How can i change this GPU code to CPU?

1

parser = argparse.ArgumentParser(description=‘PSMNet’)
parser.add_argument(’–KITTI’, default=‘2015’,
help=‘KITTI version’)
parser.add_argument(’–datapath’, default=’/scratch/datasets/kitti2015/testing/’,
help=‘select model’)
parser.add_argument(’–loadmodel’, default=None,
help=‘loading model’)
parser.add_argument(’–model’, default=‘stackhourglass’,
help=‘select model’)
parser.add_argument(’–maxdisp’, type=int, default=192,
help=‘maxium disparity’)
parser.add_argument(’–no-cuda’, action=‘store_true’, default=False,
help=‘enables CUDA training’)
parser.add_argument(’–seed’, type=int, default=1, metavar=‘S’,
help=‘random seed (default: 1)’)
parser.add_argument(’–save_path’, type=str, default=‘finetune_1000’, metavar=‘S’,
help=‘path to save the predict’)
parser.add_argument(’–save_figure’, action=‘store_true’, help=‘if true, save the numpy file, not the png file’)
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()

torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)

if args.KITTI == ‘2015’:
from dataloader import KITTI_submission_loader as DA
else:
from dataloader import KITTI_submission_loader2012 as DA

test_left_img, test_right_img = DA.dataloader(args.datapath)

if args.model == ‘stackhourglass’:
model = stackhourglass(args.maxdisp)
elif args.model == ‘basic’:
model = basic(args.maxdisp)
else:
print(‘no model’)

model = nn.DataParallel(model, device_ids=[0])
model.cuda()

if args.loadmodel is not None:
state_dict = torch.load(args.loadmodel)
model.load_state_dict(state_dict[‘state_dict’])

print(‘Number of model parameters: {}’.format(sum([p.data.nelement() for p in model.parameters()])))

def test(imgL,imgR):
model.eval()

    if args.cuda:
       imgL = torch.FloatTensor(imgL).cuda()
       imgR = torch.FloatTensor(imgR).cuda()     

    imgL, imgR= Variable(imgL), Variable(imgR)

    with torch.no_grad():
        output = model(imgL,imgR)
    output = torch.squeeze(output)
    pred_disp = output.data.cpu().numpy()

    return pred_disp

def main():
processed = preprocess.get_transform(augment=False)
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)

for inx in range(len(test_left_img)):

   imgL_o = (skimage.io.imread(test_left_img[inx]).astype('float32'))
   imgR_o = (skimage.io.imread(test_right_img[inx]).astype('float32'))
   imgL = processed(imgL_o).numpy()
   imgR = processed(imgR_o).numpy()
   imgL = np.reshape(imgL,[1,3,imgL.shape[1],imgL.shape[2]])
   imgR = np.reshape(imgR,[1,3,imgR.shape[1],imgR.shape[2]])

   # pad to (384, 1248)
   top_pad = 384-imgL.shape[2]
   left_pad = 1248-imgL.shape[3]
   imgL = np.lib.pad(imgL,((0,0),(0,0),(top_pad,0),(0,left_pad)),mode='constant',constant_values=0)
   imgR = np.lib.pad(imgR,((0,0),(0,0),(top_pad,0),(0,left_pad)),mode='constant',constant_values=0)

   start_time = time.time()
   pred_disp = test(imgL,imgR)
   print('time = %.2f' %(time.time() - start_time))

   top_pad   = 384-imgL_o.shape[0]
   left_pad  = 1248-imgL_o.shape[1]
   img = pred_disp[top_pad:,:-left_pad]
   print(test_left_img[inx].split('/')[-1])
   if args.save_figure:
       skimage.io.imsave(args.save_path+'/'+test_left_img[inx].split('/')[-1],(img*256).astype('uint16'))
   else:
       np.save(args.save_path+'/'+test_left_img[inx].split('/')[-1][:-4], img)

if name == ‘main’:
main()

2

Please format your code for better legibility using the options at the top of the textbox. Also, you can convert your code to CPU by removing the cuda(s) from your code.

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