ntasks = (len(sys.argv)-1)//5 for task_id in range(ntasks): print("task_id: %i" % task_id) argv_idx = task_id*5 + 1 dir_in = sys.argv[argv_idx] dir_out = sys.argv[argv_idx+1] fn = sys.argv[argv_idx+2] nruns_f = int(sys.argv[argv_idx+3]) nruns_J = int(sys.argv[argv_idx+4]) model_dir = dir_in + "model/" fn_in = dir_in + fn fn_out = dir_out + fn params, data = hand_io.read_hand_instance(model_dir, fn_in + ".txt", False) if data.model.is_mirrored: mirror_factor = -1. else: mirror_factor = 1. start = t.time() for i in range(nruns_f): err = f(params, data.model.nbones, data.model.base_relatives, data.model.parents, data.model.inverse_base_absolutes,data.model.base_positions, data.model.weights,mirror_factor,data.points, data.correspondences) end = t.time() tf = (end - start)/nruns_f print("err:") #print(err)
ntasks = (len(sys.argv) - 1) // 5 for task_id in range(ntasks): print("task_id: %i" % task_id) argv_idx = task_id * 5 + 1 dir_in = sys.argv[argv_idx] dir_out = sys.argv[argv_idx + 1] fn = sys.argv[argv_idx + 2] nruns_f = int(sys.argv[argv_idx + 3]) nruns_J = int(sys.argv[argv_idx + 4]) model_dir = dir_in + "model/" fn_in = dir_in + fn fn_out = dir_out + fn params, data = hand_io.read_hand_instance(model_dir, fn_in + ".txt", False) if data.model.is_mirrored: mirror_factor = -1. else: mirror_factor = 1. start = t.time() for i in range(nruns_f): err = f(params, data.model.nbones, data.model.base_relatives, data.model.parents, data.model.inverse_base_absolutes, data.model.base_positions, data.model.weights, mirror_factor, data.points, data.correspondences) end = t.time() tf = (end - start) / nruns_f print("err:") #print(err)
ntasks = (len(sys.argv)-1)//5 for task_id in range(ntasks): print("task_id: %i" % task_id) argv_idx = task_id*5 + 1 dir_in = sys.argv[argv_idx] dir_out = sys.argv[argv_idx+1] fn = sys.argv[argv_idx+2] nruns_f = int(sys.argv[argv_idx+3]) nruns_J = int(sys.argv[argv_idx+4]) model_dir = dir_in + "model/" fn_in = dir_in + fn fn_out = dir_out + fn params, us, data = hand_io.read_hand_instance(model_dir, fn_in + ".txt", True) all_params = np.append(us.flatten(), params) if data.model.is_mirrored: mirror_factor = -1. else: mirror_factor = 1. start = t.time() for i in range(nruns_f): err = f(all_params, data.model.nbones, data.model.base_relatives, data.model.parents, data.model.inverse_base_absolutes,data.model.base_positions, data.model.weights,mirror_factor,data.points, data.correspondences,data.model.triangles) end = t.time() tf = (end - start)/nruns_f print("err:")