def test():
model = ModelFlow_stride(2, 3, opt.start_channel).cuda()
transform = SpatialTransform().cuda()
model.load_state_dict(torch.load(opt.modelpath))
model.eval()
transform.eval()
grid = generate_grid(imgshape)
grid = Variable(torch.from_numpy(np.reshape(grid, (1, ) +
grid.shape))).cuda().float()
start = timeit.default_timer()
A = Variable(torch.from_numpy(load_5D(opt.fixed))).cuda().float()
B = Variable(torch.from_numpy(load_5D(opt.moving))).cuda().float()
start2 = timeit.default_timer()
print('Time for loading data: ', start2 - start)
pred = model(A, B)
F_AB = pred.permute(0, 2, 3, 4, 1).data.cpu().numpy()[0, :, :, :, :]
F_AB = F_AB.astype(np.float32) * range_flow
warped_A = transform(A,
pred.permute(0, 2, 3, 4, 1) * range_flow,
grid).data.cpu().numpy()[0, 0, :, :, :]
start3 = timeit.default_timer()
print('Time for registration: ', start3 - start2)
warped_F_BA = transform(-pred,
pred.permute(0, 2, 3, 4, 1) * range_flow,
grid).permute(0, 2, 3, 4,
1).data.cpu().numpy()[0, :, :, :, :]
warped_F_BA = warped_F_BA.astype(np.float32) * range_flow
start4 = timeit.default_timer()
print('Time for generating inverse flow: ', start4 - start3)
save_flow(F_AB, savepath + '/flow_A_B.nii.gz')
save_flow(warped_F_BA, savepath + '/inverse_flow_B_A.nii.gz')
save_img(warped_A, savepath + '/warped_A.nii.gz')
start5 = timeit.default_timer()
print('Time for saving results: ', start5 - start4)
del pred
pred = model(B, A)
F_BA = pred.permute(0, 2, 3, 4, 1).data.cpu().numpy()[0, :, :, :, :]
F_BA = F_BA.astype(np.float32) * range_flow
warped_B = transform(B,
pred.permute(0, 2, 3, 4, 1) * range_flow,
grid).data.cpu().numpy()[0, 0, :, :, :]
warped_F_AB = transform(-pred,
pred.permute(0, 2, 3, 4, 1) * range_flow,
grid).permute(0, 2, 3, 4,
1).data.cpu().numpy()[0, :, :, :, :]
warped_F_AB = warped_F_AB.astype(np.float32) * range_flow
save_flow(F_BA, savepath + '/flow_B_A.nii.gz')
save_flow(warped_F_AB, savepath + '/inverse_flow_A_B.nii.gz')
save_img(warped_B, savepath + '/warped_B.nii.gz')
def test():
device = torch.device("cuda:6")
model = ModelFlow_stride(2, 3, opt.start_channel).cuda(device)
# model =ModelFlow_stride(2,3,opt.start_channel).cpu()
# transform = SpatialTransform().cpu()
transform = SpatialTransform().cuda(device)
# model.load_state_dict(torch.load(opt.modelpath))
# model.load_state_dict(torch.load(opt.modelpath, map_location=torch.device('cpu')))
model.load_state_dict(torch.load(opt.modelpath, map_location=device))
model.eval()
transform.eval()
grid = generate_grid(imgshape)
grid = Variable(torch.from_numpy(np.reshape(
grid, (1, ) + grid.shape))).cuda(device).float()
# grid = Variable(torch.from_numpy(np.reshape(grid, (1,) + grid.shape))).cpu().float()
start = timeit.default_timer()
A = sitk.GetArrayFromImage(sitk.ReadImage(opt.fixed, sitk.sitkFloat32))
B = sitk.GetArrayFromImage(sitk.ReadImage(opt.moving, sitk.sitkFloat32))
#A = Variable(torch.from_numpy(A)).cuda(device).float()
#B = Variable(torch.from_numpy(B)).cuda(device).float()
# A, B = padding(A,B)
#A = load_5D(A).cuda(device).float()
#B = load_5D(B).cuda(device).float()
A = load_5D(A)
B = load_5D(B)
A = Variable(torch.from_numpy(A)).cuda(device).float()
B = Variable(torch.from_numpy(B)).cuda(device).float()
#A = Variable(torch.from_numpy( load_5D(opt.fixed))).cuda(device).float()
#B = Variable(torch.from_numpy( load_5D(opt.moving))).cuda(device).float()
start2 = timeit.default_timer()
print('Time for loading data: ', start2 - start)
pred = model(A, B)
F_AB = pred.permute(0, 2, 3, 4, 1).data.cpu().numpy()[0, :, :, :, :]
#F_AB = pred.permute(0,2,3,4,1).data.cuda(device).numpy()[0, :, :, :, :]
F_AB = F_AB.astype(np.float32) * range_flow
warped_A = transform(A,
pred.permute(0, 2, 3, 4, 1) * range_flow,
grid).data.cpu().numpy()[0, 0, :, :, :]
#warped_A = transform(A,pred.permute(0,2,3,4,1)*range_flow,grid).data.cuda(device).numpy()[0, 0, :, :, :]
start3 = timeit.default_timer()
print('Time for registration: ', start3 - start2)
warped_F_BA = transform(-pred,
pred.permute(0, 2, 3, 4, 1) * range_flow,
grid).permute(0, 2, 3, 4,
1).data.cpu().numpy()[0, :, :, :, :]
#warped_F_BA = transform(-pred,pred.permute(0,2,3,4,1)*range_flow,grid).permute(0,2,3,4,1).data.cuda(device).numpy()[0, :, :, :, :]
warped_F_BA = warped_F_BA.astype(np.float32) * range_flow
start4 = timeit.default_timer()
print('Time for generating inverse flow: ', start4 - start3)
save_flow(F_AB, savepath + '/flow_A_B.nii.gz')
save_flow(warped_F_BA, savepath + '/inverse_flow_B_A.nii.gz')
save_img(warped_A, savepath + '/warped_A.nii.gz')
start5 = timeit.default_timer()
print('Time for saving results: ', start5 - start4)
del pred
pred = model(B, A)
F_BA = pred.permute(0, 2, 3, 4, 1).data.cpu().numpy()[0, :, :, :, :]
#F_BA = pred.permute(0,2,3,4,1).data.cuda(device).numpy()[0, :, :, :, :]
F_BA = F_BA.astype(np.float32) * range_flow
warped_B = transform(B,
pred.permute(0, 2, 3, 4, 1) * range_flow,
grid).data.cpu().numpy()[0, 0, :, :, :]
#warped_B = transform(B,pred.permute(0,2,3,4,1)*range_flow,grid).data.cuda(device).numpy()[0, 0, :, :, :]
warped_F_AB = transform(-pred,
pred.permute(0, 2, 3, 4, 1) * range_flow,
grid).permute(0, 2, 3, 4,
1).data.cpu().numpy()[0, :, :, :, :]
#warped_F_AB = transform(-pred,pred.permute(0,2,3,4,1)*range_flow,grid).permute(0,2,3,4,1).data.cuda(device).numpy()[0, :, :, :, :]
warped_F_AB = warped_F_AB.astype(np.float32) * range_flow
save_flow(F_BA, savepath + '/flow_B_A.nii.gz')
save_flow(warped_F_AB, savepath + '/inverse_flow_A_B.nii.gz')
save_img(warped_B, savepath + '/warped_B.nii.gz')