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))