test_diffnet.py

# This code is based on Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation 
# Copyright (c) 2023, Zikang Zhou. 
# Modifications Copyright (c)  Da Saem Lee, 2025
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser

import pytorch_lightning as pl
from torch_geometric.loader import DataLoader

from datasets import ArgoverseV2Dataset
from predictors import TrajNet
from transforms import TargetBuilder
import os

if __name__ == '__main__':
    pl.seed_everything(2023, workers=True)

    parser = ArgumentParser()
    parser.add_argument('--root', type=str, required=True)
    parser.add_argument('--batch_size', type=int, default=4)
    parser.add_argument('--num_workers', type=int, default=4) 
    parser.add_argument('--pin_memory', type=bool, default=True)
    parser.add_argument('--persistent_workers', type=bool, default=True)
    parser.add_argument('--accelerator', type=str, default='auto')
    parser.add_argument('--devices', type=str, default="4,")
    parser.add_argument('--ckpt_path', type=str, required=True)
    parser.add_argument('--sampling', choices=['ddpm','ddim'],default='ddpm')
    parser.add_argument('--sampling_stride', type = int, default = 20)
    parser.add_argument('--num_eval_samples', type = int, default = 6)
    parser.add_argument('--eval_mode_error_2', type = int, default = 1)
    
    parser.add_argument('--guid_sampling', choices=['no_guid', 'guid'],default = 'no_guid')
    parser.add_argument('--guid_task', choices=['none', 'goal', 'target_vel', 'target_vego','rand_goal','rand_goal_rand_o'],default = 'none')
    parser.add_argument('--guid_method', choices=['none', 'ECM', 'ECMR'],default = 'none')
    parser.add_argument('--guid_plot',choices=['no_plot', 'plot'],default = 'no_plot')
    parser.add_argument('--path_pca_V_k', type = str,default = 'none')
    
    args = parser.parse_args()

    model = {
        'DiffNet': DiffNet,
    }['DiffNet'].load_from_checkpoint(checkpoint_path=args.ckpt_path)
    
    model.add_extra_param(args)
    
    
    model.sampling = args.sampling
    model.sampling_stride = args.sampling_stride
    model.check_param()
    model.num_eval_samples = args.num_eval_samples
    model.eval_mode_error_2 = args.eval_mode_error_2
    val_dataset = {
        'argoverse_v2': ArgoverseV2Dataset,
    }[model.dataset](root=args.root, split='test',
                     transform=TargetBuilder(model.init_timestep, model.num_generation_timestep))
    dataloader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers,
                            pin_memory=args.pin_memory, persistent_workers=args.persistent_workers)
    
    
    trainer = pl.Trainer(accelerator=args.accelerator, devices=args.devices, strategy='ddp')
    trainer.test(model, dataloader)