train_vectornet.py

import os
import sys
from os.path import join as pjoin
from datetime import datetime
import json
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
from core.dataloader.argoverse_loader_v2 import ArgoverseInMem as ArgoverseInMemv2, GraphData
from core.trainer.vectornet_trainer import VectorNetTrainer

TEST = False

sys.path.append("core/dataloader")


def train(n_gpu, args):
    """
    script to train the vectornet
    :param args:
    :return:
    """
    # data loading
    train_set = ArgoverseInMemv2(pjoin(args.data_root, "train_intermediate")).shuffle()
    eval_set = ArgoverseInMemv2(pjoin(args.data_root, "val_intermediate"))

    # init output dir
    time_stamp = datetime.now().strftime("%m-%d-%H-%M")
    output_dir = pjoin(args.output_dir, time_stamp)
    if not args.multi_gpu or (args.multi_gpu and n_gpu == 0):
        if os.path.exists(output_dir) and len(os.listdir(output_dir)) > 0:
            raise Exception("The output folder does exists and is not empty! Check the folder.")
        else:
            os.makedirs(output_dir)

            # dump the args
            with open(pjoin(output_dir, 'conf.json'), 'w') as fp:
                json.dump(vars(args), fp, indent=4, separators=(", ", ": "))

    # init trainer
    trainer = VectorNetTrainer(
        trainset=train_set,
        evalset=eval_set,
        testset=eval_set,
        batch_size=args.batch_size,
        num_workers=args.num_workers,
        lr=args.lr,
        warmup_epoch=args.warmup_epoch,
        lr_decay_rate=args.lr_decay_rate,
        lr_update_freq=args.lr_update_freq,
        weight_decay=args.adam_weight_decay,
        betas=(args.adam_beta1, args.adam_beta2),
        num_global_graph_layer=args.num_glayer,
        aux_loss=args.aux_loss,
        with_cuda=args.with_cuda,
        cuda_device=n_gpu,
        multi_gpu=args.multi_gpu,
        save_folder=output_dir,
        log_freq=args.log_freq,
        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
    )

    # resume minimum eval loss
    min_eval_loss = trainer.min_eval_loss

    # training
    for iter_epoch in range(args.n_epoch):
        _ = trainer.train(iter_epoch)

        eval_loss = trainer.eval(iter_epoch)

        if not min_eval_loss:
            min_eval_loss = eval_loss
        elif eval_loss < min_eval_loss:
            # save the model when a lower eval_loss is found
            min_eval_loss = eval_loss
            trainer.save(iter_epoch, min_eval_loss)
            trainer.save_model("best")

    trainer.save_model("final")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument("-d", "--data_root", required=False, type=str, default="dataset/interm_data_small",
                        help="root dir for datasets")
    parser.add_argument("-o", "--output_dir", required=False, type=str, default="run/vectornet/",
                        help="ex)dir to save checkpoint and model")

    parser.add_argument("-l", "--num_glayer", type=int, default=1,
                        help="number of global graph layers")
    parser.add_argument("-a", "--aux_loss", action="store_true", default=True,
                        help="Training with the auxiliary recovery loss")

    parser.add_argument("-b", "--batch_size", type=int, default=256,
                        help="number of batch_size")
    parser.add_argument("-e", "--n_epoch", type=int, default=50,
                        help="number of epochs")
    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, nargs='+', default=[],
    #                     help="CUDA device ids")
    parser.add_argument("-m", "--multi_gpu", action="store_true", default=False,
                        help="training with distributed data parallel: true, or false")
    parser.add_argument("-r", "--local_rank", default=0, type=int,
                        help="the default id of gpu")

    parser.add_argument("--log_freq", type=int, default=2,
                        help="printing loss every n iter: setting n")
    parser.add_argument("--on_memory", type=bool, default=True, help="Loading on memory: true or false")

    parser.add_argument("--lr", type=float, default=1e-3, help="learning rate of adam")
    parser.add_argument("-we", "--warmup_epoch", type=int, default=20,
                        help="the number of warmup epoch with initial learning rate, after the learning rate decays")
    parser.add_argument("-luf", "--lr_update_freq", type=int, default=5,
                        help="learning rate decay frequency for lr scheduler")
    parser.add_argument("-ldr", "--lr_decay_rate", type=float, default=0.9, help="lr scheduler decay rate")

    parser.add_argument("--adam_weight_decay", type=float, default=0.01, help="weight_decay of adam")
    parser.add_argument("--adam_beta1", type=float, default=0.9, help="adam first beta value")
    parser.add_argument("--adam_beta2", type=float, default=0.999, help="adam first beta value")

    parser.add_argument("-rc", "--resume_checkpoint", type=str, help="resume a checkpoint for fine-tune")
    parser.add_argument("-rm", "--resume_model", type=str, help="resume a model state for fine-tune")

    args = parser.parse_args()
    train(args.local_rank, args)