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"""
Train a diffusion model on images.
"""
from pdb import set_trace as st
import random
import json
import sys
import os
sys.path.append('.')
import torch.distributed as dist
import traceback
import torch as th
# if th.cuda.is_available(): # FIXME
# from xformers.triton import FusedLayerNorm as LayerNorm
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_nv_util import TrainLoop3DRecNV, TrainLoop3DRec, TrainLoop3DRecNVPatch, TrainLoop3DRecNVPatchSingleForward, TrainLoop3DRecNVPatchSingleForwardMV, TrainLoop3DRecNVPatchSingleForwardMV_NoCrop, TrainLoop3DRecNVPatchSingleForwardMVAdvLoss, TrainLoop3DRecNVPatchSingleForwardMV_NoCrop_adv
from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default, dataset_defaults
# from datasets import g_buffer_objaverse
from nsr.losses.builder import E3DGELossClass, E3DGE_with_AdvLoss
# 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
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"global_rank={args.global_rank}, local_rank={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,
**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,
**args_to_dict(args,
dataset_defaults().keys())
)
# 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,
wds_split=args.wds_split,
# 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,
'nv_rec_patch_mvE_gs': TrainLoop3DRecNVPatchSingleForwardMV_NoCrop,
'nv_rec_patch_mvE_gs_disc': TrainLoop3DRecNVPatchSingleForwardMV_NoCrop_adv,
}[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))
# train_loop.rendering_kwargs = args.rendering_kwargs
if args.inference:
camera = th.load('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,
wds_split=1, # out of 4
)
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__":
th.multiprocessing.set_start_method('spawn')
# 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:
# for multi-node training
if dist_util.get_world_size() > 1:
args.global_rank = int(os.environ["RANK"])
else:
args.global_rank = 0
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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