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from pytorch_caney.data.datamodules.mim_datamodule \
import build_mim_dataloader
from pytorch_caney.models.mim.mim \
import build_mim_model
from pytorch_caney.training.mim_utils \
import build_optimizer, save_checkpoint
from pytorch_caney.training.mim_utils import get_grad_norm
from pytorch_caney.lr_scheduler import build_scheduler, setup_scaled_lr
from pytorch_caney.ptc_logging import create_logger
from pytorch_caney.config import get_config
import argparse
import datetime
import joblib
import numpy as np
import os
import time
import torch
import torch.cuda.amp as amp
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from timm.utils import AverageMeter
def parse_args():
"""
Parse command-line arguments
"""
parser = argparse.ArgumentParser(
'pytorch-caney implementation of MiM pre-training script',
add_help=False)
parser.add_argument(
'--cfg',
type=str,
required=True,
metavar="FILE",
help='path to config file')
parser.add_argument(
"--data-paths",
nargs='+',
required=True,
help="paths where dataset is stored")
parser.add_argument(
'--dataset',
type=str,
required=True,
help='Dataset to use')
parser.add_argument(
'--batch-size',
type=int,
help="batch size for single GPU")
parser.add_argument(
'--resume',
help='resume from checkpoint')
parser.add_argument(
'--accumulation-steps',
type=int,
help="gradient accumulation steps")
parser.add_argument(
'--use-checkpoint',
action='store_true',
help="whether to use gradient checkpointing to save memory")
parser.add_argument(
'--enable-amp',
action='store_true')
parser.add_argument(
'--disable-amp',
action='store_false',
dest='enable_amp')
parser.set_defaults(enable_amp=True)
parser.add_argument(
'--output',
default='output',
type=str,
metavar='PATH',
help='root of output folder, the full path is ' +
'<output>/<model_name>/<tag> (default: output)')
parser.add_argument(
'--tag',
help='tag of experiment')
args = parser.parse_args()
config = get_config(args)
return args, config
def train(config,
dataloader,
model,
model_wo_ddp,
optimizer,
lr_scheduler,
scaler):
"""
Start pre-training a specific model and dataset.
Args:
config: config object
dataloader: dataloader to use
model: model to pre-train
model_wo_ddp: model to pre-train that is not the DDP version
optimizer: pytorch optimizer
lr_scheduler: learning-rate scheduler
scaler: loss scaler
"""
logger.info("Start training")
start_time = time.time()
for epoch in range(config.TRAIN.START_EPOCH, config.TRAIN.EPOCHS):
dataloader.sampler.set_epoch(epoch)
execute_one_epoch(config, model, dataloader,
optimizer, epoch, lr_scheduler, scaler)
if dist.get_rank() == 0 and \
(epoch % config.SAVE_FREQ == 0 or
epoch == (config.TRAIN.EPOCHS - 1)):
save_checkpoint(config, epoch, model_wo_ddp, 0.,
optimizer, lr_scheduler, scaler, logger)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info('Training time {}'.format(total_time_str))
def execute_one_epoch(config,
model,
dataloader,
optimizer,
epoch,
lr_scheduler,
scaler):
"""
Execute training iterations on a single epoch.
Args:
config: config object
model: model to pre-train
dataloader: dataloader to use
optimizer: pytorch optimizer
epoch: int epoch number
lr_scheduler: learning-rate scheduler
scaler: loss scaler
"""
model.train()
optimizer.zero_grad()
num_steps = len(dataloader)
# Set up logging meters
batch_time = AverageMeter()
data_time = AverageMeter()
loss_meter = AverageMeter()
norm_meter = AverageMeter()
loss_scale_meter = AverageMeter()
start = time.time()
end = time.time()
for idx, (img, mask, _) in enumerate(dataloader):
data_time.update(time.time() - start)
img = img.cuda(non_blocking=True)
mask = mask.cuda(non_blocking=True)
with amp.autocast(enabled=config.ENABLE_AMP):
loss = model(img, mask)
if config.TRAIN.ACCUMULATION_STEPS > 1:
loss = loss / config.TRAIN.ACCUMULATION_STEPS
scaler.scale(loss).backward()
loss.backward()
if config.TRAIN.CLIP_GRAD:
scaler.unscale_(optimizer)
grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(),
config.TRAIN.CLIP_GRAD)
else:
grad_norm = get_grad_norm(model.parameters())
if (idx + 1) % config.TRAIN.ACCUMULATION_STEPS == 0:
scaler.step(optimizer)
optimizer.zero_grad()
scaler.update()
lr_scheduler.step_update(epoch * num_steps + idx)
else:
optimizer.zero_grad()
scaler.scale(loss).backward()
if config.TRAIN.CLIP_GRAD:
scaler.unscale_(optimizer)
grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(),
config.TRAIN.CLIP_GRAD)
else:
grad_norm = get_grad_norm(model.parameters())
scaler.step(optimizer)
scaler.update()
lr_scheduler.step_update(epoch * num_steps + idx)
torch.cuda.synchronize()
loss_meter.update(loss.item(), img.size(0))
norm_meter.update(grad_norm)
loss_scale_meter.update(scaler.get_scale())
batch_time.update(time.time() - end)
end = time.time()
if idx % config.PRINT_FREQ == 0:
lr = optimizer.param_groups[0]['lr']
memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0)
etas = batch_time.avg * (num_steps - idx)
logger.info(
f'Train: [{epoch}/{config.TRAIN.EPOCHS}][{idx}/{num_steps}]\t'
f'eta {datetime.timedelta(seconds=int(etas))} lr {lr:.6f}\t'
f'time {batch_time.val:.4f} ({batch_time.avg:.4f})\t'
f'data_time {data_time.val:.4f} ({data_time.avg:.4f})\t'
f'loss {loss_meter.val:.4f} ({loss_meter.avg:.4f})\t'
f'grad_norm {norm_meter.val:.4f} ({norm_meter.avg:.4f})\t'
f'loss_scale {loss_scale_meter.val:.4f}' +
f' ({loss_scale_meter.avg:.4f})\t'
f'mem {memory_used:.0f}MB')
epoch_time = time.time() - start
logger.info(
f"EPOCH {epoch} training takes " +
f"{datetime.timedelta(seconds=int(epoch_time))}")
def main(config):
"""
Starts training process after building the proper model, optimizer, etc.
Args:
config: config object
"""
pretrain_data_loader = build_mim_dataloader(config, logger)
simmim_model = build_model(config, logger)
simmim_optimizer = build_optimizer(config,
simmim_model,
is_pretrain=True,
logger=logger)
model, model_wo_ddp = make_ddp(simmim_model)
n_iter_per_epoch = len(pretrain_data_loader)
lr_scheduler = build_scheduler(config, simmim_optimizer, n_iter_per_epoch)
scaler = amp.GradScaler()
train(config,
pretrain_data_loader,
model,
model_wo_ddp,
simmim_optimizer,
lr_scheduler,
scaler)
def build_model(config, logger):
logger.info(f"Creating model:{config.MODEL.TYPE}/{config.MODEL.NAME}")
model = build_mim_model(config)
model.cuda()
logger.info(str(model))
return model
def make_ddp(model):
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[int(os.environ["RANK"])], broadcast_buffers=False)
model_without_ddp = model.module
return model, model_without_ddp
def setup_rank_worldsize():
if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
rank = int(os.environ["RANK"])
world_size = int(os.environ['WORLD_SIZE'])
print(f"RANK and WORLD_SIZE in environ: {rank}/{world_size}")
else:
rank = -1
world_size = -1
return rank, world_size
def setup_distributed_processing(rank, world_size):
torch.cuda.set_device(int(os.environ["RANK"]))
torch.distributed.init_process_group(
backend='nccl', init_method='env://', world_size=world_size, rank=rank)
torch.distributed.barrier()
def setup_seeding(config):
seed = config.SEED + dist.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
if __name__ == '__main__':
_, config = parse_args()
rank, world_size = setup_rank_worldsize()
setup_distributed_processing(rank, world_size)
setup_seeding(config)
cudnn.benchmark = True
linear_scaled_lr, linear_scaled_min_lr, linear_scaled_warmup_lr = \
setup_scaled_lr(config)
config.defrost()
config.TRAIN.BASE_LR = linear_scaled_lr
config.TRAIN.WARMUP_LR = linear_scaled_warmup_lr
config.TRAIN.MIN_LR = linear_scaled_min_lr
config.freeze()
os.makedirs(config.OUTPUT, exist_ok=True)
logger = create_logger(output_dir=config.OUTPUT,
dist_rank=dist.get_rank(),
name=f"{config.MODEL.NAME}")
if dist.get_rank() == 0:
path = os.path.join(config.OUTPUT, "config.json")
with open(path, "w") as f:
f.write(config.dump())
logger.info(f"Full config saved to {path}")
logger.info(config.dump())
config_file_name = f'{config.TAG}.config.sav'
config_file_path = os.path.join(config.OUTPUT, config_file_name)
joblib.dump(config, config_file_path)
main(config)
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