def main(options):
if options.num_classes == 2:
TRAINING_PATH = 'train_2classes.txt'
else:
TRAINING_PATH = 'train.txt'
IMG_PATH = '/Users/waz/JHU/CV-ADNI/ImageNoSkull'
dset_train = AD_3DRandomPatch(IMG_PATH, TRAINING_PATH)
train_loader = DataLoader(dset_train,
batch_size=options.batch_size,
shuffle=True,
num_workers=4,
drop_last=True)
sparsity = 0.05
beta = 0.5
mean_square_loss = nn.MSELoss()
kl_div_loss = nn.KLDivLoss()
use_gpu = len(options.gpuid) >= 1
autoencoder = AutoEncoder()
autoencoder = autoencoder.cpu()
optimizer = torch.optim.Adam(autoencoder.parameters(),
lr=options.learning_rate,
weight_decay=options.weight_decay)
train_loss = 0.
for epoch in range(options.epochs):
print("At {0}-th epoch.".format(epoch))
for i, patches in enumerate(train_loader):
for b, batch in enumerate(patches):
batch = Variable(batch)
output, mean_activitaion = autoencoder(batch)
loss1 = mean_square_loss(output, batch)
loss2 = kl_div_loss(mean_activitaion,
Variable(torch.Tensor([sparsity])))
print("loss1", loss1)
print("loss2", loss2)
loss = loss1 + loss2
train_loss += loss
logging.info(
"batch {0} training loss is : {1:.5f}, {1:.5f}".format(
b, loss1.data[0], loss2.data[0]))
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_avg_loss = train_loss / len(train_loader * 1000)
print(
"Average training loss is {0:.5f} at the end of epoch {1}".format(
train_avg_loss.data[0], epoch))
torch.save(model.state_dict(), open("autoencoder_model", 'wb'))
def main(options):
if options.num_classes == 2:
TRAINING_PATH = 'train_2classes.txt'
else:
TRAINING_PATH = 'train.txt'
IMG_PATH = './Image'
dset_train = AD_3DRandomPatch(IMG_PATH, TRAINING_PATH)
train_loader = DataLoader(dset_train,
batch_size=options.batch_size,
shuffle=True,
num_workers=4,
drop_last=True)
sparsity = 0.05
beta = 0.5
mean_square_loss = nn.MSELoss()
kl_div_loss = nn.KLDivLoss(reduce=False)
use_gpu = len(options.gpuid) >= 1
autoencoder = AutoEncoder()
if (use_gpu):
autoencoder = autoencoder.cuda()
else:
autoencoder = autoencoder.cpu()
optimizer = torch.optim.Adam(autoencoder.parameters(),
lr=options.learning_rate,
weight_decay=options.weight_decay)
train_loss = 0.
for epoch in range(options.epochs):
print("At {0}-th epoch.".format(epoch))
for i, patches in enumerate(train_loader):
print(i)
print(len(patches))
def main(options):
if options.num_classes == 3:
TRAINING_PATH = '/media/ailab/Backup Plus/ADNI/data/train_3classes.txt'
else:
TRAINING_PATH = '/media/ailab/Backup Plus/ADNI/data/train_3classes.txt'
IMG_PATH = '/media/ailab/Backup Plus/ADNI/data/image'
dset_train = AD_3DRandomPatch(IMG_PATH, TRAINING_PATH)
train_loader = DataLoader(dset_train,
batch_size=options.batch_size,
shuffle=True,
num_workers=0,
drop_last=True)
sparsity = 0.05
beta = 0.5
mean_square_loss = nn.MSELoss()
#kl_div_loss = nn.KLDivLoss(reduce=False)
use_gpu = len(options.gpuid) >= 1
autoencoder = AutoEncoder()
if (use_gpu):
autoencoder = autoencoder.cuda()
else:
autoencoder = autoencoder.cpu()
#autoencoder.load_state_dict(torch.load("./autoencoder_pretrained_model19"))
optimizer = torch.optim.Adam(autoencoder.parameters(),
lr=options.learning_rate,
weight_decay=options.weight_decay)
last_train_loss = 1e-4
f = open("autoencoder_loss", 'a')
for epoch in range(options.epochs):
train_loss = 0.
print("At {0}-th epoch.".format(epoch))
for i, patches in enumerate(train_loader):
patch = patches['patch']
for b, batch in enumerate(patch):
batch = Variable(batch).cuda()
#batch = out.view(-1, 343)
output, s_ = autoencoder(batch)
batch = batch.view(-1, 343)
loss1 = mean_square_loss(output, batch)
s = Variable(torch.ones(s_.shape) * sparsity).cuda()
loss2 = (s * torch.log(s / (s_ + 1e-8)) + (1 - s) * torch.log(
(1 - s) / ((1 - s_ + 1e-8)))).sum() / options.batch_size
#kl_div_loss(mean_activitaion, sparsity)
loss = loss1 + beta * loss2
train_loss += loss
logging.info(
"batch {0} training loss is : {1:.5f}, {2:.5f}".format(
i * 1000 + b, loss1.data[0], loss2.data[0]))
f.write("batch {0} training loss is : {1:.3f}\n".format(
i * 1000 + b, loss.data[0]))
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_avg_loss = train_loss / (len(train_loader) * 1000)
print(
"Average training loss is {0:.5f} at the end of epoch {1}".format(
train_avg_loss.data[0], epoch))
if (abs(train_avg_loss.data[0] - last_train_loss) <=
options.estop) or ((epoch + 1) % 20 == 0):
torch.save(autoencoder.state_dict(),
open("autoencoder_pretrained_model" + str(epoch), 'wb'))
last_train_loss = train_avg_loss.data[0]
f.close()
