use_gpu_flag = True with_attention_flag = True coor_layer_flag = True no_pad_flag = False ################## init model########################### model = models.VONet.PADVOFeature(coor_layer_flag = coor_layer_flag) model = model.float() # normalization parameter # model and optimization if use_gpu_flag: model = nn.DataParallel(model.cuda()) #model = model.cuda() print(model) model.load_state_dict(torch.load(args.model_load)) ego_pre = ep.EgomotionPrediction() ################### load data#################### motion_files_path_test = args.motion_path_test path_files_path_test = args.image_list_path_test print(motion_files_path_test) print(path_files_path_test) # transform camera_parameter=[640,180,640,640,320,90] image_size =(camera_parameter[1],camera_parameter[0]) transforms_ = [ transforms.Resize(image_size), #transforms.Resize((180,651)),#robocar remap #transforms.Resize((262,651)),#robocar remap transforms.ToTensor(), transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5)) ]
import egomotionprediction as ep import numpy as np quat = [] print(type(quat)) tran = [] for i in range(0, 10): quat.append([0, 0, np.sin(i * 0.1), np.cos(i * 0.1)]) tran.append([0, 0, 0]) epo = ep.EgomotionPrediction() res = epo.predict_patch(quat, tran) print(np.array(res))