""" 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