG, outputReg1, outputReg2, outputReg3 = netG(inputs)
elif opt.isContourLoss:
outputG,_ = netG(inputs)
elif opt.isDeeplySupervised:
outputG, outputG_path1, outputG_path2 = netG(inputs)
else:
outputG = netG(inputs)
#get the probability map of outputG
# if opt.NDim==2:
# outputG = softmax2d(outputG) #batch
# elif opt.NDim==3:
outputG = F.softmax(outputG, dim=1)
#outputG = outputG.data.max(1)[1]
#outputG = torch.squeeze(outputG) #[N,C,W,H]
#labels = labels.unsqueeze(1) #expand the 1st dim
#one hot encode for the gt labels
#ohlabels = OneHotEncode(nclass=2,nd=3)(labels.data.cpu()) #NxWxHxD->NxCxWxHxD
ohlabels = OneHotEncode(nclass=opt.out_channels,nd=opt.NDim)(labels.data.cpu()) #NxWxHxD->NxCxWxHxD
# print 'outputG: ',outputG.size(),'labels: ',ohlabels.size()
outputR = Variable(ohlabels.type(torch.FloatTensor).cuda()) #output_Real
outputG = outputG.type(torch.FloatTensor).cuda()
if opt.isNetDInputIncludeSource:
inputReal_netD = torch.cat((inputs,outputR),dim=1)
outputD_real = netD(inputReal_netD)
inputFake_netD = torch.cat((inputs,outputG),dim=1)
outputD_fake = netD(inputFake_netD)
else:
outputD_real = netD(outputR)
outputD_fake = netD(outputG)
## step.5.2 update D network: maximize log(D(x)) + log(1 - D(G(z)))
batch_size = inputs.size(0)
output_shape = outputD_real.shape
if opt.isNetDFullyConv: #if netD is FCN based
#train with real data
if opt.NDim==3:
real_label = torch.ones(output_shape[0],output_shape[2],output_shape[3],output_shape[4]).long()
fake_label = torch.zeros(output_shape[0],output_shape[2],output_shape[3],output_shape[4]).long()
else:
real_label = torch.ones(output_shape[0],output_shape[2],output_shape[3]).long()
fake_label = torch.zeros(output_shape[0],output_shape[2],output_shape[3]).long()
real_label = Variable(real_label.cuda())
loss_real = criterion_BCEND(outputD_real,real_label)
loss_real.backward()
#train with fake data
fake_label = Variable(fake_label.cuda())
loss_fake = criterion_BCEND(outputD_fake,fake_label)
loss_fake.backward()
else: #if netD is CNN based
#train with real data
real_label = torch.ones(batch_size,1)
real_label = Variable(real_label.cuda())
loss_real = criterion(outputD_real,real_label)
loss_real.backward()
#train with fake data
fake_label=torch.zeros(batch_size,1)
fake_label = Variable(fake_label.cuda())
loss_fake = criterion(outputD_fake,fake_label)
loss_fake.backward()
lossD = lossD + (loss_real + loss_fake)
##step.5.3 update G network: minimize the L1/L2 loss, maximize the D(G(x))
#we want to fool the discriminator, thus we pretend the label here to be real. Actually, we can explain from the
#view of equation (note the max and min difference for generator and discriminator)
if opt.isAdLoss:
if opt.isSegReg:
outputG, outputReg1, outputReg2, outputReg3 = netG(inputs)
elif opt.isContourLoss:
outputG,_ = netG(inputs)
elif opt.isDeeplySupervised:
outputG, outputG_path1, outputG_path2 = netG(inputs)
else:
outputG = netG(inputs)
#obtain the prediction probability maps
# if opt.NDim==2:
# outputG = softmax2d(outputG) #batach
# elif opt.NDim==3:
outputG = F.softmax(outputG, dim=1)
# outputG = outputG.data.max(1)[1]
outputG = outputG.type(torch.FloatTensor).cuda() #we directly use the probabilities: NxCxWxHxD
if opt.isNetDInputIncludeSource:
input_netD = torch.cat((inputs,outputG),dim=1)
outputD = netD(input_netD)
else:
outputD = netD(outputG)
#obtain the prediction probability maps
outputD = F.softmax(outputD, dim=1)
#compute the non-zero item probability, here we already use data, so it is not variable any more
averProbTensor = (1 - outputD.data[0].cpu())
if opt.isNetDFullyConv and opt.isConfidenceRegionAware:
confRegion = computeVoxelAttentionWeight(averProbTensor)
confRegion = Variable(confRegion.cuda(),requires_grad=False)
if opt.isSampleImportanceFromAd:
averProb = averProbTensor.mean()
adImportance = computeSampleAttentionWeight(averProb)
if opt.isNetDFullyConv:
lossG_D = lossG_D + opt.lambdaAD * criterion_BCEND(outputD, real_label) #note, for generator, the label for outputG is real
else:
lossG_D = lossG_D + opt.lambdaAD * criterion(outputD, real_label) #note, for generator, the label for outputG is real
lossG_D.backward(retain_graph=True)
if opt.isSegReg:
outputG, outputReg1, outputReg2, outputReg3 = netG(inputs)
elif opt.isContourLoss:
outputG,outputContour = netG(inputs)
elif opt.isDeeplySupervised:
outputG, outputG_path1, outputG_path2 = netG(inputs)
else:
outputG = netG(inputs) #here I am not sure whether we should use twice or not
if opt.isSoftmaxLoss:
if opt.isNetDFullyConv and opt.isConfidenceRegionAware:
lossG_G_main = criterion_WCEND(outputG,torch.squeeze(labels,dim=1),confRegion)
elif opt.isSampleImportanceFromAd:
lossG_G_main = (1+adImportance) * criterion_CEND(outputG,torch.squeeze(labels,dim=1))
else:
lossG_G_main = criterion_CEND(outputG,torch.squeeze(labels,dim=1))
if opt.isDeeplySupervised:
lossG_G_path1 = opt.lambda_ds1*criterion_CEND(outputG_path1,torch.squeeze(labels,dim=1))
lossG_G_path2 = opt.lambda_ds2*criterion_CEND(outputG_path2,torch.squeeze(labels,dim=1))
lossG_G = lossG_G + (lossG_G_main + lossG_G_path1 + lossG_G_path2)
else:
lossG_G = lossG_G + lossG_G_main
lossG_G.backward(retain_graph=True) #compute gradients
if opt.isDiceLoss:
# criterion_dice = myWeightedDiceLoss4Organs(organIDs=[0,1,2,3], organWeights=[1,4,8,6])
if opt.isSampleImportanceFromAd:
loss_dice_main = (1+adImportance) * criterion_dice(outputG,torch.squeeze(labels,dim=1))
else:
loss_dice_main = criterion_dice(outputG,torch.squeeze(labels,dim=1))
if opt.isDeeplySupervised:
loss_dice_path1 = opt.lambda_ds1*criterion_dice(outputG_path1,torch.squeeze(labels,dim=1))
loss_dice_path2 = opt.lambda_ds2*criterion_dice(outputG_path2,torch.squeeze(labels,dim=1))
loss_dice = loss_dice + (loss_dice_main + loss_dice_path1 + loss_dice_path2)
else:
loss_dice = loss_dice + loss_dice_main
# loss_dice = myDiceLoss4Organs(outputG,torch.squeeze(labels)) #succeed
# loss_dice.backward(retain_graph=True) #compute gradients for dice loss
loss_dice.backward(retain_graph=True) #compute gradients for dice loss
if opt.isContourLoss:
lossG_contour = lossG_contour + criterion_BCE2D(outputContour,contours)
lossG_contour.backward(retain_graph=True)
if opt.isSegReg:
lossG_Reg1 = criterion_MSE(outputReg1, regGT1)
lossG_Reg2 = criterion_MSE(outputReg2, regGT2)
lossG_Reg3 = criterion_MSE(outputReg3, regGT3)
lossG_Reg = lossG_Reg + (lossG_Reg1 + lossG_Reg2 + lossG_Reg3)
lossG_Reg.backward()
if opt.isFocalLoss:
lossG_focal = lossG_focal + criterion_focal(outputG,torch.squeeze(labels,dim=1))
lossG_focal.backward(retain_graph=True) #compute gradients
#update network parameters after iter_size weight computation
if opt.isAdLoss:
optimizerD.step()
optimizerG.step()
#empty memory
#del outputD
#del outputG
del inputs
del labels
if opt.isAdLoss:
showTestStatistics(netG, lossG_G/opt.iter_size, loss_dice/opt.iter_size, lossG_D/opt.iter_size, lossG_focal/opt.iter_size, lossG_contour/opt.iter_size, lossG_Reg/opt.iter_size, lossD/opt.iter_size, iter, netD=netD)
else:
showTestStatistics(netG, lossG_G/opt.iter_size, loss_dice/opt.iter_size, lossG_D/opt.iter_size, lossG_focal/opt.iter_size, lossG_contour/opt.iter_size, lossG_Reg/opt.iter_size, lossD/opt.iter_size, iter)
#showTestStatistics(netG, lossG_G, loss_dice, lossG_D, lossG_focal, lossG_contour, lossG_Reg, lossD, iter, netD=netD)
if iter%opt.decLREvery==0 and iter>0:
opt.lr = opt.lr*0.5
adjust_learning_rate(optimizerG, opt.lr)
print 'now the learning rate is {}'.format(opt.lr)
print('Finished Training')
'''
A function to show the test statistics during training
a).show training loss
b).show validation loss
c).show accuracy performance on test subjects
'''
def showTestStatistics(netG, lossG_G, loss_dice, lossG_D, lossG_focal, lossG_contour, lossG_Reg, lossD, iter, labeled=True, netD=None):
dsc = -1
if labeled:
if opt.isDiceLoss and opt.isSoftmaxLoss and opt.isAdLoss and opt.isSegReg and opt.isFocalLoss:
lossG = opt.lambdaAD * lossG_D + lossG_G+loss_dice.data[0] + lossG_Reg + lossG_focal
elif opt.isDiceLoss and opt.isSoftmaxLoss and opt.isSegReg and opt.isFocalLoss:
lossG = lossG_G+loss_dice.data[0] + lossG_Reg + lossG_focal
elif opt.isDiceLoss and opt.isFocalLoss and opt.isAdLoss and opt.isSegReg:
lossG = opt.lambdaAD * lossG_D + lossG_focal + loss_dice.data[0] + lossG_Reg
elif opt.isDiceLoss and opt.isFocalLoss and opt.isSegReg:
lossG = lossG_focal + loss_dice.data[0] + lossG_Reg
elif opt.isDiceLoss and opt.isSoftmaxLoss and opt.isAdLoss and opt.isSegReg:
lossG = opt.lambdaAD * lossG_D + lossG_G+loss_dice.data[0] + lossG_Reg
elif opt.isDiceLoss and opt.isSoftmaxLoss and opt.isSegReg:
lossG = lossG_G+loss_dice.data[0] + lossG_Reg
elif opt.isSoftmaxLoss and opt.isAdLoss and opt.isSegReg:
lossG = opt.lambdaAD * lossG_D + lossG_G + lossG_Reg
elif opt.isSoftmaxLoss and opt.isSegReg:
lossG = lossG_G + lossG_Reg
elif opt.isDiceLoss and opt.isAdLoss and opt.isSegReg:
lossG = opt.lambdaAD * lossG_D + loss_dice.data[0] + lossG_Reg
elif opt.isDiceLoss and opt.isSegReg:
lossG = loss_dice.data[0] + lossG_Reg
elif opt.isDiceLoss and opt.isSoftmaxLoss and opt.isAdLoss:
lossG = opt.lambdaAD * lossG_D + lossG_G + loss_dice.data[0]
elif opt.isDiceLoss and opt.isSoftmaxLoss:
lossG = lossG_G + loss_dice.data[0]
elif opt.isDiceLoss and opt.isFocalLoss and opt.isAdLoss:
lossG = opt.lambdaAD * lossG_D + lossG_focal + loss_dice.data[0]
elif opt.isDiceLoss and opt.isFocalLoss:
lossG = lossG_focal + loss_dice.data[0]
elif opt.isSoftmaxLoss and opt.isAdLoss:
lossG = opt.lambdaAD * lossG_D + lossG_G
elif opt.isSoftmaxLoss:
lossG = lossG_G
elif opt.isFocalLoss and opt.isAdLoss:
lossG = opt.lambdaAD * lossG_D + lossG_focal
elif opt.isFocalLoss:
lossG = lossG_focal
elif opt.isDiceLoss and opt.isAdLoss:
lossG = opt.lambdaAD * lossG_D + loss_dice.data[0]
elif opt.isDiceLoss:
lossG = loss_dice
else:
if opt.isAdLoss:
lossG = lossG_D
#print('loss for generator is %f'%lossG.data[0])
#print statistics
global running_loss, start
running_loss = running_loss + lossG.data[0]
# print 'running_loss is ',running_loss,' type: ',type(running_loss)
# print type(outputD_fake.cpu().data[0].numpy())
if iter%opt.showTrainLossEvery==0: #print every 2000 mini-batches
print '************************************************'
print 'time now is: ' + time.asctime(time.localtime(time.time()))
#if opt.isAdLoss:
# print 'the outputD_real for iter {}'.format(iter), ' is ',outputD_real.cpu().data[0].numpy()[0]
# print 'the outputD_fake for iter {}'.format(iter), ' is ',outputD_fake.cpu().data[0].numpy()[0]
# print 'running loss is ',running_loss
print 'average running loss for generator between iter [%d, %d] is: %.3f'%(iter - 100 + 1,iter,running_loss/100)
if opt.isAdLoss:
print 'loss for discriminator at iter ',iter, ' is %f'%lossD.data[0]
print 'total loss for generator at iter ',iter, ' is %f'%lossG.data[0]
if opt.isDiceLoss and opt.isSoftmaxLoss and opt.isAdLoss and opt.isSegReg:
print 'lossG_D, lossG_G and loss_dice loss_Reg are %.2f, %.2f and %.2f respectively.'%(lossG_D.data[0], lossG_G.data[0], loss_dice.data[0], lossG_Reg.data[0])
elif opt.isDiceLoss and opt.isSoftmaxLoss and opt.isAdLoss:
print 'lossG_D, lossG_G and loss_dice are %.2f, %.2f and %.2f respectively.'%(lossG_D.data[0], lossG_G.data[0], loss_dice.data[0])
elif opt.isDiceLoss and opt.isSoftmaxLoss:
print 'lossG_G and loss_dice are %.2f and %.2f respectively.'%(lossG_G.data[0], loss_dice.data[0])
elif opt.isDiceLoss and opt.isFocalLoss and opt.isAdLoss:
print 'lossG_D, lossG_focal and loss_dice are %.2f, %.2f and %.2f respectively.'%(lossG_D.data[0], lossG_focal.data[0], loss_dice.data[0])
elif opt.isSoftmaxLoss and opt.isAdLoss:
print 'lossG_D and lossG_G are %.2f and %.2f respectively.'%(lossG_D.data[0], lossG_G.data[0])
elif opt.isFocalLoss and opt.isAdLoss:
print 'lossG_D and lossG_focal are %.2f and %.2f respectively.'%(lossG_D.data[0], lossG_focal.data[0])
elif opt.isDiceLoss and opt.isAdLoss:
print 'lossG_D and loss_dice are %.2f and %.2f respectively.'%(lossG_D.data[0], loss_dice.data[0])
if opt.isContourLoss:
print 'lossG_contour is {}'.format(lossG_contour.data[0])
print 'cost time for iter [%d, %d] is %.2f'%(iter - 100 + 1,iter, time.time()-start)
print '************************************************'
running_loss = 0.0
start = time.time()
if iter%opt.saveModelEvery==0: #save the model
state={
'epoch': iter + 1,
#'state_dict': netG.state_dict(),
'model': netG.state_dict(),
}
torch.save(state,opt.prefixModelName+'%d.pt'%iter)
if opt.isAdLoss:
torch.save(netD.state_dict(), opt.prefixModelName+'netD_%d.pt'%iter)
print 'save model: '+opt.prefixModelName+'%d.pt'%iter
if iter%opt.showTestPerformanceEvery==0: #test one subject
# to test on the validation dataset in the format of h5
if opt.in_modalities == 1:
inputs, labels = data_generator_test.next()
elif opt.in_modalities == 2:
input1, input2, labels = data_generator_test.next()
inputs = np.concatenate((input1, input2), axis=1)
elif opt.in_modalities == 3:
input1, input2, input3, labels = data_generator_test.next()
inputs = np.concatenate((input1, input2, input3), axis=1)
else:
assert opt.in_modalities <= 3, 'ERROR: WE NEED TO NOTIFY CORRECT NUMBER OF INPUT MODALITIES'
# print inputs.size,labels.size
labels = np.squeeze(labels,axis=1)
labels = labels.astype(int)
inputs = torch.from_numpy(inputs)
labels = torch.from_numpy(labels)
inputs = inputs.cuda()
labels = labels.cuda()
inputs,labels = Variable(inputs),Variable(labels)
if opt.isSegReg:
outputG, outputReg1, outputReg2, outputReg3 = netG(inputs)
elif opt.isContourLoss:
outputG,_ = netG(inputs)
elif opt.isDeeplySupervised:
outputG, outputG_path1, outputG_path2 = netG(inputs)
else:
outputG = netG(inputs) #here I am not sure whether we should use twice or not
lossG_G = criterion_CEND(outputG,torch.squeeze(labels,dim=1))
loss_dice = criterion_dice(outputG,torch.squeeze(labels,dim=1))
del outputG
print '.......come to validation stage: iter {}'.format(iter),'........'
print 'lossG_G and loss_dice are %.2f and %.2f respectively.'%(lossG_G.data[0], loss_dice.data[0])
####release all the unoccupied memory####
torch.cuda.empty_cache()
if opt.in_modalities==3:
t1_test_itk = sitk.ReadImage(os.path.join(path_test,opt.test_input_file_name))
flair_test_itk = sitk.ReadImage(os.path.join(path_test,opt.test_input_file_name1))
ir_test_itk = sitk.ReadImage(os.path.join(path_test,opt.test_input_file_name2))
t1np = sitk.GetArrayFromImage(t1_test_itk)
flairnp = sitk.GetArrayFromImage(flair_test_itk)
irnp = sitk.GetArrayFromImage(ir_test_itk)
else:
t1_test_itk = sitk.ReadImage(os.path.join(path_test,opt.test_input_file_name))
t1np = sitk.GetArrayFromImage(t1_test_itk)
ct_test_itk = sitk.ReadImage(os.path.join(path_test,opt.test_gt_file_name))
ctnp = sitk.GetArrayFromImage(ct_test_itk)
mrnp = t1np
mu = np.mean(mrnp)
#for training data in pelvicSeg
if opt.how2normalize == 1:
maxV, minV=np.percentile(mrnp, [99 ,1])
print 'maxV,',maxV,' minV, ',minV
mrnp=(mrnp-mu)/(maxV-minV)
print 'unique value: ',np.unique(ctnp)
#for training data in pelvicSeg
elif opt.how2normalize == 2:
maxV, minV = np.percentile(mrnp, [99 ,1])
print 'maxV,',maxV,' minV, ',minV
mrnp = (mrnp-mu)/(maxV-minV)
print 'unique value: ',np.unique(ctnp)
#for training data in pelvicSegRegH5
elif opt.how2normalize== 3:
std = np.std(mrnp)
mrnp = (mrnp - mu)/std
print 'maxV,',np.ndarray.max(mrnp),' minV, ',np.ndarray.min(mrnp)
elif opt.how2normalize== 4:
maxV, minV = np.percentile(mrnp, [99.2 ,1])
print 'maxV is: ',np.ndarray.max(mrnp)
mrnp[np.where(mrnp>maxV)] = maxV
print 'maxV is: ',np.ndarray.max(mrnp)
mu=np.mean(mrnp)
std = np.std(mrnp)
mrnp = (mrnp - mu)/std
print 'maxV,',np.ndarray.max(mrnp),' minV, ',np.ndarray.min(mrnp)
if opt.how2normalize == 6:
maxPercentT1, minPercentT1 = np.percentile(t1np, [99.5, 0])
maxPercentFlair, minPercentFlair = np.percentile(flairnp, [99.5, 0])
maxPercentIR, minPercentIR = np.percentile(irnp, [99.5, 0])
print 'maxPercentT1: ',maxPercentT1, ' minPercentT1: ',minPercentT1, ' maxPercentFlair: ',maxPercentFlair, 'minPercentFlair: ', minPercentFlair,' maxPercentIR: ',maxPercentIR, ' minPercentIR: ',minPercentIR
matT1 = (t1np - minPercentT1)/(maxPercentT1 - minPercentT1)
matFlair = (flairnp - minPercentFlair) / (maxPercentFlair - minPercentFlair)
matIR = (irnp - minPercentIR) / (maxPercentIR - minPercentIR)
print 'maxT1: ',np.amax(matT1), ' maxFlair: ', np.amax(matFlair), ' maxIR: ', np.amax(matIR)
print 'minT1: ', np.amin(matT1), ' minFlair: ', np.amin(matFlair), ' minIR: ', np.amin(matIR)
# full image version with average over the overlapping regions
mat1 = np.expand_dims(matT1,axis=0)
mat2 = np.expand_dims(matFlair, axis=0)
mat3 = np.expand_dims(matIR, axis=0)
matFA = np.concatenate((mat1, mat2, mat3),axis=0)
matGT = ctnp
# the attention regions
row,col,leng = mrnp.shape
if opt.isTestonAttentionRegion:
# the attention regions
y1 = int (leng * 0.25)
y2 = int (leng * 0.75)
x1 = int (col * 0.25)
x2 = int(col * 0.75)
matFA = matFA[:,y1:y2,x1:x2] #note, matFA and matFAOut same size
matGT = ctnp[:,y1:y2,x1:x2]
else:
matFA = matFA
matGT = ctnp
# volFA = sitk.GetImageFromArray(matFA)
# sitk.WriteImage(volFA,'volFA'+'.nii.gz')
# volGT = sitk.GetImageFromArray(matGT)
# sitk.WriteImage(volGT,'volGT'+'.nii.gz')
print 'matFA.shape: ',matFA.shape
matOut,_ = testOneSubject(matFA,matGT, opt.out_channels, opt.input_sz, opt.output_sz, opt.test_step_sz, netG,opt.prefixModelName+'%d.pt'%iter, nd=opt.NDim)
ct_estimated = np.zeros([ctnp.shape[0],ctnp.shape[1],ctnp.shape[2]])
print 'matOut shape: ',matOut.shape
if opt.isTestonAttentionRegion:
ct_estimated[:, y1:y2, x1:x2] = matOut
else:
ct_estimated = matOut
ct_estimated = np.rint(ct_estimated)
ct_estimated = denoiseImg_closing(ct_estimated, kernel=np.ones((20,20,20)))
ct_estimated = denoiseImg_isolation(ct_estimated, struct=np.ones((3,3,3)))
diceBladder = dice(ct_estimated,ctnp,1)
# diceProstate = dice(ct_estimated,ctnp,2)
# diceRectumm = dice(ct_estimated,ctnp,3)
print 'pred: ',ct_estimated.dtype, ' shape: ',ct_estimated.shape
print 'gt: ',ctnp.dtype,' shape: ',ct_estimated.shape
#print 'dice1 = ',diceBladder,' dice2= ',diceProstate,' dice3= ',diceRectumm
print 'dice1 = ',diceBladder
volout = sitk.GetImageFromArray(ct_estimated)
sitk.WriteImage(volout,opt.prefixPredictedFN+'{}'.format(iter)+'.nii.gz')
# netG.save_state_dict('Segmentor_model_%d.pt'%iter)
# netD.save_state_dic('Discriminator_model_%d.pt'%iter)
dsc = diceBladder
return dsc
'''
semi-supervised network
'''
def trainSemiSupervisedNet():
given_weight = torch.FloatTensor(opt.given_weight) #note, weights for each organ
given_ids = torch.FloatTensor(opt.given_ids)
given_weight = given_weight.cuda()
given_ids = given_ids.cuda()
path_test = opt.path_test
path_patients_h5 = opt.path_patients_h5
path_patients_unlabeled_h5 = opt.path_patients_unlabeled_h5
path_patients_h5_test = opt.path_patients_h5_test
########################################configs####################################
global data_generator_test,path_test,criterion_CEND,criterion_dice
## step.1 prepare data flow
if opt.NDim == 3:
data_generator = Generator_3D_patches(path_patients_h5,opt.batchSize,inputKey='dataMR',outputKey='dataSeg')
data_generator_test = Generator_3D_patches(path_patients_h5_test,opt.batchSize,inputKey='dataMR',outputKey='dataSeg')
else:
data_generator_test = Generator_2D_slices(path_patients_h5_test,opt.batchSize,inputKey='dataMR2D',outputKey='dataSeg2D')
if opt.isSegReg:
data_generator = Generator_2D_slices_variousKeys(path_patients_h5,opt.batchSize,inputKey='dataMR2D',outputKey='dataSeg2D',regKey1='dataBladder2D',regKey2='dataProstate2D',regKey3='dataRectum2D')
elif opt.isContourLoss:
data_generator = Generator_2D_slicesV1(path_patients_h5,opt.batchSize,inputKey='dataMR2D',segKey='dataSeg2D',contourKey='dataContour2D')
else:
data_generator = Generator_2D_slices(path_patients_h5,opt.batchSize,inputKey='dataMR2D',outputKey='dataSeg2D')
if opt.isSemiSupervised:
data_generator_unlabeled = Generator_3D_patches_unlabeled(path_patients_unlabeled_h5,opt.batchSize,inputKey='dataMR')
# inputs=Variable(torch.randn(1000,1,32,32)) #here should be tensor instead of variable
# targets=Variable(torch.randn(1000,10,1,1)) #here should be tensor instead of variable
# trainset=data_utils.TensorDataset(inputs, targets)
# trainloader = data_utils.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
# inputs=torch.randn(1000,1,32,32)
# targets=torch.LongTensor(1000)
## step.2 prepare network architecture
if opt.isSegReg:
negG = ResSegRegNet(opt.in_channels, opt.out_channels, nd=opt.NDim)
elif opt.isContourLoss:
netG = ResSegContourNet(opt.in_channels, opt.out_channels, nd=opt.NDim, isRandomConnection=opt.isResidualEnhancement,isSmallDilation=opt.isViewExpansion, isSpatialDropOut=opt.isSpatialDropOut,dropoutRate=opt.dropoutRate)
elif opt.isDeeplySupervised and opt.isHighResolution:
netG = HRResSegNet_DS(opt.in_channels, opt.out_channels, nd=opt.NDim, isRandomConnection=opt.isResidualEnhancement,isSmallDilation=opt.isViewExpansion, isSpatialDropOut=opt.isSpatialDropOut,dropoutRate=opt.dropoutRate)
elif opt.isDeeplySupervised:
netG = ResSegNet_DS(opt.in_channels, opt.out_channels, nd=opt.NDim, isRandomConnection=opt.isResidualEnhancement,isSmallDilation=opt.isViewExpansion, isSpatialDropOut=opt.isSpatialDropOut,dropoutRate=opt.dropoutRate)
elif opt.isHighResolution:
netG = HRResSegNet(opt.in_channels, opt.out_channels, nd=opt.NDim, isRandomConnection=opt.isResidualEnhancement, isSmallDilation=opt.isViewExpansion, isSpatialDropOut=opt.isSpatialDropOut,dropoutRate=opt.dropoutRate)
else:
netG = ResSegNet(opt.in_channels, opt.out_channels, nd=opt.NDim, isRandomConnection=opt.isResidualEnhancement,isSmallDilation=opt.isViewExpansion, isSpatialDropOut=opt.isSpatialDropOut,dropoutRate=opt.dropoutRate)
#netG.apply(weights_init)
netG.cuda()
if opt.isAdLoss:
if opt.isNetDFullyConv:
netD = Discriminator_my23DLRResFCN(opt.in_channels_netD, opt.out_channels_netD, nd=opt.NDim)
else:
netD = Discriminator(opt.in_channels_netD, opt.out_channels_netD, nd=opt.NDim)
netD.apply(weights_init)
netD.cuda()
optimizerD =optim.Adam(netD.parameters(),lr=opt.lr_netD)
params = list(netG.parameters())
print('len of params is ')
print(len(params))
print('size of params is ')
print(params[0].size())
optimizerG =optim.Adam(netG.parameters(),lr=opt.lr)
## step.3 prepare criterion (loss function)
# criterion_NLL2D = nn.NLLLoss2d(weight=given_weight)
if opt.NDim==2:
criterion_CEND = CrossEntropy2d(weight=given_weight)
elif opt.NDim==3:
criterion_CEND = CrossEntropy3d(weight=given_weight)
criterion_BCEND = CrossEntropy3d()
criterion_WCEND = WeightedCrossEntropy3d(weight=given_weight)
criterion_BCE2D = CrossEntropy2d()#for contours
# criterion_dice = DiceLoss4Organs(organIDs=[1,2,3], organWeights=[1,1,1])
# criterion_dice = WeightedDiceLoss4Organs()
criterion = nn.BCELoss()
criterion = criterion.cuda()
criterion_CEND = criterion_CEND.cuda()
criterion_WCEND = criterion_WCEND.cuda()
criterion_BCEND = criterion_BCEND.cuda()
criterion_BCE2D = criterion_BCE2D.cuda()
if opt.isSegReg:
criterion_MSE = nn.MSELoss()
criterion_MSE = criterion_MSE.cuda()
if opt.isGeneDiceLoss:
criterion_dice = GeneralizedDiceLoss4Organs(organIDs=given_ids)
criterion_dice = criterion_dice.cuda()
elif opt.isDiceLoss:
criterion_dice = myWeightedDiceLoss4Organs(organIDs=given_ids, organWeights = given_weight)
criterion_dice = criterion_dice.cuda()
if opt.isFocalLoss:
criterion_focal = myFocalLoss(4, alpha=given_weight, gamma=2)
criterion_focal = criterion_focal.cuda()
# softmax2d = nn.Softmax2d()
## step.4 check if we should resume training using the existing models
if opt.resume:
if os.path.isfile(opt.resume):
print("=> loading checkpoint '{}'".format(opt.resume))
checkpoint = torch.load(opt.resume)
opt.start_epoch = checkpoint["epoch"] + 1
netG.load_state_dict(checkpoint["model"])
if opt.isAdLoss:
netD.load_state_dict(torch.load(opt.resume_netD))
else:
print("=> no checkpoint found at '{}'".format(opt.resume))
########### We'd better use dataloader to load a lot of data,and we also should train several epoches###############
# running_loss = 0.0
global start
start = time.time()
currDSC = 0
for iter in range(opt.start_epoch, opt.numofIters+1):
netD.zero_grad()
netG.zero_grad()
lossG_G = Variable(torch.FloatTensor([0]).cuda())
loss_dice = Variable(torch.FloatTensor([0]).cuda())
lossG_D = Variable(torch.FloatTensor([0]).cuda())
lossG_D_ADV = Variable(torch.FloatTensor([0]).cuda())
lossG_focal = Variable(torch.FloatTensor([0]).cuda())
lossG_contour = Variable(torch.FloatTensor([0]).cuda())
lossG_Reg = Variable(torch.FloatTensor([0]).cuda())
lossD = Variable(torch.FloatTensor([0]).cuda())
for m in range(0, opt.iter_size):
if random.random() <0.5:
loader = data_generator
labeled = True
else:
loader = data_generator_unlabeled
labeled = False
if labeled: #for labeled cases
## step.5.1 prepare training data (input, labels)
#we should consider different data to train
if opt.isSegReg:
inputs,labels, regGT1, regGT2, regGT3 = data_generator.next()
elif opt.isContourLoss:
inputs,labels,contours = data_generator.next()
else:
inputs,labels = data_generator.next()
#print inputs.size,labels.size
labels = np.squeeze(labels,axis=1)
labels = labels.astype(int)
if opt.isContourLoss:
contours = np.squeeze(contours,axis=1)
contours = contours.astype(int)
contours = torch.from_numpy(contours)
contours = contours.cuda()
contours = Variable(contours)
inputs = torch.from_numpy(inputs)
labels = torch.from_numpy(labels)
inputs = inputs.cuda()
labels = labels.cuda()
#wrap them into Variable
inputs, labels = Variable(inputs),Variable(labels)
## step.5.2 prepare the training data for adversarial training
if opt.isAdLoss:
if opt.isSegReg:
outputG, outputReg1, outputReg2, outputReg3 = netG(inputs)
elif opt.isContourLoss:
outputG,_ = netG(inputs)
elif opt.isDeeplySupervised:
outputG,outputG_path1,outputG_path2 = netG(inputs)
else:
outputG = netG(inputs)
#get the probability map of outputG
# if opt.NDim==2:
# outputG = softmax2d(outputG) #batch
# elif opt.NDim==3:
outputG = F.softmax(outputG, dim=1)
#outputG = outputG.data.max(1)[1]
#outputG = torch.squeeze(outputG) #[N,C,W,H]
#labels = labels.unsqueeze(1) #expand the 1st dim
#one hot encode for the gt labels
ohlabels = OneHotEncode(nclass=2,nd=3)(labels.data.cpu()) #NxWxHxD->NxCxWxHxD
#print 'outputG: ',outputG.size(),'labels: ',ohlabels.size()
outputR = Variable(ohlabels.type(torch.FloatTensor).cuda()) #output_Real
outputG = outputG.type(torch.FloatTensor).cuda()
if opt.isNetDInputIncludeSource:
inputReal_netD = torch.cat((inputs,outputR),dim=1)
outputD_real = netD(inputReal_netD)
inputFake_netD = torch.cat((inputs,outputG),dim=1)
outputD_fake = netD(inputFake_netD)
else:
outputD_real = netD(outputR)
outputD_fake = netD(outputG)
## step.5.2 update D network: maximize log(D(x)) + log(1 - D(G(z)))
batch_size = inputs.size(0)
output_shape = outputD_real.shape