def do_epoch(dm, model, optimizer, criterion, lr_sched, mode='train'):
dc = []
L = []
# lr_sched.step()
if mode == 'train':
model.train()
for idx, sample in enumerate(dm):
X = Variable(sample['sat'].to(device))
Y = Variable(sample['mask'].to(device))
optimizer.zero_grad()
y_pred = model(X)
y_pred = torch.nn.functional.sigmoid(y_pred)
loss = criterion(y_pred, Y)
loss.backward()
optimizer.step()
l = loss.detach().cpu().numpy()
dc.append(dice_coeff(y_pred, Y))
L.append(l)
print('\r', 'Epoch: ', epoch,
'step: ,' idx, '|', len(dm),
'Loss: %.4f' % np.mean(L),
'Dice: %.4f' % np.mean(dc),
end=' ')
elif mode == 'valid':
model.eval()
with torch.no_grad():
for idx, sample in enumerate(dm):
X = Variable(sample['sat'].cuda())
Y = Variable(sample['mask'].cuda())
y_pred = model(X)
y_pred = torch.nn.functional.sigmoid(y_pred)
dc.append(dice_coeff(y_pred, Y))
print('\r', idx, '|', len(dm),
'Dice: %.4f' % np.mean(dc),
end=' ')
if model == 'valid':
lr_sched.step(np.mean(dc))
return np.mean(dc)
def make_train_step(idx, data, model, optimizer, criterion, meters):
# get the inputs and wrap in Variable
if torch.cuda.is_available():
inputs = Variable(data['sat_img'].cuda())
labels = Variable(data['map_img'].cuda())
else:
inputs = Variable(data['sat_img'])
labels = Variable(data['map_img'])
# zero the parameter gradients
optimizer.zero_grad()
# forward
# prob_map = model(inputs) # last activation was a sigmoid
# outputs = (prob_map > 0.3).float()
outputs = model(inputs)
# pay attention to the weighted loss should input logits not probs
if args.lovasz_loss:
loss, BCE_loss, DICE_loss = criterion(outputs, labels)
outputs = torch.nn.functional.sigmoid(outputs)
else:
outputs = torch.nn.functional.sigmoid(outputs)
loss, BCE_loss, DICE_loss = criterion(outputs, labels)
# backward
loss.backward()
# https://github.com/asanakoy/kaggle_carvana_segmentation/blob/master/albu/src/train.py
# torch.nn.utils.clip_grad_norm(model.parameters(), 1.)
optimizer.step()
meters["train_acc"].update(metrics.dice_coeff(outputs, labels),
outputs.size(0))
meters["train_loss"].update(loss.data[0], outputs.size(0))
meters["train_IoU"].update(metrics.jaccard_index(outputs, labels),
outputs.size(0))
meters["train_BCE"].update(BCE_loss.data[0], outputs.size(0))
meters["train_DICE"].update(DICE_loss.data[0], outputs.size(0))
meters["outputs"] = outputs
return meters
def validate(net, loader, device):
net.eval()
loss = 0
with tqdm(total=len(loader),
dynamic_ncols=True,
desc="validation",
unit="batch",
leave=False) as pbar:
for images, masks in loader:
images, masks = images.to(device), masks.to(device)
with torch.no_grad():
output, _ = net(images)
output = torch.sigmoid(output)
output = (output > 0.5).float()
loss += dice_coeff(output, masks).item()
pbar.update()
net.train()
return loss / len(loader)
def compile_model(model, num_classes, metrics, loss, lr):
from keras.losses import binary_crossentropy
from keras.losses import categorical_crossentropy
from keras.metrics import binary_accuracy
from keras.metrics import categorical_accuracy
from keras.optimizers import Adam
from metrics import dice_coeff
from metrics import jaccard_index
from metrics import class_jaccard_index
from metrics import pixelwise_precision
from metrics import pixelwise_sensitivity
from metrics import pixelwise_specificity
from metrics import pixelwise_recall
from losses import focal_loss
if isinstance(loss, str):
if loss in {'ce', 'crossentropy'}:
if num_classes == 1:
loss = binary_crossentropy
else:
loss = categorical_crossentropy
elif loss in {'focal', 'focal_loss'}:
loss = focal_loss(num_classes)
else:
raise ValueError('unknown loss %s' % loss)
if isinstance(metrics, str):
metrics = [metrics, ]
for i, metric in enumerate(metrics):
if not isinstance(metric, str):
continue
elif metric == 'acc':
metrics[i] = binary_accuracy if num_classes == 1 else categorical_accuracy
elif metric == 'jaccard_index':
metrics[i] = jaccard_index(num_classes)
elif metric == 'jaccard_index0':
metrics[i] = class_jaccard_index(0)
elif metric == 'jaccard_index1':
metrics[i] = class_jaccard_index(1)
elif metric == 'jaccard_index2':
metrics[i] = class_jaccard_index(2)
elif metric == 'jaccard_index3':
metrics[i] = class_jaccard_index(3)
elif metric == 'jaccard_index4':
metrics[i] = class_jaccard_index(4)
elif metric == 'jaccard_index5':
metrics[i] = class_jaccard_index(5)
elif metric == 'dice_coeff':
metrics[i] = dice_coeff(num_classes)
elif metric == 'pixelwise_precision':
metrics[i] = pixelwise_precision(num_classes)
elif metric == 'pixelwise_sensitivity':
metrics[i] = pixelwise_sensitivity(num_classes)
elif metric == 'pixelwise_specificity':
metrics[i] = pixelwise_specificity(num_classes)
elif metric == 'pixelwise_recall':
metrics[i] = pixelwise_recall(num_classes)
else:
raise ValueError('metric %s not recognized' % metric)
model.compile(optimizer=Adam(lr=lr),
loss=loss,
metrics=metrics)
def test_model():
MAX = 0
save = 0
# for index in range(100):
model = Insensee_3Dunet(1).to(device)
model.load_state_dict(torch.load('./3dunet_model_save/weights_390.pth'))
test_dataset = MRIdataset.LiverDataset(MRIdataset.test_imagepath, MRIdataset.test_labelpath,
MRIdataset.testimg_ids, MRIdataset.testlabel_ids)
test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=4)
train_dataset = MRIdataset.LiverDataset(MRIdataset.imagepath, MRIdataset.labelpath,
MRIdataset.img_ids, MRIdataset.label_ids)
train_loader = DataLoader(train_dataset, batch_size=1, shuffle=False, num_workers=4)
# train_iter = enumerate(train_loader)
model.eval()
LV_Dice = 0
LV_Jac = 0
RV_Dice = 0
RV_Jac = 0
Myo_Dice = 0
Myo_Jac = 0
i = 0
# _, batch = train_iter.__next__()
# _, batch = train_iter.__next__()
# img, label= batch
# i = 1
# # print(img.shape)
# # img =torch.squeeze(img)
# # img = img.numpy().astype(float)
# # img = img.transpose(1, 2, 0)
# # new_image = nib.Nifti1Image(img, np.eye(4))
# # nib.save(new_image, r'/home/peng/Desktop/CROP/train2test/trainimg_%d.nii.gz' % i)
#
# img = img.to(device)
# pred = torch.argmax(model(img), 1)
# pred = pred.cpu()
# pred = torch.squeeze(pred)
# pred = pred.numpy().astype(float)
# pred = pred.transpose(1, 2, 0)
#
# label = torch.squeeze(label)
# label = label.numpy().astype(float)
# label = label.transpose(1, 2, 0)
#
#
# new_image = nib.Nifti1Image(pred, np.eye(4))
# nib.save(new_image, r'/home/peng/Desktop/CROP/train2test/pred_%d.nii.gz' % i)
#
# new_label = nib.Nifti1Image(label, np.eye(4))
# nib.save(new_label, r'/home/peng/Desktop/CROP/train2test/label_%d.nii.gz' % i)
for img, label in test_loader:
img = img.to(device)
pred = torch.argmax(model(img), 1)
pred = pred.cpu()
LV_dice, LV_jac, RV_dice, RV_jac, Myo_dice, Myo_jac = dice_coeff(pred, label)
LV_Dice += LV_dice
LV_Jac += LV_jac
RV_Dice += RV_dice
RV_Jac += RV_jac
Myo_Dice += Myo_dice
Myo_Jac += Myo_jac
print('LV_Dice_%d:' % i, '%.6f' % LV_dice, '||', 'RV_Dice_%d:' % i, '%.6f' % RV_dice, '||'
, 'Myo_Dice_%d:' % i, '%.6f' % Myo_dice)
i += 1
print('===============================================')
print('LV_Dice_avg:', LV_Dice / i, 'RV_Dice_avg:', RV_Dice / i, 'Myo_Dice_avg:', Myo_Dice / i)
def validation(valid_loader, model, criterion, logger, epoch_num):
"""
Args:
train_loader:
model:
criterion:
optimizer:
epoch:
Returns:
"""
# logging accuracy and loss
valid_acc = metrics.MetricTracker()
valid_loss = metrics.MetricTracker()
valid_IoU = metrics.MetricTracker()
valid_BCE = metrics.MetricTracker()
valid_DICE = metrics.MetricTracker()
log_iter = len(valid_loader) // logger.print_freq
# switch to evaluate mode
model.eval()
# Iterate over data.
for idx, data in enumerate(tqdm(valid_loader, desc='validation')):
# get the inputs and wrap in Variable
if torch.cuda.is_available():
inputs = Variable(data['sat_img'].cuda(), volatile=True)
labels = Variable(data['map_img'].cuda(), volatile=True)
else:
inputs = Variable(data['sat_img'], volatile=True)
labels = Variable(data['map_img'], volatile=True)
# forward
# prob_map = model(inputs) # last activation was a sigmoid
# outputs = (prob_map > 0.3).float()
outputs = model(inputs)
# pay attention to the weighted loss should input logits not probs
if args.lovasz_loss:
loss, BCE_loss, DICE_loss = criterion(outputs, labels)
outputs = torch.nn.functional.sigmoid(outputs)
else:
outputs = torch.nn.functional.sigmoid(outputs)
loss, BCE_loss, DICE_loss = criterion(outputs, labels)
valid_acc.update(metrics.dice_coeff(outputs, labels), outputs.size(0))
valid_loss.update(loss.data[0], outputs.size(0))
valid_IoU.update(metrics.jaccard_index(outputs, labels),
outputs.size(0))
valid_BCE.update(BCE_loss.data[0], outputs.size(0))
valid_DICE.update(DICE_loss.data[0], outputs.size(0))
# tensorboard logging
if idx % log_iter == 0:
step = (epoch_num * logger.print_freq) + (idx / log_iter)
# log accuracy and loss
info = {
'loss': valid_loss.avg,
'accuracy': valid_acc.avg,
'IoU': valid_IoU.avg
}
for tag, value in info.items():
logger.scalar_summary(tag, value, step)
# log the sample images
log_img = [
data_utils.show_tensorboard_image(data['sat_img'],
data['map_img'],
outputs,
as_numpy=True),
]
logger.image_summary('valid_images', log_img, step)
print(
'Validation Loss: {:.4f} BCE: {:.4f} DICE: {:.4f} Acc: {:.4f} IoU: {:.4f}'
.format(valid_loss.avg, valid_BCE.avg, valid_DICE.avg, valid_acc.avg,
valid_IoU.avg))
print()
return {
'valid_loss': valid_loss.avg,
'valid_acc': valid_acc.avg,
'valid_IoU': valid_IoU.avg,
'valid_BCE': valid_BCE.avg,
'valid_DICE': valid_DICE.avg
}
def val_model(model):
test_dataset = MRIdataset.LiverDataset(MRIdataset.test_imagepath,
MRIdataset.test_labelpath,
MRIdataset.testimg_ids,
MRIdataset.testlabel_ids, False)
test_loader = DataLoader(test_dataset,
batch_size=1,
shuffle=False,
num_workers=4)
model.eval()
LV_Dice = 0
RV_Dice = 0
Myo_Dice = 0
i = 0
for img, label, _, _ in test_loader:
if i < 50:
img = torch.squeeze(img)
label = torch.squeeze(label)
LV_dice = 0
RV_dice = 0
Myo_dice = 0
for z in range(img.shape[0]):
img_2d = img[z, :, :]
label_2d = label[z, :, :]
img_2d = torch.unsqueeze(img_2d, 0)
img_2d = torch.unsqueeze(img_2d, 0)
img_2d = img_2d.to(device)
output = model(img_2d)
# print(output.shape)
pred = torch.argmax(output, 1)
# print(pred.shape, label.shape)
pred = pred.cpu()
LV_dice_2d, LV_jac_2d, RV_dice_2d, RV_jac_2d, Myo_dice_2d, Myo_jac_2d = dice_coeff(
pred, label_2d)
LV_dice += LV_dice_2d
RV_dice += RV_dice_2d
Myo_dice += Myo_dice_2d
LV_dice /= img.shape[0]
RV_dice /= img.shape[0]
Myo_dice /= img.shape[0]
LV_Dice += LV_dice
RV_Dice += RV_dice
Myo_Dice += Myo_dice
# print('LV_Dice_%d:' % i, '%.6f' % LV_dice, '||', 'RV_Dice_%d:' % i, '%.6f' % RV_dice, '||'
# , 'Myo_Dice_%d:' % i, '%.6f' % Myo_dice)
i += 1
print('===============================================')
LV_Dice_avg = LV_Dice / i
RV_Dice_avg = RV_Dice / i
Myo_Dice_avg = Myo_Dice / i
Mean_metric = (LV_Dice_avg + RV_Dice_avg + Myo_Dice_avg) / 3
print('Mean_metric:', Mean_metric)
return Mean_metric
def dice_loss(y_true, y_pred):
loss = 1 - dice_coeff(y_true, y_pred)
return loss
def test_model():
MAX = 0
save = 0
# for index in range(100):
# model = DeepSupervision_U_Net(1, 4).to(device)
model = ResNetUNet(4).to(device)
# print(model)
model.load_state_dict(
torch.load(
'/home/laisong/MMs/LS/3dunet_model_save/BEST_weights_150.pth'))
test_dataset = MRIdataset.LiverDataset(MRIdataset.test_imagepath,
MRIdataset.test_labelpath,
MRIdataset.testimg_ids,
MRIdataset.testlabel_ids, False)
test_loader = DataLoader(test_dataset,
batch_size=1,
shuffle=False,
num_workers=4)
train_dataset = MRIdataset.LiverDataset(MRIdataset.imagepath,
MRIdataset.labelpath,
MRIdataset.img_ids,
MRIdataset.label_ids, False)
train_loader = DataLoader(train_dataset,
batch_size=1,
shuffle=False,
num_workers=4)
B_dataset = MRIdataset.LiverDataset(MRIdataset.B_imagepath,
MRIdataset.B_labelpath,
MRIdataset.Bimg_ids,
MRIdataset.Blabel_ids, False)
B_loader = DataLoader(B_dataset,
batch_size=1,
shuffle=False,
num_workers=4)
# train_iter = enumerate(train_loader)
model.eval()
LV_Dice = 0
LV_Jac = 0
RV_Dice = 0
RV_Jac = 0
Myo_Dice = 0
Myo_Jac = 0
i = 0
# _, batch = train_iter.__next__()
# _, batch = train_iter.__next__()
# img, label= batch
# i = 1
# # print(img.shape)
# # img =torch.squeeze(img)
# # img = img.numpy().astype(float)
# # img = img.transpose(1, 2, 0)
# # new_image = nib.Nifti1Image(img, np.eye(4))
# # nib.save(new_image, r'/home/peng/Desktop/CROP/train2test/trainimg_%d.nii.gz' % i)
#
# img = img.to(device)
# pred = torch.argmax(model(img), 1)
# pred = pred.cpu()
# pred = torch.squeeze(pred)
# pred = pred.numpy().astype(float)
# pred = pred.transpose(1, 2, 0)
#
# label = torch.squeeze(label)
# label = label.numpy().astype(float)
# label = label.transpose(1, 2, 0)
#
#
# new_image = nib.Nifti1Image(pred, np.eye(4))
# nib.save(new_image, r'/home/peng/Desktop/CROP/train2test/pred_%d.nii.gz' % i)
#
# new_label = nib.Nifti1Image(label, np.eye(4))
# nib.save(new_label, r'/home/peng/Desktop/CROP/train2test/label_%d.nii.gz' % i)
for img, label, _, _ in test_loader:
if i < 50:
img = torch.squeeze(img)
label = torch.squeeze(label)
LV_dice = 0
RV_dice = 0
Myo_dice = 0
for z in range(img.shape[0]):
img_2d = img[z, :, :]
label_2d = label[z, :, :]
img_2d = torch.unsqueeze(img_2d, 0)
img_2d = torch.unsqueeze(img_2d, 0)
img_2d = img_2d.to(device)
output = model(img_2d)
# print(output.shape)
pred = torch.argmax(output, 1)
# print(pred.shape, label.shape)
pred = pred.cpu()
LV_dice_2d, LV_jac_2d, RV_dice_2d, RV_jac_2d, Myo_dice_2d, Myo_jac_2d = dice_coeff(
pred, label_2d)
LV_dice += LV_dice_2d
RV_dice += RV_dice_2d
Myo_dice += Myo_dice_2d
LV_dice /= img.shape[0]
RV_dice /= img.shape[0]
Myo_dice /= img.shape[0]
LV_Dice += LV_dice
RV_Dice += RV_dice
Myo_Dice += Myo_dice
print('LV_Dice_%d:' % i, '%.6f' % LV_dice, '||', 'RV_Dice_%d:' % i,
'%.6f' % RV_dice, '||', 'Myo_Dice_%d:' % i,
'%.6f' % Myo_dice)
i += 1
print('===============================================')
print('LV_Dice_avg:', LV_Dice / i, 'RV_Dice_avg:', RV_Dice / i,
'Myo_Dice_avg:', Myo_Dice / i)
