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"""
Train a diffusion model on images.
"""
import cv2
from pathlib import Path
import imageio
import random
import json
import sys
import os
from tqdm import tqdm
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
from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default
# from datasets.shapenet import load_data, load_eval_data, load_memory_data, load_dataset
from nsr.losses.builder import E3DGELossClass
from datasets.eg3d_dataset import LMDBDataset_MV_Compressed_eg3d
from dnnlib.util import EasyDict, InfiniteSampler
from pdb import set_trace as st
# th.backends.cuda.matmul.allow_tf32 = True # https://huggingface.co/docs/diffusers/optimization/fp16
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()
# st()
if args.objv_dataset:
from datasets.g_buffer_objaverse import load_data, load_dataset, load_eval_data, load_memory_data
else: # shapenet
from datasets.shapenet import load_data, load_eval_data, load_memory_data, load_dataset
# st()
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
)
else:
if args.cfg in ['ffhq' ]:
training_set = LMDBDataset_MV_Compressed_eg3d(
args.data_dir,
args.image_size,
args.image_size_encoder,
)
training_set_sampler = InfiniteSampler(
dataset=training_set,
rank=dist_util.get_rank(),
num_replicas=dist_util.get_world_size(),
seed=SEED)
data = iter(
th.utils.data.DataLoader(
dataset=training_set,
sampler=training_set_sampler,
batch_size=args.batch_size,
pin_memory=True,
num_workers=args.num_workers,
persistent_workers=args.num_workers>0,
prefetch_factor=max(8//args.batch_size, 2),
))
else:
# st()
# loader = load_data(
loader = load_dataset(
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=None,
dataset_size=args.dataset_size,
trainer_name=args.trainer_name,
use_lmdb=args.use_lmdb,
infi_sampler=False,
# infi_sampler=True,
# 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
)
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=None,
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
# 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,
# }[args.trainer_name]
# TrainLoop(rec_model=auto_encoder,
# loss_class=loss_class,
# data=data,
# eval_data=eval_data,
# **vars(args)).run_loop() # ! overfitting
number = 0
# tgt_dir = Path(f'/mnt/lustre/yslan/3D_Dataset/resized_for_fid/chair/{args.image_size}')
# tgt_dir = Path(f'/mnt/lustre/yslan/3D_Dataset/resized_for_fid/chair-new/{args.image_size}')
# tgt_dir.mkdir(parents=True, exist_ok=True)
for idx, batch in enumerate(tqdm(loader)):
# for idx in tqdm(len(loader)): # ! dataset here, direct reference
# batch = loader[idx]
# worker=3: 2.5it/s; worker=8: 1.47it/s; worker=4, 2.3it/s; worker=1, 1.45it/s
# ! save to target folder for FID/KID
# for idx in range(batch['img'].shape[0]):
# # imageio.v3.imwrite(tgt_dir / f'{number}.png' ,(127.5+127.5*batch['img'][idx].cpu().numpy()).astype(np.uint8))
# cv2.imwrite(str(tgt_dir / f'{number}.png') ,(127.5+127.5*batch['img'][idx].cpu().permute(1,2,0).numpy()).astype(np.uint8))
# number += 1
pass
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,
use_lmdb=False,
objv_dataset=False,
)
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"])
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
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