def test(imgL,
imgR,
image_sizes=None,
calibs_fu=None,
calibs_baseline=None,
calibs_Proj=None,
calibs_Proj_R=None):
model.eval()
with torch.no_grad():
outputs = model(imgL,
imgR,
calibs_fu,
calibs_baseline,
calibs_Proj,
calibs_Proj_R=calibs_Proj_R)
if args.save_feat_map:
# test feature hook
print("*" * 5 + "hook record extractor features" + "*" * 5)
print(module_name_extractors)
print("*" * 5 + "hook record extractor features" + "*" * 5)
pred_disp = outputs['depth_preds']
rets = [pred_disp]
if cfg.RPN3D_ENABLE:
box_pred = make_fcos3d_postprocessor(cfg)(outputs['bbox_cls'],
outputs['bbox_reg'],
outputs['bbox_centerness'],
image_sizes=image_sizes,
calibs_Proj=calibs_Proj)
rets.append(box_pred)
return rets
def test(imgL,
imgR,
image_sizes=None,
calibs_fu=None,
calibs_baseline=None,
calibs_Proj=None,
calibs_Proj_R=None):
model.eval()
with torch.no_grad():
outputs = model(imgL,
imgR,
calibs_fu,
calibs_baseline,
calibs_Proj,
calibs_Proj_R=calibs_Proj_R)
pred_disp = outputs['depth_preds']
rets = [pred_disp]
if cfg.RPN3D_ENABLE:
box_pred = make_fcos3d_postprocessor(cfg)(outputs['bbox_cls'],
outputs['bbox_reg'],
outputs['bbox_centerness'],
image_sizes=image_sizes,
calibs_Proj=calibs_Proj)
rets.append(box_pred)
return rets