bev_image_ = sample_["lidar_bev_2Dimage"]
test.get_eval_value_onestep(point_voxel_, image_data_,
reference_bboxes_,
num_ref_bboxes_)
test.save_feature_result(bev_image_, reference_bboxes_,
num_ref_bboxes_, batch_ndx_,
epoch)
if batch_ndx_ > 5:
print("accumulated number of true data is ",
test.get_num_T())
print("accumulated number of positive data is ",
test.get_num_P())
print("accumulated number of true positive data is ",
test.get_num_TP_set())
break
test.display_average_precision(
plot_AP_graph=config["plot_AP_graph"])
print("=" * 50)
test.initialize_ap()
for batch_ndx, sample in enumerate(data_loader_test):
image_data = sample['image'].cuda()
point_voxel = sample['pointcloud'].cuda()
reference_bboxes = sample['bboxes'].cpu().clone().detach()
num_ref_bboxes = sample["num_bboxes"]
bev_image = sample["lidar_bev_2Dimage"]
test.get_eval_value_onestep(point_voxel, image_data,
reference_bboxes, num_ref_bboxes)
test.save_feature_result(bev_image, reference_bboxes,
num_ref_bboxes, batch_ndx, epoch)
if batch_ndx > 10:
print("accumulated number of true data is ", test.get_num_T())
print("accumulated number of positive data is ",
