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Running
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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_data, load_eval_data, load_memory_data
from nsr.losses.builder import E3DGELossClass
from pdb import set_trace as st
import json
from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default
import random
import traceback
import json
import sys
import os
def training_loop(args):
# dist_util.setup_dist(rank, master_addr, master_port, args.gpus)
dist_util.setup_dist(args)
# logger.configure(dir=args.logdir, format_strs=["tensorboard", "csv"])
logger.configure(dir=args.logdir)
# print(args)
with open(os.path.join(args.logdir, 'args.json'), 'w') as f:
json.dump(vars(args), f, indent=2)
logger.log("creating data loader...")
# TODO, load shapenet data
# data = load_data(
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
)
eval_data = load_eval_data(
file_path=args.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
)
# try dry run
# batch = next(data)
# batch = None
# logger.log("creating model and diffusion...")
logger.log("creating 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())
# 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(
rec_model=auto_encoder,
# encoder,
# decoder
loss_class=loss_class,
# diffusion=diffusion,
data=data,
eval_data=eval_data,
# 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_interval=5000
).run_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,
eval_batch_size=8,
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/",
)
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()
args.local_rank = int(os.environ["LOCAL_RANK"])
args.gpus = th.cuda.device_count()
opts = args
args.rendering_kwargs = rendering_options_defaults(opts)
# 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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