def runTraining(args):
print('-' * 40)
print('~~~~~~~~ Starting the training... ~~~~~~')
print('-' * 40)
batch_size = args.batch_size
batch_size_val = 1
batch_size_val_save = 1
lr = args.lr
epoch = args.epochs
root_dir = './DataSet_Challenge/Val_1'
model_dir = 'model'
print(' Dataset: {} '.format(root_dir))
transform = transforms.Compose([
transforms.ToTensor()
])
mask_transform = transforms.Compose([
transforms.ToTensor()
])
train_set = medicalDataLoader.MedicalImageDataset('train',
root_dir,
transform=transform,
mask_transform=mask_transform,
augment=True,
equalize=False)
train_loader = DataLoader(train_set,
batch_size=batch_size,
num_workers=5,
shuffle=True)
val_set = medicalDataLoader.MedicalImageDataset('val',
root_dir,
transform=transform,
mask_transform=mask_transform,
equalize=False)
val_loader = DataLoader(val_set,
batch_size=batch_size_val,
num_workers=5,
shuffle=False)
val_loader_save_images = DataLoader(val_set,
batch_size=batch_size_val_save,
num_workers=4,
shuffle=False)
# Initialize
print("~~~~~~~~~~~ Creating the DAF Stacked model ~~~~~~~~~~")
net = DAF_stack()
print(" Model Name: {}".format(args.modelName))
print(" Model ot create: DAF_Stacked")
net.apply(weights_init)
softMax = nn.Softmax()
CE_loss = nn.CrossEntropyLoss()
Dice_loss = computeDiceOneHot()
mseLoss = nn.MSELoss()
if torch.cuda.is_available():
net.cuda()
softMax.cuda()
CE_loss.cuda()
Dice_loss.cuda()
optimizer = Adam(net.parameters(), lr=lr, betas=(0.9, 0.99), amsgrad=False)
BestDice, BestEpoch = 0, 0
BestDice3D = [0,0,0,0]
d1Val = []
d2Val = []
d3Val = []
d4Val = []
d1Val_3D = []
d2Val_3D = []
d3Val_3D = []
d4Val_3D = []
d1Val_3D_std = []
d2Val_3D_std = []
d3Val_3D_std = []
d4Val_3D_std = []
Losses = []
print("~~~~~~~~~~~ Starting the training ~~~~~~~~~~")
for i in range(epoch):
net.train()
lossVal = []
totalImages = len(train_loader)
for j, data in enumerate(train_loader):
image, labels, img_names = data
# prevent batchnorm error for batch of size 1
if image.size(0) != batch_size:
continue
optimizer.zero_grad()
MRI = to_var(image)
Segmentation = to_var(labels)
################### Train ###################
net.zero_grad()
# Network outputs
semVector_1_1, \
semVector_2_1, \
semVector_1_2, \
semVector_2_2, \
semVector_1_3, \
semVector_2_3, \
semVector_1_4, \
semVector_2_4, \
inp_enc0, \
inp_enc1, \
inp_enc2, \
inp_enc3, \
inp_enc4, \
inp_enc5, \
inp_enc6, \
inp_enc7, \
out_enc0, \
out_enc1, \
out_enc2, \
out_enc3, \
out_enc4, \
out_enc5, \
out_enc6, \
out_enc7, \
outputs0, \
outputs1, \
outputs2, \
outputs3, \
outputs0_2, \
outputs1_2, \
outputs2_2, \
outputs3_2 = net(MRI)
segmentation_prediction = (outputs0 + outputs1 + outputs2 + outputs3 + outputs0_2 + outputs1_2 + outputs2_2 + outputs3_2) / 8
predClass_y = softMax(segmentation_prediction)
Segmentation_planes = getOneHotSegmentation(Segmentation)
segmentation_prediction_ones = predToSegmentation(predClass_y)
# It needs the logits, not the softmax
Segmentation_class = getTargetSegmentation(Segmentation)
# Cross-entropy loss
loss0 = CE_loss(outputs0, Segmentation_class)
loss1 = CE_loss(outputs1, Segmentation_class)
loss2 = CE_loss(outputs2, Segmentation_class)
loss3 = CE_loss(outputs3, Segmentation_class)
loss0_2 = CE_loss(outputs0_2, Segmentation_class)
loss1_2 = CE_loss(outputs1_2, Segmentation_class)
loss2_2 = CE_loss(outputs2_2, Segmentation_class)
loss3_2 = CE_loss(outputs3_2, Segmentation_class)
lossSemantic1 = mseLoss(semVector_1_1, semVector_2_1)
lossSemantic2 = mseLoss(semVector_1_2, semVector_2_2)
lossSemantic3 = mseLoss(semVector_1_3, semVector_2_3)
lossSemantic4 = mseLoss(semVector_1_4, semVector_2_4)
lossRec0 = mseLoss(inp_enc0, out_enc0)
lossRec1 = mseLoss(inp_enc1, out_enc1)
lossRec2 = mseLoss(inp_enc2, out_enc2)
lossRec3 = mseLoss(inp_enc3, out_enc3)
lossRec4 = mseLoss(inp_enc4, out_enc4)
lossRec5 = mseLoss(inp_enc5, out_enc5)
lossRec6 = mseLoss(inp_enc6, out_enc6)
lossRec7 = mseLoss(inp_enc7, out_enc7)
lossG = loss0 + loss1 + loss2 + loss3 + loss0_2 + loss1_2 + loss2_2 + loss3_2 + 0.25 * (
lossSemantic1 + lossSemantic2 + lossSemantic3 + lossSemantic4) \
+ 0.1 * (lossRec0 + lossRec1 + lossRec2 + lossRec3 + lossRec4 + lossRec5 + lossRec6 + lossRec7) # CE_lossG
# Compute the DSC
DicesN, DicesB, DicesW, DicesT, DicesZ = Dice_loss(segmentation_prediction_ones, Segmentation_planes)
DiceB = DicesToDice(DicesB)
DiceW = DicesToDice(DicesW)
DiceT = DicesToDice(DicesT)
DiceZ = DicesToDice(DicesZ)
Dice_score = (DiceB + DiceW + DiceT+ DiceZ) / 4
lossG.backward()
optimizer.step()
lossVal.append(lossG.cpu().data.numpy())
printProgressBar(j + 1, totalImages,
prefix="[Training] Epoch: {} ".format(i),
length=15,
suffix=" Mean Dice: {:.4f}, Dice1: {:.4f} , Dice2: {:.4f}, , Dice3: {:.4f}, Dice4: {:.4f} ".format(
Dice_score.cpu().data.numpy(),
DiceB.data.cpu().data.numpy(),
DiceW.data.cpu().data.numpy(),
DiceT.data.cpu().data.numpy(),
DiceZ.data.cpu().data.numpy(),))
printProgressBar(totalImages, totalImages,
done="[Training] Epoch: {}, LossG: {:.4f}".format(i,np.mean(lossVal)))
# Save statistics
modelName = args.modelName
directory = 'Results/Statistics/' + modelName
Losses.append(np.mean(lossVal))
d1,d2,d3,d4 = inference(net, val_loader)
d1Val.append(d1)
d2Val.append(d2)
d3Val.append(d3)
d4Val.append(d4)
if not os.path.exists(directory):
os.makedirs(directory)
np.save(os.path.join(directory, 'Losses.npy'), Losses)
np.save(os.path.join(directory, 'd1Val.npy'), d1Val)
np.save(os.path.join(directory, 'd2Val.npy'), d2Val)
np.save(os.path.join(directory, 'd3Val.npy'), d3Val)
currentDice = (d1+d2+d3+d4)/4
print("[val] DSC: (1): {:.4f} (2): {:.4f} (3): {:.4f} (4): {:.4f}".format(d1,d2,d3,d4)) # MRI
currentDice = currentDice.data.numpy()
# Evaluate on 3D
saveImages_for3D(net, val_loader_save_images, batch_size_val_save, 1000, modelName, False, False)
reconstruct3D(modelName, 1000, isBest=False)
DSC_3D = evaluate3D(modelName)
mean_DSC3D = np.mean(DSC_3D, 0)
std_DSC3D = np.std(DSC_3D,0)
d1Val_3D.append(mean_DSC3D[0])
d2Val_3D.append(mean_DSC3D[1])
d3Val_3D.append(mean_DSC3D[2])
d4Val_3D.append(mean_DSC3D[3])
d1Val_3D_std.append(std_DSC3D[0])
d2Val_3D_std.append(std_DSC3D[1])
d3Val_3D_std.append(std_DSC3D[2])
d4Val_3D_std.append(std_DSC3D[3])
np.save(os.path.join(directory, 'd0Val_3D.npy'), d1Val_3D)
np.save(os.path.join(directory, 'd1Val_3D.npy'), d2Val_3D)
np.save(os.path.join(directory, 'd2Val_3D.npy'), d3Val_3D)
np.save(os.path.join(directory, 'd3Val_3D.npy'), d4Val_3D)
np.save(os.path.join(directory, 'd0Val_3D_std.npy'), d1Val_3D_std)
np.save(os.path.join(directory, 'd1Val_3D_std.npy'), d2Val_3D_std)
np.save(os.path.join(directory, 'd2Val_3D_std.npy'), d3Val_3D_std)
np.save(os.path.join(directory, 'd3Val_3D_std.npy'), d4Val_3D_std)
if currentDice > BestDice:
BestDice = currentDice
BestEpoch = i
if currentDice > 0.40:
if np.mean(mean_DSC3D)>np.mean(BestDice3D):
BestDice3D = mean_DSC3D
print("### In 3D -----> MEAN: {}, Dice(1): {:.4f} Dice(2): {:.4f} Dice(3): {:.4f} Dice(4): {:.4f} ###".format(np.mean(mean_DSC3D),mean_DSC3D[0], mean_DSC3D[1], mean_DSC3D[2], mean_DSC3D[3]))
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Saving best model..... ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
if not os.path.exists(model_dir):
os.makedirs(model_dir)
torch.save(net.state_dict(), os.path.join(model_dir, "Best_" + modelName + ".pth"),pickle_module=dill)
reconstruct3D(modelName, 1000, isBest=True)
print("### ###")
print("### Best Dice: {:.4f} at epoch {} with Dice(1): {:.4f} Dice(2): {:.4f} Dice(3): {:.4f} Dice(4): {:.4f} ###".format(BestDice, BestEpoch, d1,d2,d3,d4))
print("### Best Dice in 3D: {:.4f} with Dice(1): {:.4f} Dice(2): {:.4f} Dice(3): {:.4f} Dice(4): {:.4f} ###".format(np.mean(BestDice3D),BestDice3D[0], BestDice3D[1], BestDice3D[2], BestDice3D[3] ))
print("### ###")
if i % (BestEpoch + 50) == 0:
for param_group in optimizer.param_groups:
lr = lr*0.5
param_group['lr'] = lr
print(' ---------- New learning Rate: {}'.format(lr))
def runTraining():
print('-' * 40)
print('~~~~~~~~ Starting the training... ~~~~~~')
print('-' * 40)
batch_size = 4
batch_size_val = 1
batch_size_val_save = 1
batch_size_val_savePng = 4
lr = 0.0001
epoch = 1000
root_dir = '../DataSet/Bladder_Aug'
modelName = 'UNetG_Dilated_Progressive'
model_dir = 'model'
transform = transforms.Compose([transforms.ToTensor()])
mask_transform = transforms.Compose([transforms.ToTensor()])
train_set = medicalDataLoader.MedicalImageDataset(
'train',
root_dir,
transform=transform,
mask_transform=mask_transform,
augment=False,
equalize=False)
train_loader = DataLoader(train_set,
batch_size=batch_size,
num_workers=5,
shuffle=True)
val_set = medicalDataLoader.MedicalImageDataset(
'val',
root_dir,
transform=transform,
mask_transform=mask_transform,
equalize=False)
val_loader = DataLoader(val_set,
batch_size=batch_size_val,
num_workers=5,
shuffle=False)
val_loader_save_images = DataLoader(val_set,
batch_size=batch_size_val_save,
num_workers=5,
shuffle=False)
val_loader_save_imagesPng = DataLoader(val_set,
batch_size=batch_size_val_savePng,
num_workers=5,
shuffle=False)
# Initialize
print("~~~~~~~~~~~ Creating the model ~~~~~~~~~~")
num_classes = 4
initial_kernels = 32
# Load network
netG = UNetG_Dilated_Progressive(1, initial_kernels, num_classes)
softMax = nn.Softmax()
CE_loss = nn.CrossEntropyLoss()
Dice_loss = computeDiceOneHot()
if torch.cuda.is_available():
netG.cuda()
softMax.cuda()
CE_loss.cuda()
Dice_loss.cuda()
'''try:
netG = torch.load('./model/Best_UNetG_Dilated_Progressive_Stride_Residual_ChannelsFirst32.pkl')
print("--------model restored--------")
except:
print("--------model not restored--------")
pass'''
optimizerG = Adam(netG.parameters(),
lr=lr,
betas=(0.9, 0.99),
amsgrad=False)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizerG,
mode='max',
patience=4,
verbose=True,
factor=10**-0.5)
BestDice, BestEpoch = 0, 0
d1Train = []
d2Train = []
d3Train = []
d1Val = []
d2Val = []
d3Val = []
Losses = []
Losses1 = []
Losses05 = []
Losses025 = []
Losses0125 = []
print("~~~~~~~~~~~ Starting the training ~~~~~~~~~~")
for i in range(epoch):
netG.train()
lossVal = []
lossValD = []
lossVal1 = []
lossVal05 = []
lossVal025 = []
lossVal0125 = []
d1TrainTemp = []
d2TrainTemp = []
d3TrainTemp = []
timesAll = []
success = 0
totalImages = len(train_loader)
for j, data in enumerate(train_loader):
image, labels, img_names = data
# prevent batchnorm error for batch of size 1
if image.size(0) != batch_size:
continue
optimizerG.zero_grad()
MRI = to_var(image)
Segmentation = to_var(labels)
target_dice = to_var(torch.ones(1))
################### Train ###################
netG.zero_grad()
deepSupervision = False
multiTask = False
start_time = time.time()
if deepSupervision == False and multiTask == False:
# No deep supervision
segmentation_prediction = netG(MRI)
else:
# Deep supervision
if deepSupervision == True:
segmentation_prediction, seg_3, seg_2, seg_1 = netG(MRI)
else:
segmentation_prediction, reg_output = netG(MRI)
# Regression
feats = getValuesRegression(labels)
feats_t = torch.from_numpy(feats).float()
featsVar = to_var(feats_t)
MSE_loss_val = MSE_loss(reg_output, featsVar)
predClass_y = softMax(segmentation_prediction)
spentTime = time.time() - start_time
timesAll.append(spentTime / batch_size)
Segmentation_planes = getOneHotSegmentation(Segmentation)
segmentation_prediction_ones = predToSegmentation(predClass_y)
# It needs the logits, not the softmax
Segmentation_class = getTargetSegmentation(Segmentation)
# No deep supervision
CE_lossG = CE_loss(segmentation_prediction, Segmentation_class)
if deepSupervision == True:
imageLabels_05 = resizeTensorMaskInSingleImage(
Segmentation_class, 2)
imageLabels_025 = resizeTensorMaskInSingleImage(
Segmentation_class, 4)
imageLabels_0125 = resizeTensorMaskInSingleImage(
Segmentation_class, 8)
CE_lossG_3 = CE_loss(seg_3, imageLabels_05)
CE_lossG_2 = CE_loss(seg_2, imageLabels_025)
CE_lossG_1 = CE_loss(seg_1, imageLabels_0125)
'''weight = torch.ones(4).cuda() # Num classes
weight[0] = 0.2
weight[1] = 0.2
weight[2] = 1
weight[3] = 1
CE_loss.weight = weight'''
# Dice loss
DicesN, DicesB, DicesW, DicesT = Dice_loss(
segmentation_prediction_ones, Segmentation_planes)
DiceN = DicesToDice(DicesN)
DiceB = DicesToDice(DicesB)
DiceW = DicesToDice(DicesW)
DiceT = DicesToDice(DicesT)
Dice_score = (DiceB + DiceW + DiceT) / 3
if deepSupervision == False and multiTask == False:
lossG = CE_lossG
else:
# Deep supervision
if deepSupervision == True:
lossG = CE_lossG + 0.25 * CE_lossG_3 + 0.1 * CE_lossG_2 + 0.1 * CE_lossG_1
else:
lossG = CE_lossG + 0.000001 * MSE_loss_val
lossG.backward()
optimizerG.step()
lossVal.append(lossG.data[0])
lossVal1.append(CE_lossG.data[0])
if deepSupervision == True:
lossVal05.append(CE_lossG_3.data[0])
lossVal025.append(CE_lossG_2.data[0])
lossVal0125.append(CE_lossG_1.data[0])
printProgressBar(
j + 1,
totalImages,
prefix="[Training] Epoch: {} ".format(i),
length=15,
suffix=
" Mean Dice: {:.4f}, Dice1: {:.4f} , Dice2: {:.4f}, , Dice3: {:.4f} "
.format(Dice_score.data[0], DiceB.data[0], DiceW.data[0],
DiceT.data[0]))
if deepSupervision == False:
'''printProgressBar(totalImages, totalImages,
done="[Training] Epoch: {}, LossG: {:.4f},".format(i,np.mean(lossVal),np.mean(lossVal1)))'''
printProgressBar(
totalImages,
totalImages,
done="[Training] Epoch: {}, LossG: {:.4f}, lossMSE: {:.4f}".
format(i, np.mean(lossVal), np.mean(lossVal1)))
else:
printProgressBar(
totalImages,
totalImages,
done=
"[Training] Epoch: {}, LossG: {:.4f}, Loss4: {:.4f}, Loss3: {:.4f}, Loss2: {:.4f}, Loss1: {:.4f}"
.format(i, np.mean(lossVal), np.mean(lossVal1),
np.mean(lossVal05), np.mean(lossVal025),
np.mean(lossVal0125)))
Losses.append(np.mean(lossVal))
d1, d2, d3 = inference(netG, val_loader, batch_size, i,
deepSupervision)
d1Val.append(d1)
d2Val.append(d2)
d3Val.append(d3)
d1Train.append(np.mean(d1TrainTemp).data[0])
d2Train.append(np.mean(d2TrainTemp).data[0])
d3Train.append(np.mean(d3TrainTemp).data[0])
mainPath = '../Results/Statistics/' + modelName
directory = mainPath
if not os.path.exists(directory):
os.makedirs(directory)
###### Save statistics ######
np.save(os.path.join(directory, 'Losses.npy'), Losses)
np.save(os.path.join(directory, 'd1Val.npy'), d1Val)
np.save(os.path.join(directory, 'd2Val.npy'), d2Val)
np.save(os.path.join(directory, 'd3Val.npy'), d3Val)
np.save(os.path.join(directory, 'd1Train.npy'), d1Train)
np.save(os.path.join(directory, 'd2Train.npy'), d2Train)
np.save(os.path.join(directory, 'd3Train.npy'), d3Train)
currentDice = (d1 + d2 + d3) / 3
# How many slices with/without tumor correctly classified
print("[val] DSC: (1): {:.4f} (2): {:.4f} (3): {:.4f} ".format(
d1, d2, d3))
if currentDice > BestDice:
BestDice = currentDice
BestDiceT = d1
BestEpoch = i
if currentDice > 0.7:
print(
"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Saving best model..... ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
torch.save(
netG, os.path.join(model_dir,
"Best_" + modelName + ".pkl"))
# Save images
saveImages(netG, val_loader_save_images, batch_size_val_save,
i, modelName, deepSupervision)
saveImagesAsMatlab(netG, val_loader_save_images,
batch_size_val_save, i, modelName,
deepSupervision)
print("### ###")
print("### Best Dice: {:.4f} at epoch {} with DiceT: {:.4f} ###".
format(BestDice, BestEpoch, BestDiceT))
print("### ###")
# This is not as we did it in the MedPhys paper
if i % (BestEpoch + 20):
for param_group in optimizerG.param_groups:
param_group['lr'] = lr / 2
def train (self, targ, inp, mode = 'online', epochs = 500, par = None,
out = None, Jrout = None, track = False):
'''
This is the main function of the model: is used to trained the system
given a target and and input. Two training mode can be selected:
(offline, online). The first uses the exact likelihood gradient (which
is non local in time, thus non biologically-plausible), the second is
the online approx. of the gradient as descrived in the article.
Args:
targ: numpy.array of shape (N, T), where N is the number of neurons
in the network and T is the time length of the sequence.
inp : numpy.array of shape (N, T) that collects the input signal
to neurons.
Keywords:
mode : (default: online) The training mode to use, either 'offline'
or 'online'.
epochs: (default: 500) The number of epochs of training.
par : (default: None) Optional different dictionary collecting
training parameters: {dv, alpha, alpha_rout, beta_ro, offT}.
If not provided defaults to the parameter dictionary of
the model.
out : (default: None) Output target trajectories, numpy.array
of shape (K, T), where K is the dimension of the output
trajectories. This parameter should be specified if either
Jrout != None or track is True.
Jrout : (default: None) Pre-trained readout connection matrix.
If not provided, a novel matrix is built and trained
simultaneously with the recurrent connections training.
If Jrout is provided, the out parameter should be specified
as it is needed to compute output error.
track : (default: None) Flag to signal whether to track the evolution
of output MSE over training epochs. If track is True then
the out parameters should be specified as it is needed to
compute output error.
'''
assert (targ.shape == inp.shape);
par = self.par if par is None else par;
dv = par['dv'];
itau_m = self.itau_m;
itau_s = self.itau_s;
sigm = self._sigm;
alpha = par['alpha'];
alpha_rout = par['alpha_rout'];
beta_ro = par['beta_ro'];
offT = par['offT'];
self.S [:, 0] = targ [:, 0].copy ();
self.S_hat [:, 0] = self.S [:, 0] * itau_s;
Tmax = np.shape (targ) [-1];
dH = np.zeros ((self.N, self.T));
track = np.zeros (epochs) if track else None;
opt_rec = Adam (alpha = alpha, drop = 0.9, drop_time = 100 * Tmax if mode == 'online' else 100);
if Jrout is None:
S_rout = ut.sfilter (targ, itau = beta_ro);
J_rout = np.random.normal (0., 0.1, size = (out.shape[0], self.N));
opt = Adam (alpha = alpha_rout, drop = 0.9, drop_time = 20 * Tmax if mode == 'online' else 20);
else:
J_rout = Jrout;
for epoch in trange (epochs, leave = False, desc = 'Training {}'.format (mode)):
if Jrout is None:
# Here we train the readout
dJrout = (out - J_rout @ S_rout) @ S_rout.T;
J_rout = opt.step (J_rout, dJrout);
# Here we train the network
for t in range (Tmax - 1):
self.S_hat [:, t] = self.S_hat [:, t - 1] * itau_s + targ [:, t] * (1. - itau_s) if t > 0 else self.S_hat [:, 0];
self.H [:, t + 1] = self.H [:, t] * (1. - itau_m) + itau_m * (self.J @ self.S_hat [:, t] + inp [:, t] + self.h [:, t])\
+ self.Jreset @ targ [:, t];
dH [:, t + 1] = dH [:, t] * (1. - itau_m) + itau_m * self.S_hat [:, t];
if mode == 'online':
dJ = np.outer (targ [:, t + 1] - sigm (self.H [:, t + 1], dv = dv), dH [:, t + 1]);
self.J = opt_rec.step (self.J, dJ);
np.fill_diagonal (self.J, 0.);
if mode == 'offline':
dJ = (targ - sigm (self.H, dv = dv)) @ dH.T;
self.J = opt_rec.step (self.J, dJ);
np.fill_diagonal (self.J, 0.);
# Here we track MSE
if track is not None:
S_gen = self.compute (inp, init = np.zeros (self.N));
track [epoch] = np.mean ((out - J_rout @ ut.sfilter (S_gen,
itau = beta_ro))[:, offT:]**2.);
return (J_rout, track) if Jrout is None else track;
def main():
# generate data and translate labels
train_features, train_targets = generate_all_datapoints_and_labels()
test_features, test_targets = generate_all_datapoints_and_labels()
train_labels, test_labels = convert_labels(train_targets), convert_labels(test_targets)
print('*************************************************************************')
print('*************************************************************************')
print('*************************************************************************')
print('*************************************************************************')
print('*************************************************************************')
print('Model: Linear + ReLU + Linear +ReLU + Linear + ReLU + Linear + Tanh')
print('Loss: MSE')
print('Optimizer: SGD')
print('*************************************************************************')
print('Training')
print('*************************************************************************')
# build network, loss and optimizer for Model 1
my_model_design_1=[Linear(2,25), ReLU(), Linear(25,25), Dropout(p=0.5), ReLU(),
Linear(25,25), ReLU(),Linear(25,2),Tanh()]
my_model_1=Sequential(my_model_design_1)
optimizer_1=SGD(my_model_1,lr=1e-3)
criterion_1=LossMSE()
# train Model 1
batch_size=1
for epoch in range(50):
temp_train_loss_sum=0.
temp_test_loss_sum=0.
num_train_correct=0
num_test_correct=0
# trained in batch-fashion: here batch size = 1
for temp_batch in range(0,len(train_features), batch_size):
temp_train_features=train_features.narrow(0, temp_batch, batch_size)
temp_train_labels=train_labels.narrow(0, temp_batch, batch_size)
for i in range(batch_size):
# clean parameter gradient before each batch
optimizer_1.zero_grad()
temp_train_feature=temp_train_features[i]
temp_train_label=temp_train_labels[i]
# forward pass to compute loss
temp_train_pred=my_model_1.forward(temp_train_feature)
temp_train_loss=criterion_1.forward(temp_train_pred,temp_train_label)
temp_train_loss_sum+=temp_train_loss
_, temp_train_pred_cat=torch.max(temp_train_pred,0)
_, temp_train_label_cat=torch.max(temp_train_label,0)
if temp_train_pred_cat==temp_train_label_cat:
num_train_correct+=1
# calculate gradient according to loss gradient
temp_train_loss_grad=criterion_1.backward(temp_train_pred,temp_train_label)
# accumulate parameter gradient in each batch
my_model_1.backward(temp_train_loss_grad)
# update parameters by optimizer
optimizer_1.step()
# evaluate the current model on testing set
# only forward pass is implemented
for i_test in range(len(test_features)):
temp_test_feature=test_features[i_test]
temp_test_label=test_labels[i_test]
temp_test_pred=my_model_1.forward(temp_test_feature)
temp_test_loss=criterion_1.forward(temp_test_pred,temp_test_label)
temp_test_loss_sum+=temp_test_loss
_, temp_test_pred_cat=torch.max(temp_test_pred,0)
_, temp_test_label_cat=torch.max(temp_test_label,0)
if temp_test_pred_cat==temp_test_label_cat:
num_test_correct+=1
temp_train_loss_mean=temp_train_loss_sum/len(train_features)
temp_test_loss_mean=temp_test_loss_sum/len(test_features)
temp_train_accuracy=num_train_correct/len(train_features)
temp_test_accuracy=num_test_correct/len(test_features)
print("Epoch: {}/{}..".format(epoch+1, 50),
"Training Loss: {:.4f}..".format(temp_train_loss_mean),
"Training Accuracy: {:.4f}..".format(temp_train_accuracy),
"Validation/Test Loss: {:.4f}..".format(temp_test_loss_mean),
"Validation/Test Accuracy: {:.4f}..".format(temp_test_accuracy), )
# # visualize the classification performance of Model 1 on testing set
test_pred_labels_1=[]
for i in range(1000):
temp_test_feature=test_features[i]
temp_test_label=test_labels[i]
temp_test_pred=my_model_1.forward(temp_test_feature)
_, temp_train_pred_cat=torch.max(temp_test_pred,0)
if test_targets[i].int() == temp_train_pred_cat.int():
test_pred_labels_1.append(int(test_targets[i]))
else:
test_pred_labels_1.append(2)
fig,axes = plt.subplots(1,1,figsize=(6,6))
axes.scatter(test_features[:,0], test_features[:,1], c=test_pred_labels_1)
axes.set_title('Classification Performance of Model 1')
plt.show()
print('*************************************************************************')
print('*************************************************************************')
print('*************************************************************************')
print('*************************************************************************')
print('*************************************************************************')
print('Model: Linear + ReLU + Linear + Dropout+ SeLU + Linear + Dropout + ReLU + Linear + Sigmoid')
print('Loss: Cross Entropy')
print('Optimizer: Adam')
print('*************************************************************************')
print('Training')
print('*************************************************************************')
# build network, loss function and optimizer for Model 2
my_model_design_2=[Linear(2,25), ReLU(), Linear(25,25), Dropout(p=0.5), SeLU(),
Linear(25,25),Dropout(p=0.5), ReLU(),Linear(25,2),
Sigmoid()]
my_model_2=Sequential(my_model_design_2)
optimizer_2=Adam(my_model_2,lr=1e-3)
criterion_2=CrossEntropy()
# train Model 2
batch_size=1
epoch=0
while(epoch<25):
temp_train_loss_sum=0.
temp_test_loss_sum=0.
num_train_correct=0
num_test_correct=0
# trained in batch-fashion: here batch size = 1
for temp_batch in range(0,len(train_features), batch_size):
temp_train_features=train_features.narrow(0, temp_batch, batch_size)
temp_train_labels=train_labels.narrow(0, temp_batch, batch_size)
for i in range(batch_size):
# clean parameter gradient before each batch
optimizer_2.zero_grad()
temp_train_feature=temp_train_features[i]
temp_train_label=temp_train_labels[i]
# forward pass to compute loss
temp_train_pred=my_model_2.forward(temp_train_feature)
temp_train_loss=criterion_2.forward(temp_train_pred,temp_train_label)
temp_train_loss_sum+=temp_train_loss
_, temp_train_pred_cat=torch.max(temp_train_pred,0)
_, temp_train_label_cat=torch.max(temp_train_label,0)
if temp_train_pred_cat==temp_train_label_cat:
num_train_correct+=1
# calculate gradient according to loss gradient
temp_train_loss_grad=criterion_2.backward(temp_train_pred,temp_train_label)
'''
if (not temp_train_loss_grad[0]>=0) and (not temp_train_loss_grad[0]<0):
continue
'''
# accumulate parameter gradient in each batch
my_model_2.backward(temp_train_loss_grad)
# update parameters by optimizer
optimizer_2.step()
# evaluate the current model on testing set
# only forward pass is implemented
for i_test in range(len(test_features)):
temp_test_feature=test_features[i_test]
temp_test_label=test_labels[i_test]
temp_test_pred=my_model_2.forward(temp_test_feature)
temp_test_loss=criterion_2.forward(temp_test_pred,temp_test_label)
temp_test_loss_sum+=temp_test_loss
_, temp_test_pred_cat=torch.max(temp_test_pred,0)
_, temp_test_label_cat=torch.max(temp_test_label,0)
if temp_test_pred_cat==temp_test_label_cat:
num_test_correct+=1
temp_train_loss_mean=temp_train_loss_sum/len(train_features)
temp_test_loss_mean=temp_test_loss_sum/len(test_features)
temp_train_accuracy=num_train_correct/len(train_features)
temp_test_accuracy=num_test_correct/len(test_features)
# in case there is gradient explosion problem, initiliza model again and restart training
# but the situation seldom happens
if (not temp_train_loss_grad[0]>=0) and (not temp_train_loss_grad[0]<0):
epoch=0
my_model_design_2=[Linear(2,25), ReLU(), Linear(25,25), Dropout(p=0.5), ReLU(),
Linear(25,25),Dropout(p=0.5), ReLU(),Linear(25,2),Sigmoid()]
my_model_2=Sequential(my_model_design_2)
optimizer_2=Adam(my_model_2,lr=1e-3)
criterion_2=CrossEntropy()
print('--------------------------------------------------------------------------------')
print('--------------------------------------------------------------------------------')
print('--------------------------------------------------------------------------------')
print('--------------------------------------------------------------------------------')
print('--------------------------------------------------------------------------------')
print('Restart training because of gradient explosion')
continue
print("Epoch: {}/{}..".format(epoch+1, 25),
"Training Loss: {:.4f}..".format(temp_train_loss_mean),
"Training Accuracy: {:.4f}..".format(temp_train_accuracy),
"Validation/Test Loss: {:.4f}..".format(temp_test_loss_mean),
"Validation/Test Accuracy: {:.4f}..".format(temp_test_accuracy), )
epoch+=1
# visualize the classification performance of Model 2 on testing set
test_pred_labels_2=[]
for i in range(1000):
temp_test_feature=test_features[i]
temp_test_label=test_labels[i]
temp_test_pred=my_model_2.forward(temp_test_feature)
_, temp_train_pred_cat=torch.max(temp_test_pred,0)
if test_targets[i].int() == temp_train_pred_cat.int():
test_pred_labels_2.append(int(test_targets[i]))
else:
test_pred_labels_2.append(2)
fig,axes = plt.subplots(1,1,figsize=(6,6))
axes.scatter(test_features[:,0], test_features[:,1], c=test_pred_labels_2)
axes.set_title('Classification Performance of Model 2')
plt.show()
def runTraining():
print('-' * 40)
print('~~~~~~~~ Starting the training... ~~~~~~')
print('-' * 40)
batch_size = 4
batch_size_val = 1
batch_size_val_save = 1
lr = 0.0001
epoch = 200
num_classes = 2
initial_kernels = 32
modelName = 'IVD_Net'
img_names_ALL = []
print('.' * 40)
print(" ....Model name: {} ........".format(modelName))
print(' - Num. classes: {}'.format(num_classes))
print(' - Num. initial kernels: {}'.format(initial_kernels))
print(' - Batch size: {}'.format(batch_size))
print(' - Learning rate: {}'.format(lr))
print(' - Num. epochs: {}'.format(epoch))
print('.' * 40)
root_dir = '../Data/Training_PngITK'
model_dir = 'IVD_Net'
transform = transforms.Compose([transforms.ToTensor()])
mask_transform = transforms.Compose([transforms.ToTensor()])
train_set = medicalDataLoader.MedicalImageDataset(
'train',
root_dir,
transform=transform,
mask_transform=mask_transform,
augment=False,
equalize=False)
train_loader = DataLoader(train_set,
batch_size=batch_size,
num_workers=5,
shuffle=True)
val_set = medicalDataLoader.MedicalImageDataset(
'val',
root_dir,
transform=transform,
mask_transform=mask_transform,
equalize=False)
val_loader = DataLoader(val_set,
batch_size=batch_size_val,
num_workers=5,
shuffle=False)
val_loader_save_images = DataLoader(val_set,
batch_size=batch_size_val_save,
num_workers=5,
shuffle=False)
# Initialize
print("~~~~~~~~~~~ Creating the model ~~~~~~~~~~")
net = IVD_Net_asym(1, num_classes, initial_kernels)
# Initialize the weights
net.apply(weights_init)
softMax = nn.Softmax()
CE_loss = nn.CrossEntropyLoss()
Dice_ = computeDiceOneHotBinary()
if torch.cuda.is_available():
net.cuda()
softMax.cuda()
CE_loss.cuda()
Dice_.cuda()
# To load a pre-trained model
'''try:
net = torch.load('modelName')
print("--------model restored--------")
except:
print("--------model not restored--------")
pass'''
optimizer = Adam(net.parameters(), lr=lr, betas=(0.9, 0.99), amsgrad=False)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode='max',
patience=4,
verbose=True,
factor=10**-0.5)
BestDice, BestEpoch = 0, 0
d1Train = []
d1Val = []
Losses = []
print("~~~~~~~~~~~ Starting the training ~~~~~~~~~~")
for i in range(epoch):
net.train()
lossTrain = []
d1TrainTemp = []
totalImages = len(train_loader)
for j, data in enumerate(train_loader):
image_f, image_i, image_o, image_w, labels, img_names = data
# Be sure your data here is between [0,1]
image_f = image_f.type(torch.FloatTensor)
image_i = image_i.type(torch.FloatTensor)
image_o = image_o.type(torch.FloatTensor)
image_w = image_w.type(torch.FloatTensor)
labels = labels.numpy()
idx = np.where(labels > 0.0)
labels[idx] = 1.0
labels = torch.from_numpy(labels)
labels = labels.type(torch.FloatTensor)
optimizer.zero_grad()
MRI = to_var(torch.cat((image_f, image_i, image_o, image_w),
dim=1))
Segmentation = to_var(labels)
target_dice = to_var(torch.ones(1))
net.zero_grad()
segmentation_prediction = net(MRI)
predClass_y = softMax(segmentation_prediction)
Segmentation_planes = getOneHotSegmentation(Segmentation)
segmentation_prediction_ones = predToSegmentation(predClass_y)
# It needs the logits, not the softmax
Segmentation_class = getTargetSegmentation(Segmentation)
CE_loss_ = CE_loss(segmentation_prediction, Segmentation_class)
# Compute the Dice (so far in a 2D-basis)
DicesB, DicesF = Dice_(segmentation_prediction_ones,
Segmentation_planes)
DiceB = DicesToDice(DicesB)
DiceF = DicesToDice(DicesF)
loss = CE_loss_
loss.backward()
optimizer.step()
lossTrain.append(loss.data[0])
printProgressBar(j + 1,
totalImages,
prefix="[Training] Epoch: {} ".format(i),
length=15,
suffix=" Mean Dice: {:.4f},".format(
DiceF.data[0]))
printProgressBar(totalImages,
totalImages,
done="[Training] Epoch: {}, LossG: {:.4f}".format(
i, np.mean(lossTrain)))
# Save statistics
Losses.append(np.mean(lossTrain))
d1 = inference(net, val_loader, batch_size, i)
d1Val.append(d1)
d1Train.append(np.mean(d1TrainTemp).data[0])
mainPath = '../Results/Statistics/' + modelName
directory = mainPath
if not os.path.exists(directory):
os.makedirs(directory)
np.save(os.path.join(directory, 'Losses.npy'), Losses)
np.save(os.path.join(directory, 'd1Val.npy'), d1Val)
np.save(os.path.join(directory, 'd1Train.npy'), d1Train)
currentDice = d1[0].numpy()
print("[val] DSC: {:.4f} ".format(d1[0]))
if currentDice > BestDice:
BestDice = currentDice
BestEpoch = i
if currentDice > 0.75:
print(
"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Saving best model..... ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
torch.save(
net, os.path.join(model_dir, "Best_" + modelName + ".pkl"))
saveImages(net, val_loader_save_images, batch_size_val_save, i,
modelName)
# Two ways of decay the learning rate:
if i % (BestEpoch + 10):
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def train(self,
targs,
inps,
mode='online',
epochs=500,
par=None,
outs=None,
Jrout=None,
track=False):
'''
This is the main function of the model: is used to trained the system
given a target and and input. Two training mode can be selected:
(offline, online). The first uses the exact likelihood gradient (which
is non local in time, thus non biologically-plausible), the second is
the online approx. of the gradient as descrived in the article.
Args:
targ: numpy.array of shape (N, T), where N is the number of neurons
in the network and T is the time length of the sequence.
inp : numpy.array of shape (N, T) that collects the input signal
to neurons.
Keywords:
mode : (default: online) The training mode to use, either 'offline'
or 'online'.
epochs: (default: 500) The number of epochs of training.
par : (default: None) Optional different dictionary collecting
training parameters: {dv, alpha, alpha_rout, beta_ro, offT}.
If not provided defaults to the parameter dictionary of
the model.
out : (default: None) Output target trajectories, numpy.array
of shape (K, T), where K is the dimension of the output
trajectories. This parameter should be specified if either
Jrout != None or track is True.
Jrout : (default: None) Pre-trained readout connection matrix.
If not provided, a novel matrix is built and trained
simultaneously with the recurrent connections training.
If Jrout is provided, the out parameter should be specified
as it is needed to compute output error.
track : (default: None) Flag to signal whether to track the evolution
of output MSE over training epochs. If track is True then
the out parameters should be specified as it is needed to
compute output error.
'''
par = self.par if par is None else par
dv = par['dv']
itau_m = self.itau_m
itau_s = self.itau_s
sigm = self._sigm
alpha = par['alpha']
alpha_rout = par['alpha_rout']
beta_ro = par['beta_ro']
offT = par['offT']
self.J = np.random.normal(0.,
.1 / np.sqrt(self.N),
size=(self.N, self.N))
self.J[:, :self.N // 2] = np.abs(self.J[:, :self.N // 2])
self.J[:, self.N // 2:] = -np.abs(self.J[:, self.N // 2:])
np.fill_diagonal(self.J, 0.)
ndxe = self.J > 0
ndxi = self.J < 0
track = np.zeros((epochs, 2)) if track else None
Tmax = np.shape(targs[0])[-1]
opt_recE = SimpleGradient(alpha=alpha)
opt_recI = SimpleGradient(alpha=alpha)
if Jrout is None:
S_rout = [ut.sfilter(targ, itau=beta_ro) for targ in targs]
J_rout = np.random.normal(0.,
0.01,
size=(outs[0].shape[0], self.N))
opt = Adam(alpha=alpha_rout,
drop=0.9,
drop_time=epochs // 5 *
Tmax if mode == 'online' else 1e10)
else:
J_rout = Jrout
for epoch in trange(epochs,
leave=False,
desc='Training {}'.format(mode)):
if Jrout is None:
ut.shuffle((inps, outs, targs, S_rout))
else:
ut.shuffle((inps, targs))
if Jrout is None:
for out, s_rout in zip(outs, S_rout):
# Here we train the readout
dJrout = (out - J_rout @ s_rout) @ s_rout.T
J_rout = opt.step(J_rout, dJrout)
# Here we train the network
for inp, targ in zip(inps, targs):
self.S[:, 0] = targ[:, 0].copy()
self.S_hat[:, 0] = self.S[:, 0] * itau_s
dHe = np.zeros((self.N, self.N))
dHi = np.zeros((self.N, self.N))
dJe = dJi = 0.
for t in range(Tmax - 1):
Jp = ndxe * self.J
Jm = -(ndxi * self.J)
self.S_hat[:,
t] = self.S_hat[:,
t - 1] * itau_s + targ[:, t] * (
1. - itau_s
) if t > 0 else self.S_hat[:, 0]
self.H [:, t + 1] = self.H [:, t] * (1. - itau_m) +\
itau_m * ((self.Ve - self.H[:, t]).reshape (self.N, 1) / abs(self.Ve) * Jp @ self.S_hat [:, t] +
(self.Vi - self.H[:, t]).reshape (self.N, 1) / abs(self.Vi) * Jm @ self.S_hat [:, t] +
inp [:, t] + self.h [:, t]) +\
self.Jreset @ targ [:, t]
dHe [...] = dHe * (1. - itau_m * (1 + ((Jp + Jm) @ self.S_hat [:, t]).reshape (self.N, 1) / abs (self.Ve))) +\
itau_m * ((self.Ve - self.H[:, t + 1]).reshape (self.N, 1) / abs (self.Ve)
* ndxe * self.S_hat[:, t].reshape (1, self.N))
dHi [...] = dHi * (1. - itau_m * (1 + ((Jp + Jm) @ self.S_hat [:, t]).reshape (self.N, 1) / abs (self.Vi))) +\
itau_m * ((self.Vi - self.H[:, t + 1]).reshape(self.N, 1) / abs (self.Vi)
* ndxi * self.S_hat[:, t].reshape(1, self.N))
if mode == 'online':
dJe = (targ[:, t + 1] - sigm(
self.H[:, t + 1], dv=dv)).reshape(self.N, 1) * dHe
dJi = (targ[:, t + 1] - sigm(
self.H[:, t + 1], dv=dv)).reshape(self.N, 1) * dHi
Jp = opt_recE.step(Jp, dJe * ndxe)
Jm = opt_recI.step(Jm, dJi * ndxi)
Jp = np.maximum(Jp, 0.)
Jm = np.maximum(Jm, 0.)
self.J = Jp - Jm
np.fill_diagonal(self.J, 0.)
elif mode == 'offline':
dJe += (targ[:, t + 1] - sigm(
self.H[:, t + 1], dv=dv)).reshape(self.N, 1) * dHe
dJi += (targ[:, t + 1] - sigm(
self.H[:, t + 1], dv=dv)).reshape(self.N, 1) * dHi
if mode == 'offline':
Jp = opt_recE.step(Jp, dJe * ndxe)
Jm = opt_recI.step(Jm, dJi * ndxi)
Jp = np.maximum(Jp, 0.)
Jm = np.maximum(Jm, 0.)
self.J = Jp - Jm
np.fill_diagonal(self.J, 0.)
# Here we track MSE
if track is not None:
S_gen = self.compute(inp, init=np.zeros(self.N))
track[epoch, 0] = np.mean(
(out -
J_rout @ ut.sfilter(S_gen, itau=beta_ro))[:, offT:]**2.)
track[epoch,
1] = np.sum(np.abs(targ - S_gen)) / (self.N * self.T)
return (J_rout, track) if Jrout is None else track