Spaces:
Runtime error
Runtime error
File size: 9,369 Bytes
83d8d3c |
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 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from backbones import get_model
from dataset import get_dataloader
from losses import CombinedMarginLoss
from lr_scheduler import PolyScheduler
from partial_fc_v2 import PartialFC_V2
from torch import distributed
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from utils.utils_callbacks import CallBackLogging
from utils.utils_callbacks import CallBackVerification
from utils.utils_config import get_config
from utils.utils_distributed_sampler import setup_seed
from utils.utils_logging import AverageMeter
from utils.utils_logging import init_logging
assert (
torch.__version__ >= "1.12.0"
), "In order to enjoy the features of the new torch, \
we have upgraded the torch to 1.12.0. torch before than 1.12.0 may not work in the future."
try:
rank = int(os.environ["RANK"])
local_rank = int(os.environ["LOCAL_RANK"])
world_size = int(os.environ["WORLD_SIZE"])
distributed.init_process_group("nccl")
except KeyError:
rank = 0
local_rank = 0
world_size = 1
distributed.init_process_group(
backend="nccl",
init_method="tcp://127.0.0.1:12584",
rank=rank,
world_size=world_size,
)
def main(args):
# get config
cfg = get_config(args.config)
# global control random seed
setup_seed(seed=cfg.seed, cuda_deterministic=False)
torch.cuda.set_device(local_rank)
os.makedirs(cfg.output, exist_ok=True)
init_logging(rank, cfg.output)
summary_writer = SummaryWriter(log_dir=os.path.join(cfg.output, "tensorboard")) if rank == 0 else None
wandb_logger = None
if cfg.using_wandb:
import wandb
# Sign in to wandb
try:
wandb.login(key=cfg.wandb_key)
except Exception as e:
print("WandB Key must be provided in config file (base.py).")
print(f"Config Error: {e}")
# Initialize wandb
run_name = datetime.now().strftime("%y%m%d_%H%M") + f"_GPU{rank}"
run_name = run_name if cfg.suffix_run_name is None else run_name + f"_{cfg.suffix_run_name}"
try:
wandb_logger = (
wandb.init(
entity=cfg.wandb_entity,
project=cfg.wandb_project,
sync_tensorboard=True,
resume=cfg.wandb_resume,
name=run_name,
notes=cfg.notes,
)
if rank == 0 or cfg.wandb_log_all
else None
)
if wandb_logger:
wandb_logger.config.update(cfg)
except Exception as e:
print("WandB Data (Entity and Project name) must be provided in config file (base.py).")
print(f"Config Error: {e}")
train_loader = get_dataloader(cfg.rec, local_rank, cfg.batch_size, cfg.dali, cfg.seed, cfg.num_workers)
backbone = get_model(cfg.network, dropout=0.0, fp16=cfg.fp16, num_features=cfg.embedding_size).cuda()
backbone = torch.nn.parallel.DistributedDataParallel(
module=backbone, broadcast_buffers=False, device_ids=[local_rank], bucket_cap_mb=16, find_unused_parameters=True
)
backbone.train()
# FIXME using gradient checkpoint if there are some unused parameters will cause error
backbone._set_static_graph()
margin_loss = CombinedMarginLoss(
64, cfg.margin_list[0], cfg.margin_list[1], cfg.margin_list[2], cfg.interclass_filtering_threshold
)
if cfg.optimizer == "sgd":
module_partial_fc = PartialFC_V2(margin_loss, cfg.embedding_size, cfg.num_classes, cfg.sample_rate, cfg.fp16)
module_partial_fc.train().cuda()
# TODO the params of partial fc must be last in the params list
opt = torch.optim.SGD(
params=[{"params": backbone.parameters()}, {"params": module_partial_fc.parameters()}],
lr=cfg.lr,
momentum=0.9,
weight_decay=cfg.weight_decay,
)
elif cfg.optimizer == "adamw":
module_partial_fc = PartialFC_V2(margin_loss, cfg.embedding_size, cfg.num_classes, cfg.sample_rate, cfg.fp16)
module_partial_fc.train().cuda()
opt = torch.optim.AdamW(
params=[{"params": backbone.parameters()}, {"params": module_partial_fc.parameters()}],
lr=cfg.lr,
weight_decay=cfg.weight_decay,
)
else:
raise
cfg.total_batch_size = cfg.batch_size * world_size
cfg.warmup_step = cfg.num_image // cfg.total_batch_size * cfg.warmup_epoch
cfg.total_step = cfg.num_image // cfg.total_batch_size * cfg.num_epoch
lr_scheduler = PolyScheduler(
optimizer=opt, base_lr=cfg.lr, max_steps=cfg.total_step, warmup_steps=cfg.warmup_step, last_epoch=-1
)
start_epoch = 0
global_step = 0
if cfg.resume:
dict_checkpoint = torch.load(os.path.join(cfg.output, f"checkpoint_gpu_{rank}.pt"))
start_epoch = dict_checkpoint["epoch"]
global_step = dict_checkpoint["global_step"]
backbone.module.load_state_dict(dict_checkpoint["state_dict_backbone"])
module_partial_fc.load_state_dict(dict_checkpoint["state_dict_softmax_fc"])
opt.load_state_dict(dict_checkpoint["state_optimizer"])
lr_scheduler.load_state_dict(dict_checkpoint["state_lr_scheduler"])
del dict_checkpoint
for key, value in cfg.items():
num_space = 25 - len(key)
logging.info(": " + key + " " * num_space + str(value))
callback_verification = CallBackVerification(
val_targets=cfg.val_targets, rec_prefix=cfg.rec, summary_writer=summary_writer, wandb_logger=wandb_logger
)
callback_logging = CallBackLogging(
frequent=cfg.frequent,
total_step=cfg.total_step,
batch_size=cfg.batch_size,
start_step=global_step,
writer=summary_writer,
)
loss_am = AverageMeter()
amp = torch.cuda.amp.grad_scaler.GradScaler(growth_interval=100)
for epoch in range(start_epoch, cfg.num_epoch):
if isinstance(train_loader, DataLoader):
train_loader.sampler.set_epoch(epoch)
for _, (img, local_labels) in enumerate(train_loader):
global_step += 1
local_embeddings = backbone(img)
loss: torch.Tensor = module_partial_fc(local_embeddings, local_labels)
if cfg.fp16:
amp.scale(loss).backward()
if global_step % cfg.gradient_acc == 0:
amp.unscale_(opt)
torch.nn.utils.clip_grad_norm_(backbone.parameters(), 5)
amp.step(opt)
amp.update()
opt.zero_grad()
else:
loss.backward()
if global_step % cfg.gradient_acc == 0:
torch.nn.utils.clip_grad_norm_(backbone.parameters(), 5)
opt.step()
opt.zero_grad()
lr_scheduler.step()
with torch.no_grad():
if wandb_logger:
wandb_logger.log(
{
"Loss/Step Loss": loss.item(),
"Loss/Train Loss": loss_am.avg,
"Process/Step": global_step,
"Process/Epoch": epoch,
}
)
loss_am.update(loss.item(), 1)
callback_logging(global_step, loss_am, epoch, cfg.fp16, lr_scheduler.get_last_lr()[0], amp)
if global_step % cfg.verbose == 0 and global_step > 0:
callback_verification(global_step, backbone)
if cfg.save_all_states:
checkpoint = {
"epoch": epoch + 1,
"global_step": global_step,
"state_dict_backbone": backbone.module.state_dict(),
"state_dict_softmax_fc": module_partial_fc.state_dict(),
"state_optimizer": opt.state_dict(),
"state_lr_scheduler": lr_scheduler.state_dict(),
}
torch.save(checkpoint, os.path.join(cfg.output, f"checkpoint_gpu_{rank}.pt"))
if rank == 0:
path_module = os.path.join(cfg.output, "model.pt")
torch.save(backbone.module.state_dict(), path_module)
if wandb_logger and cfg.save_artifacts:
artifact_name = f"{run_name}_E{epoch}"
model = wandb.Artifact(artifact_name, type="model")
model.add_file(path_module)
wandb_logger.log_artifact(model)
if cfg.dali:
train_loader.reset()
if rank == 0:
path_module = os.path.join(cfg.output, "model.pt")
torch.save(backbone.module.state_dict(), path_module)
if wandb_logger and cfg.save_artifacts:
artifact_name = f"{run_name}_Final"
model = wandb.Artifact(artifact_name, type="model")
model.add_file(path_module)
wandb_logger.log_artifact(model)
if __name__ == "__main__":
torch.backends.cudnn.benchmark = True
parser = argparse.ArgumentParser(description="Distributed Arcface Training in Pytorch")
parser.add_argument("config", type=str, help="py config file")
main(parser.parse_args())
|