def test_kitti_2012(cfg, model, gt_flows, noc_masks):
dataset = KITTI_2012(cfg.gt_2012_dir)
flow_list = []
for idx, inputs in enumerate(tqdm(dataset)):
img, K, K_inv = inputs
img = img[None, :, :, :]
K = K[None, :, :]
K_inv = K_inv[None, :, :]
img_h = int(img.shape[2] / 2)
img1, img2 = img[:, :, :img_h, :], img[:, :, img_h:, :]
img1, img2, K, K_inv = img1.cuda(), img2.cuda(), K.cuda(), K_inv.cuda()
if cfg.mode == 'flow' or cfg.mode == 'flowposenet':
flow = model.inference_flow(img1, img2)
else:
flow, _, _, _, _, _ = model.inference(img1, img2, K, K_inv)
# pdb.set_trace()
flow = flow[0].detach().cpu().numpy()
flow = flow.transpose(1, 2, 0)
flow_list.append(flow)
eval_flow_res = eval_flow_avg(gt_flows,
noc_masks,
flow_list,
cfg,
write_img=False)
print('CONFIG: {0}, mode: {1}'.format(cfg.config_file, cfg.mode))
print('[EVAL] [KITTI 2012]')
print(eval_flow_res)
return eval_flow_res
def test_kitti_2015(cfg,
model,
gt_flows,
noc_masks,
gt_masks,
depth_save_dir=None):
dataset = KITTI_2015(cfg.gt_2015_dir)
visualizer = Visualizer_debug(depth_save_dir)
pred_flow_list = []
pred_disp_list = []
img_list = []
for idx, inputs in enumerate(tqdm(dataset)):
img, K, K_inv = inputs
img = img[None, :, :, :]
K = K[None, :, :]
K_inv = K_inv[None, :, :]
img_h = int(img.shape[2] / 2)
img1, img2 = img[:, :, :img_h, :], img[:, :, img_h:, :]
img_list.append(img1)
img1, img2, K, K_inv = img1.cuda(), img2.cuda(), K.cuda(), K_inv.cuda()
if cfg.mode == 'flow' or cfg.mode == 'flowposenet':
flow = model.inference_flow(img1, img2)
else:
flow, disp1, disp2, Rt, _, _ = model.inference(
img1, img2, K, K_inv)
disp = disp1[0].detach().cpu().numpy()
disp = disp.transpose(1, 2, 0)
pred_disp_list.append(disp)
flow = flow[0].detach().cpu().numpy()
flow = flow.transpose(1, 2, 0)
pred_flow_list.append(flow)
# pdb.set_trace()
eval_flow_res = eval_flow_avg(gt_flows,
noc_masks,
pred_flow_list,
cfg,
moving_masks=gt_masks,
write_img=False)
print('CONFIG: {0}, mode: {1}'.format(cfg.config_file, cfg.mode))
print('[EVAL] [KITTI 2015]')
print(eval_flow_res)
# depth evaluation
return eval_flow_res