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Running
on
Zero
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"""
Train a diffusion model on images.
"""
import sys
import os
sys.path.append('.')
import torch as th
import torch.multiprocessing as mp
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.script_util import create_3DAE_model, encoder_and_nsr_defaults, create_Triplane, loss_defaults
from datasets.shapenet import load_eval_rays, load_data, load_eval_data
from nsr.losses.builder import E3DGELossClass
from pdb import set_trace as st
def inference_loop(rank, master_addr, master_port, args):
dist_util.setup_dist(rank, master_addr, master_port, args.gpus)
logger.configure(dir=args.logdir)
logger.log("creating eval rays...")
# TODO, load shapenet data
eval_data = load_eval_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
)
# c_list = load_eval_rays(
# file_path=args.data_dir,
# reso=args.image_size,
# reso_encoder=args.image_size_encoder, # 224 -> 128
# )
# try dry run
# batch = next(data)
# batch = None
# logger.log("creating model and diffusion...")
logger.log("loading encoder and NSR decoder...")
auto_encoder = create_Triplane( # basically overfitting tirplane
**args_to_dict(args,
encoder_and_nsr_defaults().keys()))
# auto_encoder = create_3DAE_model(
# **args_to_dict(args,
# encoder_and_nsr_defaults().keys()))
auto_encoder.to(dist_util.dev())
auto_encoder.eval()
# schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()) )
loss_class = E3DGELossClass(dist_util.dev(), opt).to(dist_util.dev())
logger.log("training...")
TrainLoop(
model=auto_encoder,
# encoder,
# decoder
loss_class=loss_class,
# diffusion=diffusion,
data=eval_data, # TODO
# data=batch,
batch_size=args.batch_size,
microbatch=args.microbatch,
lr=args.lr,
ema_rate=args.ema_rate,
log_interval=args.log_interval,
save_interval=args.save_interval,
resume_checkpoint=args.resume_checkpoint,
use_fp16=args.use_fp16,
fp16_scale_growth=args.fp16_scale_growth,
weight_decay=args.weight_decay,
lr_anneal_steps=args.lr_anneal_steps,
).eval_loop() # ! overfitting
def create_argparser(**kwargs):
# defaults.update(model_and_diffusion_defaults())
defaults = dict(
num_workers=4,
local_rank=0,
gpus=1,
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,
microbatch=-1, # -1 disables microbatches
ema_rate="0.9999", # comma-separated list of EMA values
log_interval=10,
save_interval=10000,
resume_checkpoint="",
use_fp16=False,
fp16_scale_growth=1e-3,
data_dir="",
# load_depth=False, # TODO
logdir="/mnt/lustre/yslan/logs/nips23/eval",
)
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"
args = create_argparser().parse_args()
# st()
master_addr = '127.0.0.1'
master_port = dist_util._find_free_port()
# Launch processes.
print('Launching processes...')
th.multiprocessing.set_start_method('spawn')
subprocess_fn = inference_loop
# launch using torch.multiprocessing.spawn
if args.gpus == 1:
subprocess_fn(rank=0, master_addr=master_addr, master_port=master_port, args=args)
else:
th.multiprocessing.spawn(fn=subprocess_fn,
args=(master_addr, master_port,args),
nprocs=args.gpus)
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