scripts/vit_triplane_train.py

"""
Train a diffusion model on images.
"""
import random
import json
import sys
import os

sys.path.append('.')
import torch.distributed as dist

import traceback

import torch as th

import torch.multiprocessing as mp
import numpy as np

import argparse
import dnnlib
from guided_diffusion import dist_util, logger
from guided_diffusion.script_util import (
    args_to_dict,
    add_dict_to_argparser,
)
# from nsr.train_util import TrainLoop3DRec as TrainLoop
from nsr.train_nv_util import TrainLoop3DRecNV, TrainLoop3DRec, TrainLoop3DRecNVPatch, TrainLoop3DRecNVPatchSingleForward, TrainLoop3DRecNVPatchSingleForwardMV, TrainLoop3DRecNVPatchSingleForwardMVAdvLoss

from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default, dataset_defaults
from nsr.losses.builder import E3DGELossClass, E3DGE_with_AdvLoss

from pdb import set_trace as st

# th.backends.cuda.matmul.allow_tf32 = True # https://huggingface.co/docs/diffusers/optimization/fp16
# th.backends.cuda.matmul.allow_tf32 = True
# th.backends.cudnn.allow_tf32 = True
# th.backends.cudnn.enabled = True

enable_tf32 = th.backends.cuda.matmul.allow_tf32 # requires A100

th.backends.cuda.matmul.allow_tf32 = enable_tf32
th.backends.cudnn.allow_tf32 = enable_tf32
th.backends.cudnn.enabled = True


def training_loop(args):
    # def training_loop(args):
    dist_util.setup_dist(args)
    # th.autograd.set_detect_anomaly(True) # type: ignore
    th.autograd.set_detect_anomaly(False)  # type: ignore
    # https://blog.csdn.net/qq_41682740/article/details/126304613

    SEED = args.seed

    # dist.init_process_group(backend='nccl', init_method='env://', rank=args.local_rank, world_size=th.cuda.device_count())
    logger.log(f"{args.local_rank=} init complete, seed={SEED}")
    th.cuda.set_device(args.local_rank)
    th.cuda.empty_cache()

    # * deterministic algorithms flags
    th.cuda.manual_seed_all(SEED)
    np.random.seed(SEED)
    random.seed(SEED)

    # logger.configure(dir=args.logdir, format_strs=["tensorboard", "csv"])
    logger.configure(dir=args.logdir)

    logger.log("creating encoder and NSR decoder...")
    # device = dist_util.dev()
    device = th.device("cuda", args.local_rank)

    # shared eg3d opts
    opts = eg3d_options_default()

    if args.sr_training:
        args.sr_kwargs = dnnlib.EasyDict(
            channel_base=opts.cbase,
            channel_max=opts.cmax,
            fused_modconv_default='inference_only',
            use_noise=True
        )  # ! close noise injection? since noise_mode='none' in eg3d

    auto_encoder = create_3DAE_model(
        **args_to_dict(args,
                       encoder_and_nsr_defaults().keys()))
    auto_encoder.to(device)
    auto_encoder.train()

    logger.log("creating data loader...")
    # data = load_data(
    # st()
    if args.objv_dataset:
        from datasets.g_buffer_objaverse import load_data, load_eval_data, load_memory_data, load_wds_data
    else:  # shapenet
        from datasets.shapenet import load_data, load_eval_data, load_memory_data

    if args.overfitting:
        data = load_memory_data(
            file_path=args.data_dir,
            batch_size=args.batch_size,
            reso=args.image_size,
            reso_encoder=args.image_size_encoder,  # 224 -> 128
            num_workers=args.num_workers,
            # load_depth=args.depth_lambda > 0
            # load_depth=True,  # for evaluation
                    **args_to_dict(args,
                                   dataset_defaults().keys()))
        eval_data = None
    else:
        if args.use_wds:
            # st()
            if args.data_dir == 'NONE':
                with open(args.shards_lst) as f:
                    shards_lst = [url.strip() for url in f.readlines()]
                data = load_wds_data(
                    shards_lst,  # type: ignore
                    args.image_size,
                    args.image_size_encoder,
                    args.batch_size,
                    args.num_workers,
                    #  plucker_embedding=args.plucker_embedding,
                    #  mv_input=args.mv_input,
                    #  split_chunk_input=args.split_chunk_input,
                    **args_to_dict(args,
                                   dataset_defaults().keys()))

            elif not args.inference:
                data = load_wds_data(args.data_dir,
                                     args.image_size,
                                     args.image_size_encoder,
                                     args.batch_size,
                                     args.num_workers,
                                     plucker_embedding=args.plucker_embedding,
                                     mv_input=args.mv_input,
                                     split_chunk_input=args.split_chunk_input)
            else:
                data = None
            # ! load eval

            if args.eval_data_dir == 'NONE':
                with open(args.eval_shards_lst) as f:
                    eval_shards_lst = [url.strip() for url in f.readlines()]
            else:
                eval_shards_lst = args.eval_data_dir  # auto expanded

            eval_data = load_wds_data(
                eval_shards_lst,  # type: ignore
                args.image_size,
                args.image_size_encoder,
                args.eval_batch_size,
                args.num_workers,
                # decode_encode_img_only=args.decode_encode_img_only,
                # plucker_embedding=args.plucker_embedding,
                # load_wds_diff=False,
                # mv_input=args.mv_input,
                # split_chunk_input=args.split_chunk_input,
                **args_to_dict(args,
                               dataset_defaults().keys()))
            # load_instance=True) # TODO

        else:

            if args.inference:
                data = None
            else:
                data = load_data(
                    file_path=args.data_dir,
                    batch_size=args.batch_size,
                    reso=args.image_size,
                    reso_encoder=args.image_size_encoder,  # 224 -> 128
                    num_workers=args.num_workers,
                    load_depth=True,
                    preprocess=auto_encoder.preprocess,  # clip
                    dataset_size=args.dataset_size,
                    trainer_name=args.trainer_name,
                    use_lmdb=args.use_lmdb,
                    use_wds=args.use_wds,
                    use_lmdb_compressed=args.use_lmdb_compressed,
                    plucker_embedding=args.plucker_embedding
                    # load_depth=True # for evaluation
                )

            if args.pose_warm_up_iter > 0:
                overfitting_dataset = load_memory_data(
                    file_path=args.data_dir,
                    batch_size=args.batch_size,
                    reso=args.image_size,
                    reso_encoder=args.image_size_encoder,  # 224 -> 128
                    num_workers=args.num_workers,
                    # load_depth=args.depth_lambda > 0
                    # load_depth=True  # for evaluation
                    **args_to_dict(args,
                                   dataset_defaults().keys()))
                data = [data, overfitting_dataset, args.pose_warm_up_iter]

            eval_data = load_eval_data(
                file_path=args.eval_data_dir,
                batch_size=args.eval_batch_size,
                reso=args.image_size,
                reso_encoder=args.image_size_encoder,  # 224 -> 128
                num_workers=args.num_workers,
                load_depth=True,  # for evaluation
                preprocess=auto_encoder.preprocess,
                # interval=args.interval,
                # use_lmdb=args.use_lmdb,
                # plucker_embedding=args.plucker_embedding,
                # load_real=args.load_real,
                # four_view_for_latent=args.four_view_for_latent,
                # load_extra_36_view=args.load_extra_36_view,
                # shuffle_across_cls=args.shuffle_across_cls,
                **args_to_dict(args,
                               dataset_defaults().keys()))

    logger.log("creating data loader done...")

    args.img_size = [args.image_size_encoder]
    # try dry run
    # batch = next(data)
    # batch = None

    # logger.log("creating model and diffusion...")

    # let all processes sync up before starting with a new epoch of training
    dist_util.synchronize()

    # schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)

    opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()))
    # opt.max_depth, opt.min_depth = args.rendering_kwargs.ray_end, args.rendering_kwargs.ray_start
    if 'disc' in args.trainer_name:
        loss_class = E3DGE_with_AdvLoss(
            device,
            opt,
            # disc_weight=args.patchgan_disc, # rec_cvD_lambda
            disc_factor=args.patchgan_disc_factor,  # reduce D update speed
            disc_weight=args.patchgan_disc_g_weight).to(device)
    else:
        loss_class = E3DGELossClass(device, opt).to(device)

    # writer = SummaryWriter() # TODO, add log dir

    logger.log("training...")

    TrainLoop = {
        'input_rec': TrainLoop3DRec,
        'nv_rec': TrainLoop3DRecNV,
        # 'nv_rec_patch': TrainLoop3DRecNVPatch,
        'nv_rec_patch': TrainLoop3DRecNVPatchSingleForward,
        'nv_rec_patch_mvE': TrainLoop3DRecNVPatchSingleForwardMV,
        'nv_rec_patch_mvE_disc': TrainLoop3DRecNVPatchSingleForwardMVAdvLoss, # default for objaverse
    }[args.trainer_name]

    logger.log("creating TrainLoop done...")

    # th._dynamo.config.verbose=True # th212 required
    # th._dynamo.config.suppress_errors = True
    auto_encoder.decoder.rendering_kwargs = args.rendering_kwargs
    train_loop = TrainLoop(
        rec_model=auto_encoder,
        loss_class=loss_class,
        data=data,
        eval_data=eval_data,
        #   compile=args.compile,
        **vars(args))

    if args.inference:
        camera = th.load('assets/objv_eval_pose.pt', map_location=dist_util.dev())
        train_loop.eval_novelview_loop(camera=camera,
                                       save_latent=args.save_latent)
    else:
        train_loop.run_loop()


def create_argparser(**kwargs):
    # defaults.update(model_and_diffusion_defaults())

    defaults = dict(
        seed=0,
        dataset_size=-1,
        trainer_name='input_rec',
        use_amp=False,
        overfitting=False,
        num_workers=4,
        image_size=128,
        image_size_encoder=224,
        iterations=150000,
        anneal_lr=False,
        lr=5e-5,
        weight_decay=0.0,
        lr_anneal_steps=0,
        batch_size=1,
        eval_batch_size=12,
        microbatch=-1,  # -1 disables microbatches
        ema_rate="0.9999",  # comma-separated list of EMA values
        log_interval=50,
        eval_interval=2500,
        save_interval=10000,
        resume_checkpoint="",
        use_fp16=False,
        fp16_scale_growth=1e-3,
        data_dir="",
        eval_data_dir="",
        # load_depth=False, # TODO
        logdir="/mnt/lustre/yslan/logs/nips23/",
        # test warm up pose sampling training
        pose_warm_up_iter=-1,
        inference=False,
        export_latent=False,
        save_latent=False,
    )

    defaults.update(dataset_defaults())  # type: ignore
    defaults.update(encoder_and_nsr_defaults())  # type: ignore
    defaults.update(loss_defaults())

    parser = argparse.ArgumentParser()
    add_dict_to_argparser(parser, defaults)

    return parser


if __name__ == "__main__":
    # os.environ[
    # "TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"  # set to DETAIL for runtime logging.
    # os.environ["TORCH_CPP_LOG_LEVEL"]="INFO"
    # os.environ["NCCL_DEBUG"]="INFO"

    args = create_argparser().parse_args()
    args.local_rank = int(os.environ["LOCAL_RANK"])
    # if os.environ['WORLD_SIZE'] > 1:
    #     args.global_rank = int(os.environ["RANK"])
    args.gpus = th.cuda.device_count()

    opts = args

    args.rendering_kwargs = rendering_options_defaults(opts)

    # print(args)
    with open(os.path.join(args.logdir, 'args.json'), 'w') as f:
        json.dump(vars(args), f, indent=2)

    # Launch processes.
    print('Launching processes...')

    try:
        training_loop(args)
    # except KeyboardInterrupt as e:
    except Exception as e:
        # print(e)
        traceback.print_exc()
        dist_util.cleanup()  # clean port and socket when ctrl+c