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"""
Train a diffusion model on images.
"""
# import imageio
import gzip
import random
import json
import sys
import os
import lmdb
from tqdm import tqdm
sys.path.append('.')
import torch.distributed as dist
import pickle
import traceback
from PIL import Image
import torch as th
import torch.multiprocessing as mp
import lzma
import numpy as np
from torch.utils.data import DataLoader, Dataset
import imageio.v3 as iio
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_data_for_lmdb, load_eval_data, load_memory_data
from nsr.losses.builder import E3DGELossClass
from datasets.eg3d_dataset import init_dataset_kwargs
from pdb import set_trace as st
import bz2
# 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()
if args.objv_dataset:
from datasets.g_buffer_objaverse import load_data, load_eval_data, load_memory_data, load_data_for_lmdb
else: # shapenet
from datasets.shapenet import load_data, load_eval_data, load_memory_data, load_data_for_lmdb
logger.log("creating data loader...")
# data = load_data(
# 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 ('afhq', 'ffhq'):
# ! load data
logger.log("creating eg3d data loader...")
training_set_kwargs, dataset_name = init_dataset_kwargs(data=args.data_dir,
class_name='datasets.eg3d_dataset.ImageFolderDatasetLMDB',
reso_gt=args.image_size) # only load pose here
# if args.cond and not training_set_kwargs.use_labels:
# raise Exception('check here')
# training_set_kwargs.use_labels = args.cond
training_set_kwargs.use_labels = True
training_set_kwargs.xflip = False
training_set_kwargs.random_seed = SEED
# training_set_kwargs.max_size = args.dataset_size
# desc = f'{args.cfg:s}-{dataset_name:s}-gpus{c.num_gpus:d}-batch{c.batch_size:d}-gamma{c.loss_kwargs.r1_gamma:g}'
# * construct ffhq/afhq dataset
training_set = dnnlib.util.construct_class_by_name(
**training_set_kwargs) # subclass of training.dataset.Dataset
dataset_size = len(training_set)
# training_set_sampler = InfiniteSampler(
# dataset=training_set,
# rank=dist_util.get_rank(),
# num_replicas=dist_util.get_world_size(),
# seed=SEED)
data = DataLoader(
training_set,
shuffle=False,
batch_size=1,
num_workers=16,
drop_last=False,
# prefetch_factor=2,
pin_memory=True,
persistent_workers=True,
)
else:
# data, dataset_name, dataset_size = load_data_for_lmdb(
data, dataset_name, dataset_size, _ = load_data_for_lmdb(
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
# 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=auto_encoder.preprocess)
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
def convert_to_lmdb(dataset_loader, lmdb_path):
"""
Convert a PyTorch dataset to LMDB format.
Parameters:
- dataset: PyTorch dataset
- lmdb_path: Path to store the LMDB database
"""
env = lmdb.open(lmdb_path, map_size=1024 ** 4, readahead=False) # Adjust map_size based on your dataset size
with env.begin(write=True) as txn:
for idx, sample in enumerate(tqdm(dataset_loader)):
# remove the batch index of returned dict sample
sample = {
k:v.squeeze(0).cpu().numpy() if isinstance(v, th.Tensor) else v[0]
for k, v in sample.items()
}
# sample = dataset_loader[idx]
key = str(idx).encode('utf-8')
value = pickle.dumps(sample)
txn.put(key, value)
# txn.put("length".encode("utf-8"), f'{imgset_size}'.encode("utf-8")) # ! will incur bug in dataloading.
# txn.put("start_idx".encode("utf-8"), f'{start_idx}'.encode("utf-8"))
# txn.put("end_idx".encode("utf-8"), f'{end_idx}'.encode("utf-8"))
# env.close()
import zlib
# Function to encode and compress an image
# def encode_and_compress_image(image_path):
# def encode_and_compress_image(image):
# # Open and encode the image
# # with open(image_path, 'rb') as f:
# # image = Image.open(f)
# encoded_data = image.tobytes()
# # Compress the encoded data
# # Compress the image data using bz2
# compressed_data = gzip.compress(encoded_data)
# # compressed_data = bz2.compress(encoded_data)
# # compressed_data = lzma.compress(encoded_data)
# # compressed_data = zlib.compress(encoded_data)
# return compressed_data
# Function to compress an image using gzip
# def compress_image_gzip(image_path):
def encode_and_compress_image(inp_array, is_image=False, compress=True):
# Read the image using imageio
# image = imageio.v3.imread(image_path)
# Convert the image to bytes
# with io.BytesIO() as byte_buffer:
# imageio.imsave(byte_buffer, image, format="png")
# image_bytes = byte_buffer.getvalue()
if is_image:
inp_bytes = iio.imwrite("<bytes>", inp_array, extension=".png")
else:
inp_bytes = inp_array.tobytes()
# Compress the image data using gzip
if compress:
compressed_data = gzip.compress(inp_bytes)
return compressed_data
else:
return inp_bytes
def convert_to_lmdb_compressed(dataset_loader, lmdb_path, dataset_size):
"""
Convert a PyTorch dataset to LMDB format.
Parameters:
- dataset: PyTorch dataset
- lmdb_path: Path to store the LMDB database
"""
env = lmdb.open(lmdb_path, map_size=1024 ** 4, readahead=False) # Adjust map_size based on your dataset size
# with env.begin(write=True) as txn:
with env.begin(write=True) as txn:
txn.put("length".encode("utf-8"), str(dataset_size).encode("utf-8"))
for idx, sample in enumerate(tqdm(dataset_loader)):
# remove the batch index of returned dict sample
sample = {
k:v.squeeze(0).cpu().numpy() if isinstance(v, th.Tensor) else v[0]
for k, v in sample.items()
}
# sample = dataset_loader[idx]
for k, v in sample.items():
# if idx == 0: # record data shape and type for decoding
# txn.put(f"{k}.shape".encode("utf-8"), str(v.shape).encode("utf-8"))
# txn.put(f"{k}.dtype".encode("utf-8"), str(v.dtype).encode("utf-8"))
key = f'{idx}-{k}'.encode('utf-8')
# value = pickle.dumps(sample)
# if 'depth' in k or 'img' in k:
if 'img' in k: # only bytes required? laod the 512 depth bytes only.
v = encode_and_compress_image(v, is_image=True, compress=False)
# elif 'depth' in k:
else: # regular bytes encoding
if type(v) != str:
v = v.astype(np.float32)
v = encode_and_compress_image(v, is_image=False, compress=False)
else:
v = v.encode("utf-8")
# else: # regular bytes encoding
# v = v.tobytes()
txn.put(key, v)
# txn.put("length".encode("utf-8"), f'{imgset_size}'.encode("utf-8")) # ! will incur bug in dataloading.
# txn.put("start_idx".encode("utf-8"), f'{start_idx}'.encode("utf-8"))
# txn.put("end_idx".encode("utf-8"), f'{end_idx}'.encode("utf-8"))
# env.close()
# convert_to_lmdb(data, os.path.join(logger.get_dir(), dataset_name)) convert_to_lmdb_compressed(data, os.path.join(logger.get_dir(), dataset_name))
convert_to_lmdb_compressed(data, os.path.join(logger.get_dir()), dataset_size)
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
objv_dataset=False,
pose_warm_up_iter=-1,
)
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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