scripts/train_triplane_vae.py

import argparse
import datetime
import json
import numpy as np
import os,sys
sys.path.append("..")
# os.system("taskset -p 0xff %d"%(os.getpid()))
import time
from pathlib import Path

import torch
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter

torch.set_num_threads(8)

import util.misc as misc
from datasets import build_dataset
from util.misc import NativeScalerWithGradNormCount as NativeScaler
from models import get_model

from engine.engine_triplane_vae import train_one_epoch, evaluate


def get_args_parser():
    parser = argparse.ArgumentParser('Autoencoder', add_help=False)
    parser.add_argument('--batch_size', default=64, type=int,
                        help='Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus')
    parser.add_argument('--epochs', default=800, type=int)
    parser.add_argument('--accum_iter', default=1, type=int,
                        help='Accumulate gradient iterations (for increasing the effective batch size under memory constraints)')

    # Optimizer parameters
    parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM',
                        help='Clip gradient norm (default: None, no clipping)')
    parser.add_argument('--weight_decay', type=float, default=0.05,
                        help='weight decay (default: 0.05)')

    parser.add_argument('--lr', type=float, default=None, metavar='LR',
                        help='learning rate (absolute lr)')
    parser.add_argument('--blr', type=float, default=1e-4, metavar='LR',
                        help='base learning rate: absolute_lr = base_lr * total_batch_size / 256')
    parser.add_argument('--layer_decay', type=float, default=0.75,
                        help='layer-wise lr decay from ELECTRA/BEiT')

    parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0')

    parser.add_argument('--warmup_epochs', type=int, default=40, metavar='N',
                        help='epochs to warmup LR')

    parser.add_argument('--output_dir', default='./output/',
                        help='path where to save, empty for no saving')
    parser.add_argument('--log_dir', default='./output/',
                        help='path where to tensorboard log')
    parser.add_argument('--device', default='cuda',
                        help='device to use for training / testing')
    parser.add_argument('--seed', default=0, type=int)
    parser.add_argument('--resume', default='',
                        help='resume from checkpoint')
    parser.add_argument('--data-pth',default="../data",type=str)

    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                        help='start epoch')
    parser.add_argument('--eval', action='store_true',
                        help='Perform evaluation only')
    parser.add_argument('--dist_eval', action='store_true', default=False,
                        help='Enabling distributed evaluation (recommended during training for faster monitor')
    parser.add_argument('--num_workers', default=60, type=int)
    parser.add_argument('--pin_mem', action='store_true',
                        help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
    parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')
    parser.set_defaults(pin_mem=False)

    # distributed training parameters
    parser.add_argument('--world_size', default=1, type=int,
                        help='number of distributed processes')
    parser.add_argument('--local_rank', default=-1, type=int)
    parser.add_argument('--dist_on_itp', action='store_true')
    parser.add_argument('--dist_url', default='env://',
                        help='url used to set up distributed training')

    parser.add_argument('--configs',type=str)
    parser.add_argument('--finetune', default=False, action="store_true")
    parser.add_argument('--finetune-pth', type=str)
    parser.add_argument('--category',type=str)
    parser.add_argument('--replica',type=int,default=8)

    return parser


def main(args,config):
    misc.init_distributed_mode(args)

    print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
    print("{}".format(args).replace(', ', ',\n'))

    device = torch.device(args.device)

    # fix the seed for reproducibility
    seed = args.seed + misc.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)

    cudnn.benchmark = True

    dataset_config=config.config['dataset']
    dataset_config['category']=args.category
    dataset_config['replica']=args.replica
    dataset_config['data_path']=args.data_pth
    dataset_train = build_dataset('train',dataset_config)
    dataset_val = build_dataset('val', dataset_config)

    if True:  # args.distributed:
        num_tasks = misc.get_world_size()
        global_rank = misc.get_rank()
        sampler_train = torch.utils.data.DistributedSampler(
            dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
        )
        print("Sampler_train = %s" % str(sampler_train))
        if args.dist_eval:
            if len(dataset_val) % num_tasks != 0:
                print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
                      'This will slightly alter validation results as extra duplicate entries are added to achieve '
                      'equal num of samples per-process.')
            sampler_val = torch.utils.data.DistributedSampler(
                dataset_val, num_replicas=num_tasks, rank=global_rank,
                shuffle=True)  # shuffle=True to reduce monitor bias
        else:
            sampler_val = torch.utils.data.SequentialSampler(dataset_val)
    else:
        sampler_train = torch.utils.data.RandomSampler(dataset_train)
        sampler_val = torch.utils.data.SequentialSampler(dataset_val)

    if global_rank == 0 and args.log_dir is not None and not args.eval:
        os.makedirs(args.log_dir, exist_ok=True)
        log_writer = SummaryWriter(log_dir=args.log_dir)
    else:
        log_writer = None

    if misc.get_rank() == 0:
        log_dir = args.log_dir
        src_folder = "/data1/haolin/TriplaneDiffusion"
        misc.log_codefiles(src_folder, log_dir + "/code_bak")
        config_dict = vars(args)
        config_save_path = os.path.join(log_dir, "config.json")
        with open(config_save_path, 'w') as f:
            json.dump(config_dict, f, indent=4)
        model_config_path=os.path.join(log_dir,"setup.yaml")
        config.write_config(model_config_path)

    print("dataset len", dataset_train.__len__())
    data_loader_train = torch.utils.data.DataLoader(
        dataset_train, sampler=sampler_train,
        batch_size=args.batch_size,
        num_workers=args.num_workers,
        pin_memory=args.pin_mem,
        drop_last=True,
        prefetch_factor=2,
    )
    print("dataset len", dataset_train.__len__(), "dataloader len", len(data_loader_train))

    data_loader_val = torch.utils.data.DataLoader(
        dataset_val, sampler=sampler_val,
        # batch_size=args.batch_size,
        batch_size=1,
        # num_workers=args.num_workers,
        num_workers=1,
        pin_memory=args.pin_mem,
        drop_last=False
    )

    #model = models_ae.__dict__[args.model](N=args.point_cloud_size)
    model_config=config.config['model']
    model = get_model(model_config)
    if args.finetune:
        print("finetune the model, load from %s"%(args.finetune_pth))
        model.load_state_dict(torch.load(args.finetune_pth)['model'])
    model.to(device)

    model_without_ddp = model
    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)

    print("Model = %s" % str(model_without_ddp))
    print('number of params (M): %.2f' % (n_parameters / 1.e6))

    eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size()

    if args.lr is None:  # only base_lr is specified
        args.lr = args.blr * eff_batch_size / 256

    print("base lr: %.2e" % (args.lr * 256 / eff_batch_size))
    print("actual lr: %.2e" % args.lr)

    print("accumulate grad iterations: %d" % args.accum_iter)
    print("effective batch size: %d" % eff_batch_size)

    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=False)
        model_without_ddp = model.module

    # # build optimizer with layer-wise lr decay (lrd)
    # param_groups = lrd.param_groups_lrd(model_without_ddp, args.weight_decay,
    #     no_weight_decay_list=model_without_ddp.no_weight_decay(),
    #     layer_decay=args.layer_decay
    # )
    optimizer = torch.optim.AdamW(model_without_ddp.parameters(), lr=args.lr)
    loss_scaler = NativeScaler()

    criterion = torch.nn.BCEWithLogitsLoss()

    print("criterion = %s" % str(criterion))

    misc.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)

    if args.eval:
        test_stats = evaluate(data_loader_val, model, device)
        print(f"iou of the network on the {len(dataset_val)} test images: {test_stats['iou']:.3f}")
        exit(0)

    print(f"Start training for {args.epochs} epochs")
    start_time = time.time()
    max_iou = 0.0
    for epoch in range(args.start_epoch, args.epochs):
        # if args.distributed:
        #     data_loader_train.sampler.set_epoch(epoch)
        #test_stats = evaluate(data_loader_val, model, device)
        train_stats = train_one_epoch(
            model, criterion, data_loader_train,
            optimizer, device, epoch, loss_scaler,
            args.clip_grad,
            log_writer=log_writer,
            args=args
        )
        # if args.output_dir and (epoch % 10 == 0 or epoch + 1 == args.epochs):
        #     misc.save_model(
        #         args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
        #         loss_scaler=loss_scaler, epoch=epoch)

        if epoch % 5 == 0 or epoch + 1 == args.epochs:
            test_stats = evaluate(data_loader_val, model, device)
            print(f"iou of the network on the {len(dataset_val)} test images: {test_stats['iou']:.3f}")
            if test_stats["iou"] > max_iou:
                max_iou = test_stats["iou"]
                misc.save_model(
                    args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                    loss_scaler=loss_scaler, epoch=epoch, prefix='best')
            else:
                misc.save_model(
                    args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                    loss_scaler=loss_scaler, epoch=epoch, prefix='latest')
            # max_iou = max(max_iou, test_stats["iou"])
            print(f'Max iou: {max_iou:.2f}%')

            if log_writer is not None:
                log_writer.add_scalar('perf/test_iou', test_stats['iou'], epoch)
                log_writer.add_scalar('perf/test_loss', test_stats['loss'], epoch)

            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                         **{f'test_{k}': v for k, v in test_stats.items()},
                         'epoch': epoch,
                         'n_parameters': n_parameters}
        else:
            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                         'epoch': epoch,
                         'n_parameters': n_parameters}

        if args.output_dir and misc.is_main_process():
            if log_writer is not None:
                log_writer.flush()
            with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
                f.write(json.dumps(log_stats) + "\n")

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print('Training time {}'.format(total_time_str))


if __name__ == '__main__':
    args = get_args_parser()
    args = args.parse_args()
    if args.output_dir:
        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    config_path=args.configs
    from configs.config_utils import CONFIG
    config=CONFIG(config_path)
    main(args,config)