def inference(network, moda_1, moda_g, imageNames, epoch, folder_save):
'''root_dir = './Data/MRBrainS/DataNii/'
model_dir = 'model'
moda_1 = root_dir + 'Training/T1'
moda_g = root_dir + 'Training/GT'''
network.eval()
softMax = nn.Softmax()
numClasses = 4 # Move this out
if torch.cuda.is_available():
softMax.cuda()
network.cuda()
dscAll = np.zeros(
(len(imageNames), numClasses - 1)) # 1 class is the background!!
for i_s in range(len(imageNames)):
patch_1, patch_g, img_shape = load_data_test(
moda_1, moda_g, imageNames[i_s]
) # hardcoded to read the first file. Loop this to get all files
patchSize = 27
patchSize_gt = 9
x = np.zeros((0, 3, patchSize, patchSize, patchSize))
x = np.vstack(
(x, np.zeros(
(patch_1.shape[0], 3, patchSize, patchSize, patchSize))))
x[:, 0, :, :, :] = patch_1
pred_numpy = np.zeros(
(0, numClasses, patchSize_gt, patchSize_gt, patchSize_gt))
pred_numpy = np.vstack(
(pred_numpy,
np.zeros((patch_1.shape[0], numClasses, patchSize_gt,
patchSize_gt, patchSize_gt))))
totalOp = len(imageNames) * patch_1.shape[0]
#pred = network(numpy_to_var(x[0,:,:,:,:]).view(1,3,patchSize,patchSize,patchSize))
for i_p in range(patch_1.shape[0]):
pred = network(
numpy_to_var(x[i_p, :, :, :, :].reshape(
1, 3, patchSize, patchSize, patchSize)))
pred_y = softMax(pred)
pred_numpy[i_p, :, :, :, :] = pred_y.cpu().data.numpy()
printProgressBar(i_s * ((totalOp + 0.0) / len(imageNames)) + i_p +
1,
totalOp,
prefix="[Validation] ",
length=15)
# To reconstruct the predicted volume
extraction_step_value = 9
pred_classes = np.argmax(pred_numpy, axis=1)
pred_classes = pred_classes.reshape(
(len(pred_classes), patchSize_gt, patchSize_gt, patchSize_gt))
bin_seg = my_reconstruct_volume(
pred_classes, (img_shape[1], img_shape[2], img_shape[3]),
patch_shape=(27, 27, 27),
extraction_step=(extraction_step_value, extraction_step_value,
extraction_step_value))
bin_seg = bin_seg[:, :, extraction_step_value:img_shape[3] -
extraction_step_value]
gt = nib.load(moda_g + '/' + imageNames[i_s]).get_data()
img_pred = nib.Nifti1Image(bin_seg, np.eye(4))
img_gt = nib.Nifti1Image(gt, np.eye(4))
img_name = imageNames[i_s].split('.nii')
name = 'Pred_' + img_name[0] + '_Epoch_' + str(epoch) + '.nii.gz'
namegt = 'GT_' + img_name[0] + '_Epoch_' + str(epoch) + '.nii.gz'
if not os.path.exists(folder_save + 'Segmentations/'):
os.makedirs(folder_save + 'Segmentations/')
if not os.path.exists(folder_save + 'GT/'):
os.makedirs(folder_save + 'GT/')
nib.save(img_pred, folder_save + 'Segmentations/' + name)
nib.save(img_gt, folder_save + 'GT/' + namegt)
dsc = evaluateSegmentation(gt, bin_seg)
dscAll[i_s, :] = dsc
return dscAll
def inference(network, moda_n, moda_g, imageNames, epoch, folder_save,
number_modalities):
a = 64
b = 64
'''root_dir = './Data/MRBrainS/DataNii/'
model_dir = 'model'
moda_1 = root_dir + 'Training/T1'
moda_2 = root_dir + 'Training/T1_IR'
moda_3 = root_dir + 'Training/T2_FLAIR'
moda_g = root_dir + 'Training/GT'''
network.eval()
softMax = nn.Sigmoid()
numClasses = 1
if torch.cuda.is_available():
softMax.cuda()
network.cuda()
dscAll = []
accall = []
for i_s in range(len(imageNames)):
if number_modalities == 2:
patch_1, patch_2, patch_g, img_shape = load_data_test(
moda_n, moda_g, imageNames[i_s], number_modalities
) # hardcoded to read the first file. Loop this to get all files
if number_modalities == 3:
patch_1, patch_2, patch_3, patch_g, img_shape = load_data_test(
[moda_n], moda_g, imageNames[i_s], number_modalities
) # hardcoded to read the first file. Loop this to get all files
# Normalization
patchSize = a
patchSize_gt = b
x = np.zeros((0, number_modalities, patchSize, patchSize, patchSize))
x = np.vstack((x,
np.zeros((patch_1.shape[0], number_modalities,
patchSize, patchSize, patchSize))))
x[:, 0, :, :, :] = patch_1
x[:, 1, :, :, :] = patch_2
if (number_modalities == 3):
x[:, 2, :, :, :] = patch_3
pred_numpy = np.zeros((0, patchSize_gt, patchSize_gt, patchSize_gt))
pred_numpy = np.vstack((pred_numpy,
np.zeros((patch_1.shape[0], patchSize_gt,
patchSize_gt, patchSize_gt))))
totalOp = len(imageNames) * patch_1.shape[0]
#pred = network(numpy_to_var(x[0,:,:,:,:]).view(1,number_modalities,patchSize,patchSize,patchSize))
for i_p in range(patch_1.shape[0]):
pred = network(
numpy_to_var(x[i_p, :, :, :, :].reshape(
1, number_modalities, patchSize, patchSize, patchSize)))
pred_y = softMax(
pred.reshape(patchSize_gt, patchSize_gt, patchSize_gt))
pred_numpy[i_p, :, :, :] = pred_y.cpu().data.numpy()
printProgressBar(i_s * ((totalOp + 0.0) / len(imageNames)) + i_p +
1,
totalOp,
prefix="[Validation] ",
length=15)
# To reconstruct the predicted volume
extraction_step_value = b
pred_classes = np.round(pred_numpy)
pred_classes = pred_classes.reshape(
(patch_1.shape[0], patchSize_gt, patchSize_gt, patchSize_gt))
#bin_seg = reconstruct_volume(pred_classes, (img_shape[1], img_shape[2], img_shape[3]))
bin_seg = my_reconstruct_volume(
pred_classes, (img_shape[1], img_shape[2], img_shape[3]),
patch_shape=(a, a, a),
extraction_step=(b, b, b))
#bin_seg = bin_seg[:,:,extraction_step_value:img_shape[3]-extraction_step_value]
#label_selector = [slice(None)] + [slice(9, 117) for i in range(3)]
gt = nib.load(moda_g + '/' + imageNames[i_s]).get_fdata()
gt_patches = extract_patches(gt, (a, a, a), (b, b, b))
#gt_patches = gt_patches[label_selector]
img_pred = nib.Nifti1Image(bin_seg, np.eye(4))
img_gt = nib.Nifti1Image(gt, np.eye(4))
img_name = imageNames[i_s].split('.nii')
name = 'Pred_' + img_name[0] + '_Epoch_' + str(epoch) + '.nii.gz'
namegt = 'GT_' + img_name[0] + '_Epoch_' + str(epoch) + '.nii.gz'
if not os.path.exists(folder_save + 'Segmentations/'):
os.makedirs(folder_save + 'Segmentations/')
if not os.path.exists(folder_save + 'GT/'):
os.makedirs(folder_save + 'GT/')
nib.save(img_pred, folder_save + 'Segmentations/' + name)
nib.save(img_gt, folder_save + 'GT/' + namegt)
dsc = dc(gt_patches, pred_classes)
acc = accuracy_score(gt_patches.flatten(), pred_classes.flatten())
dscAll.append(dsc)
accall.append(acc)
return dscAll, accall
def inference(network, moda_n, moda_g, imageNames, epoch, folder_save,
number_modalities):
'''root_dir = './Data/MRBrainS/DataNii/'
model_dir = 'model'
moda_1 = root_dir + 'Training/T1'
moda_2 = root_dir + 'Training/T1_IR'
moda_3 = root_dir + 'Training/T2_FLAIR'
moda_g = root_dir + 'Training/GT'''
network.eval()
softMax = nn.Softmax()
numClasses = 4
if torch.cuda.is_available():
softMax.cuda()
network.cuda()
accAll = np.zeros((len(imageNames), numClasses))
dscAll = np.zeros(
(len(imageNames), numClasses - 1)) # 1 class is the background!!
for i_s in range(len(imageNames)):
x_train, y_train, img_shape = load_data_test(moda_n, moda_g,
imageNames, 100,
number_modalities)
pred_numpy = np.zeros(
(0, numClasses, x_train.shape[2], x_train.shape[3]))
pred_numpy = np.vstack(
(pred_numpy,
np.zeros((x_train.shape[0], numClasses, x_train.shape[2],
x_train.shape[3]))))
totalOp = len(imageNames) * x_train.shape[0]
for i_p in range(x_train.shape[0]):
pred = network(
numpy_to_var(x_train[i_p, :, :, :].reshape(
1, number_modalities, x_train.shape[2], x_train.shape[3])))
# To adapt CE to 3D
# LOGITS:
#这里没明白
pred_y = softMax(pred)
pred_numpy[i_p, :, :, :] = pred_y.cpu().data.numpy()
printProgressBar(i_s * ((totalOp + 0.0) / len(imageNames)) + i_p +
1,
totalOp,
prefix="[Validation] ",
length=15)
# segmentation_prediction = hdNet(MRIs)
# predClass_y = softMax(segmentation_prediction)
# # print(predClass_y.shape)
# pred_classes = np.argmax(predClass_y.cpu().data.numpy(), axis=1)
# pred_classes = pred_classes.reshape((len(pred_classes), x_train.shape[2], x_train.shape[3]))
# dsc_train = evaluateSegmentation(Segmentation.cpu().data.numpy(),pred_classes)
# metric.addBatch(pred_classes.astype(dtype='int'), Segmentation.cpu().data.numpy().astype(dtype='int'))
# acc = metric.classPixelAccuracy()
pred_classes = np.argmax(pred_numpy, axis=1)
pred_classes = pred_classes.reshape(
(len(pred_classes), x_train.shape[2], x_train.shape[3]))
dsc = evaluateSegmentation(y_train, pred_classes)
dscAll[i_s, :] = dsc
metric.addBatch(pred_classes.astype(dtype='int'),
y_train.astype(dtype='int'))
acc = metric.classPixelAccuracy()
accAll[i_s, :] = acc
return dscAll, accAll
def numpy_to_var(x):
torch_tensor = torch.from_numpy(x).type(
torch.FloatTensor) #变成张量，与原矩阵共享内存，因此改变张量也会改变原矩阵
if torch.cuda.is_available():
torch_tensor = torch_tensor.cuda()
return Variable(torch_tensor)
# def inference(network, moda_n, moda_g, imageNames, epoch, folder_save, number_modalities):
'''root_dir = './Data/MRBrainS/DataNii/'
model_dir = 'model'
moda_1 = root_dir + 'Training/T1'
moda_2 = root_dir + 'Training/T1_IR'
moda_3 = root_dir + 'Training/T2_FLAIR'
moda_g = root_dir + 'Training/GT'''
network.eval()
softMax = nn.Softmax(dim=0)
numClasses = 4
if torch.cuda.is_available():
softMax.cuda()
network.cuda()
dscAll = np.zeros(
(len(imageNames), numClasses - 1)) # 1 class is the background!!
for i_s in range(len(imageNames)):
# if number_modalities == 2:
# patch_1, patch_2, patch_g, img_shape = load_data_test(moda_n, moda_g, imageNames[i_s], number_modalities) # hardcoded to read the first file. Loop this to get all files
# if number_modalities == 3:
# patch_1, patch_2, patch_3, patch_g, img_shape = load_data_test(moda_n, moda_g, imageNames[i_s], number_modalities) # hardcoded to read the first file. Loop this to get all files
# patchSize = 27
# patchSize_gt = 9
# x = np.zeros((0, number_modalities, patchSize, patchSize, patchSize))
# x = np.vstack((x, np.zeros((patch_1.shape[0], number_modalities, patchSize, patchSize, patchSize))))#shape(patch_1.shape[0],, number_modalities, patchSize, patchSize, patchSize)
# x[:, 0, :, :, :] = patch_1
# x[:, 1, :, :, :] = patch_2
# if (number_modalities==3):
# x[:, 2, :, :, :] = patch_3
# pred = network(numpy_to_var(x[0,:,:,:,:]).view(1,number_modalities,patchSize,patchSize,patchSize))
x_train, y_train, img_shape = load_data_test(moda_n, moda_g,
imageNames, 100,
number_modalities)
pred_numpy = np.zeros(
(0, numClasses, x_train.shape[2], x_train.shape[3]))
pred_numpy = np.vstack(
(pred_numpy,
np.zeros((x_train.shape[0], numClasses, x_train.shape[2],
x_train.shape[3]))))
totalOp = len(imageNames) * x_train.shape[0]
for i_p in range(x_train.shape[0]):
# a=x_train[i_p,:,:,:]
# b=x_train[i_p,:,:,:].reshape(1,number_modalities,x_train.shape[2],x_train.shape[3])
pred = network(
numpy_to_var(x_train[i_p, :, :, :].reshape(
1, number_modalities, x_train.shape[2], x_train.shape[3])))
# To adapt CE to 3D
# LOGITS:
#这里没明白
pred_y = softMax(pred)
# print(pred_y.numel())
# pred_y = pred_y.permute(0,2,3,1).contiguous()#permute是pytorch中的高维转置函数，返回的是张量
# pred_y = pred_y.view(pred_y.numel() // numClasses, numClasses)#numel返回数量
pred_numpy[i_p, :, :, :] = pred_y.cpu().data.numpy()
printProgressBar(i_s * ((totalOp + 0.0) / len(imageNames)) + i_p +
1,
totalOp,
prefix="[Validation] ",
length=15)
# To reconstruct the predicted volume
# extraction_step_value = 9
pred_classes = np.argmax(pred_numpy, axis=1)
pred_classes = pred_classes.reshape(
(len(pred_classes), x_train.shape[2], x_train.shape[3]))
pred_classes = pred_classes.transpose(1, 2, 0)
gt = nib.load(moda_g + '/' + imageNames[i_s]).get_data()
img_pred = nib.Nifti1Image(pred_classes, np.eye(4))
img_gt = nib.Nifti1Image(gt, np.eye(4))
img_name = imageNames[i_s].split('.nii')
name = 'Pred_' + img_name[0] + '_Epoch_' + str(epoch) + '.nii.gz'
namegt = 'GT_' + img_name[0] + '_Epoch_' + str(epoch) + '.nii.gz'
if not os.path.exists(folder_save + 'Segmentations/'):
os.makedirs(folder_save + 'Segmentations/')
if not os.path.exists(folder_save + 'GT/'):
os.makedirs(folder_save + 'GT/')
nib.save(img_pred, folder_save + 'Segmentations/' + name)
nib.save(img_gt, folder_save + 'GT/' + namegt)
y_train = y_train.transpose(1, 2, 0)
dsc = evaluateSegmentation(y_train, pred_classes)
dscAll[i_s, :] = dsc
return dscAll
def inference(network, moda_n, moda_g, imageNames, epoch, folder_save,
number_modalities, batch_size, CE_loss):
'''root_dir = './Data/MRBrainS/DataNii/'
model_dir = 'model'
moda_1 = root_dir + 'Training/T1'
moda_2 = root_dir + 'Training/T1_IR'
moda_3 = root_dir + 'Training/T2_FLAIR'
moda_g = root_dir + 'Training/GT'''
network.eval()
softMax = nn.Softmax()
numClasses = 4
if torch.cuda.is_available():
softMax.cuda()
network.cuda()
accAll = np.zeros((len(imageNames), numClasses))
dscAll = np.zeros(
(len(imageNames), numClasses - 1)) # 1 class is the background!!
val_loss = 0
for i_s in range(len(imageNames)):
x_train, y_train, img_shape = load_data_test(moda_n, moda_g,
imageNames, 100,
number_modalities)
data_transforms = transforms.Compose([
transforms.ToTensor(),
])
image_datasets = CustomNpzFolderLoader(x_train,
y_train,
data_transforms=data_transforms)
dataloaders = DataLoader(image_datasets,
batch_size=batch_size,
shuffle=False)
dataset_sizes = len(image_datasets)
pred_numpy = np.zeros(
(0, numClasses, x_train.shape[2], x_train.shape[3]))
pred_numpy = np.vstack(
(pred_numpy,
np.zeros((x_train.shape[0], numClasses, x_train.shape[2],
x_train.shape[3]))))
totalOp = len(imageNames) * x_train.shape[0]
running_loss = 0.0
for i_p, (train_data, label) in enumerate(dataloaders):
# for i_p in range(x_train.shape[0]):
# pred = network(numpy_to_var(x_train[i_p,:,:,:].reshape(1,number_modalities,x_train.shape[2],x_train.shape[3])))
if torch.cuda.is_available():
train_data = train_data.cuda().type(torch.cuda.FloatTensor)
pred = network(train_data)
# To adapt CE to 3D
# LOGITS:
#这里没明白
pred_y = softMax(pred)
pred_numpy[i_p * batch_size:(i_p + 1) *
batch_size, :, :, :] = pred_y.cpu().data.numpy()
printProgressBar(i_s * ((totalOp + 0.0) / len(imageNames)) + i_p +
1,
totalOp,
prefix="[Validation] ",
length=15)
pred = pred.permute(
0, 2, 3, 1).contiguous() #permute是pytorch中的高维转置函数，返回的是张量
pred = pred.view(pred.numel() // numClasses,
numClasses) #numel返回数量
CE_loss_batch = CE_loss(pred,
label.view(-1).type(torch.cuda.LongTensor))
running_loss += CE_loss_batch.cpu().data.numpy()
val_loss += running_loss / dataset_sizes
pred_classes = np.argmax(pred_numpy, axis=1)
pred_classes = pred_classes.reshape(
(len(pred_classes), x_train.shape[2], x_train.shape[3]))
dsc = evaluateSegmentation(y_train, pred_classes)
dscAll[i_s, :] = dsc
metric.addBatch(pred_classes.astype(dtype='int'),
y_train.astype(dtype='int'))
acc = metric.classPixelAccuracy()
accAll[i_s, :] = acc
return np.mean(np.array(dscAll),
axis=0), np.mean(np.array(accAll),
axis=0), val_loss / len(imageNames)