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test_tnt.py
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import os
import sys
from os.path import join as pjoin
from datetime import datetime
import argparse
# from torch.utils.data import DataLoader
from torch_geometric.data import DataLoader
from core.dataloader.dataset import GraphDataset
# from core.dataloader.argoverse_loader import Argoverse, GraphData, ArgoverseInMem
from core.dataloader.argoverse_loader_v2 import GraphData, ArgoverseInMem
from core.trainer.tnt_trainer import TNTTrainer
sys.path.append("core/dataloader")
def test(args):
"""
script to test the tnt model
"param args:
:return:
"""
# data loading
test_set = ArgoverseInMem(pjoin(args.data_root, "val_intermediate"))
# init trainer
trainer = TNTTrainer(
trainset=test_set,
evalset=test_set,
testset=test_set,
batch_size=args.batch_size,
num_workers=args.num_workers,
aux_loss=True,
enable_log=False,
with_cuda=args.with_cuda,
cuda_device=args.cuda_device,
ckpt_path=args.resume_checkpoint if hasattr(args, "resume_checkpoint") and args.resume_checkpoint else None,
model_path=args.resume_model if hasattr(args, "resume_model") and args.resume_model else None
)
trainer.test(miss_threshold=2.0)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--data_root", required=False, type=str, default="dataset/interm_tnt_n_s_0804",
help="root dir for datasets")
parser.add_argument("-b", "--batch_size", type=int, default=128,
help="number of batch_size")
parser.add_argument("-w", "--num_workers", type=int, default=16,
help="dataloader worker size")
parser.add_argument("-c", "--with_cuda", action="store_true", default=True,
help="training with CUDA: true, or false")
parser.add_argument("-cd", "--cuda_device", type=int, default=[1, 0], nargs='+',
help="CUDA device ids")
parser.add_argument("-rc", "--resume_checkpoint", type=str,
# default="/home/jb/projects/Code/trajectory-prediction/TNT-Trajectory-Predition/run/tnt/05-21-07-33/checkpoint_iter26.ckpt",
help="resume a checkpoint for fine-tune")
parser.add_argument("-rm", "--resume_model", type=str,
default="/home/jb/projects/Code/trajectory-prediction/TNT-Trajectory-Predition/run/tnt/08-09-19-18/best_TNT.pth",
help="resume a model state for fine-tune")
args = parser.parse_args()
test(args)