Spaces:
Running
on
Zero
Running
on
Zero
File size: 11,358 Bytes
11e6f7b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 |
"""
Train a diffusion model on images.
"""
import json
import sys
import os
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 dnnlib.util import EasyDict, InfiniteSampler
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
import nsr
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
from nsr.losses.builder import E3DGELossClass
from torch.utils.data import Subset
from datasets.eg3d_dataset import init_dataset_kwargs
from utils.torch_utils import legacy, misc
from pdb import set_trace as st
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
# th.backends.cuda.matmul.allow_tf32 = True # https://huggingface.co/docs/diffusers/optimization/fp16
SEED = 0
def training_loop(args):
# def training_loop(args):
dist_util.setup_dist(args)
# dist.init_process_group(backend='nccl', init_method='env://', rank=args.local_rank, world_size=th.cuda.device_count())
print(f"{args.local_rank=} init complete")
th.cuda.set_device(args.local_rank)
th.cuda.empty_cache()
th.cuda.manual_seed_all(SEED)
np.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
logger.log("creating data loader...")
# data = load_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
# )
# else:
# data = load_data(
# dataset_size=args.dataset_size,
# 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=auto_encoder.preprocess # clip
# # load_depth=True # for evaluation
# )
# 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=2,
# load_depth=True, # for evaluation
# preprocess=auto_encoder.preprocess)
# ! load pre-trained SR in G
common_kwargs = dict(c_dim=25, img_resolution=512, img_channels=3)
G_kwargs = EasyDict(class_name=None,
z_dim=512,
w_dim=512,
mapping_kwargs=EasyDict())
G_kwargs.channel_base = opts.cbase
G_kwargs.channel_max = opts.cmax
G_kwargs.mapping_kwargs.num_layers = opts.map_depth
G_kwargs.class_name = opts.g_class_name
G_kwargs.fused_modconv_default = 'inference_only' # Speed up training by using regular convolutions instead of grouped convolutions.
G_kwargs.rendering_kwargs = args.rendering_kwargs
G_kwargs.num_fp16_res = 0
G_kwargs.sr_num_fp16_res = 4
G_kwargs.sr_kwargs = 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
G_kwargs.num_fp16_res = opts.g_num_fp16_res
G_kwargs.conv_clamp = 256 if opts.g_num_fp16_res > 0 else None
# creating G
resume_data = th.load(args.resume_checkpoint_EG3D, map_location='cuda:{}'.format(args.local_rank))
G_ema = dnnlib.util.construct_class_by_name(
**G_kwargs, **common_kwargs).train().requires_grad_(False).to(
dist_util.dev()) # subclass of th.nn.Module
for name, module in [
('G_ema', G_ema),
# ('D', D),
]:
misc.copy_params_and_buffers(
resume_data[name], # type: ignore
module,
require_all=True,
# load_except=d_load_except if name == 'D' else [],
)
G_ema.requires_grad_(False)
G_ema.eval()
if args.sr_training:
args.sr_kwargs = G_kwargs.sr_kwargs # uncomment if needs to train with SR module
auto_encoder = create_3DAE_model(
**args_to_dict(args,
encoder_and_nsr_defaults().keys()))
auto_encoder.to(device)
auto_encoder.train()
# * clone G_ema.decoder to auto_encoder triplane
logger.log("AE triplane decoder reuses G_ema decoder...")
auto_encoder.decoder.register_buffer('w_avg', G_ema.backbone.mapping.w_avg)
auto_encoder.decoder.triplane_decoder.decoder.load_state_dict( # type: ignore
G_ema.decoder.state_dict()) # type: ignore
# set grad=False in this manner suppresses the DDP forward no grad error.
for param in auto_encoder.decoder.triplane_decoder.decoder.parameters(): # type: ignore
param.requires_grad_(False)
if args.sr_training:
logger.log("AE triplane decoder reuses G_ema SR module...")
auto_encoder.decoder.triplane_decoder.superresolution.load_state_dict( # type: ignore
G_ema.superresolution.state_dict()) # type: ignore
# set grad=False in this manner suppresses the DDP forward no grad error.
for param in auto_encoder.decoder.triplane_decoder.superresolution.parameters(): # type: ignore
param.requires_grad_(False)
del resume_data, G_ema
th.cuda.empty_cache()
auto_encoder.to(dist_util.dev())
auto_encoder.train()
# ! load FFHQ/AFHQ
# Training set.
# training_set_kwargs, dataset_name = init_dataset_kwargs(data=args.data_dir, class_name='datasets.eg3d_dataset.ImageFolderDatasetPose') # only load pose here
training_set_kwargs, dataset_name = init_dataset_kwargs(data=args.data_dir, class_name='datasets.eg3d_dataset.ImageFolderDataset') # 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
# 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
training_set = dnnlib.util.construct_class_by_name(
**training_set_kwargs) # subclass of training.dataset.Dataset
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,))
# prefetch_factor=2))
eval_data = th.utils.data.DataLoader(dataset=Subset(training_set, np.arange(10)),
batch_size=args.eval_batch_size,
num_workers=1)
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()))
loss_class = E3DGELossClass(device, opt).to(device)
# writer = SummaryWriter() # TODO, add log dir
logger.log("training...")
TrainLoop = {
'cvD': nsr.TrainLoop3DcvD,
'nvsD': nsr.TrainLoop3DcvD_nvsD,
'cano_nvs_cvD': nsr.TrainLoop3DcvD_nvsD_canoD,
'canoD': nsr.TrainLoop3DcvD_canoD
}[args.trainer_name]
TrainLoop(rec_model=auto_encoder,
loss_class=loss_class,
data=data,
eval_data=eval_data,
**vars(args)).run_loop() # ! overfitting
def create_argparser(**kwargs):
# defaults.update(model_and_diffusion_defaults())
defaults = dict(
dataset_size=-1,
trainer_name='cvD',
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/",
resume_checkpoint_EG3D="",
)
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"
# master_addr = '127.0.0.1'
# master_port = dist_util._find_free_port()
# master_port = 31323
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
|