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main.py
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main.py
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# --------------------------------------------------------
# RepVGG: Making VGG-style ConvNets Great Again (https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_RepVGG_Making_VGG-Style_ConvNets_Great_Again_CVPR_2021_paper.pdf)
# Github source: https://github.com/DingXiaoH/RepVGG
# Licensed under The MIT License [see LICENSE for details]
# The training script is based on the code of Swin Transformer (https://github.com/microsoft/Swin-Transformer)
# --------------------------------------------------------
import time
import argparse
import datetime
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import accuracy, AverageMeter
from train.config import get_config
from data import build_loader
from train.lr_scheduler import build_scheduler
from train.logger import create_logger
from utils import load_checkpoint, save_checkpoint, get_grad_norm, auto_resume_helper, reduce_tensor, save_latest, update_model_ema, unwrap_model
import copy
from train.optimizer import build_optimizer
from repvggplus import create_RepVGGplus_by_name
try:
# noinspection PyUnresolvedReferences
from apex import amp
except ImportError:
amp = None
def parse_option():
parser = argparse.ArgumentParser('RepOpt-VGG training script built on the codebase of Swin Transformer', add_help=False)
parser.add_argument(
"--opts",
help="Modify config options by adding 'KEY VALUE' pairs. ",
default=None,
nargs='+',
)
# easy config modification
parser.add_argument('--arch', default=None, type=str, help='arch name')
parser.add_argument('--batch-size', default=128, type=int, help="batch size for single GPU")
parser.add_argument('--data-path', default='/your/path/to/dataset', type=str, help='path to dataset')
parser.add_argument('--scales-path', default=None, type=str, help='path to the trained Hyper-Search model')
parser.add_argument('--zip', action='store_true', help='use zipped dataset instead of folder dataset')
parser.add_argument('--cache-mode', type=str, default='part', choices=['no', 'full', 'part'],
help='no: no cache, '
'full: cache all data, '
'part: sharding the dataset into nonoverlapping pieces and only cache one piece')
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('--amp-opt-level', type=str, default='O0', choices=['O0', 'O1', 'O2'], #TODO Note: use amp if you have it
help='mixed precision opt level, if O0, no amp is used')
parser.add_argument('--output', default='/your/path/to/save/dir', 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')
parser.add_argument('--eval', action='store_true', help='Perform evaluation only')
parser.add_argument('--throughput', action='store_true', help='Test throughput only')
# distributed training
parser.add_argument("--local_rank", type=int, default=0, help='local rank for DistributedDataParallel')
args, unparsed = parser.parse_known_args()
config = get_config(args)
return args, config
def main(config):
dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn = build_loader(config)
logger.info(f"Creating model:{config.MODEL.ARCH}")
model = create_RepVGGplus_by_name(config.MODEL.ARCH, deploy=False, use_checkpoint=args.use_checkpoint)
optimizer = build_optimizer(config, model)
logger.info(str(model))
model.cuda()
if torch.cuda.device_count() > 1:
if config.AMP_OPT_LEVEL != "O0":
model, optimizer = amp.initialize(model, optimizer, opt_level=config.AMP_OPT_LEVEL)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[config.LOCAL_RANK],
broadcast_buffers=False)
model_without_ddp = model.module
else:
if config.AMP_OPT_LEVEL != "O0":
model, optimizer = amp.initialize(model, optimizer, opt_level=config.AMP_OPT_LEVEL)
model_without_ddp = model
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
logger.info(f"number of params: {n_parameters}")
if hasattr(model_without_ddp, 'flops'):
flops = model_without_ddp.flops()
logger.info(f"number of GFLOPs: {flops / 1e9}")
if config.THROUGHPUT_MODE:
throughput(data_loader_val, model, logger)
return
if config.EVAL_MODE:
load_weights(model, config.MODEL.RESUME)
acc1, acc5, loss = validate(config, data_loader_val, model)
logger.info(f"Only eval. top-1 acc, top-5 acc, loss: {acc1:.3f}, {acc5:.3f}, {loss:.5f}")
return
lr_scheduler = build_scheduler(config, optimizer, len(data_loader_train))
if config.AUG.MIXUP > 0.:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif config.MODEL.LABEL_SMOOTHING > 0.:
criterion = LabelSmoothingCrossEntropy(smoothing=config.MODEL.LABEL_SMOOTHING)
else:
criterion = torch.nn.CrossEntropyLoss()
max_accuracy = 0.0
max_ema_accuracy = 0.0
if config.TRAIN.EMA_ALPHA > 0 and (not config.EVAL_MODE) and (not config.THROUGHPUT_MODE):
model_ema = copy.deepcopy(model)
else:
model_ema = None
if config.TRAIN.AUTO_RESUME:
resume_file = auto_resume_helper(config.OUTPUT)
if resume_file:
if config.MODEL.RESUME:
logger.warning(f"auto-resume changing resume file from {config.MODEL.RESUME} to {resume_file}")
config.defrost()
config.MODEL.RESUME = resume_file
config.freeze()
logger.info(f'auto resuming from {resume_file}')
else:
logger.info(f'no checkpoint found in {config.OUTPUT}, ignoring auto resume')
if (not config.THROUGHPUT_MODE) and config.MODEL.RESUME:
max_accuracy = load_checkpoint(config, model_without_ddp, optimizer, lr_scheduler, logger, model_ema=model_ema)
logger.info("Start training")
start_time = time.time()
for epoch in range(config.TRAIN.START_EPOCH, config.TRAIN.EPOCHS):
data_loader_train.sampler.set_epoch(epoch)
train_one_epoch(config, model, criterion, data_loader_train, optimizer, epoch, mixup_fn, lr_scheduler, model_ema=model_ema)
if dist.get_rank() == 0:
save_latest(config, epoch, model_without_ddp, max_accuracy, optimizer, lr_scheduler, logger, model_ema=model_ema)
if epoch % config.SAVE_FREQ == 0:
save_checkpoint(config, epoch, model_without_ddp, max_accuracy, optimizer, lr_scheduler, logger, model_ema=model_ema)
if epoch % config.SAVE_FREQ == 0 or epoch >= (config.TRAIN.EPOCHS - 10):
if data_loader_val is not None:
acc1, acc5, loss = validate(config, data_loader_val, model)
logger.info(f"Accuracy of the network at epoch {epoch}: {acc1:.3f}%")
max_accuracy = max(max_accuracy, acc1)
logger.info(f'Max accuracy: {max_accuracy:.2f}%')
if max_accuracy == acc1 and dist.get_rank() == 0:
save_checkpoint(config, epoch, model_without_ddp, max_accuracy, optimizer, lr_scheduler, logger,
is_best=True, model_ema=model_ema)
if model_ema is not None:
if data_loader_val is not None:
acc1, acc5, loss = validate(config, data_loader_val, model_ema)
logger.info(f"EMAAccuracy of the network at epoch {epoch} test images: {acc1:.3f}%")
max_ema_accuracy = max(max_ema_accuracy, acc1)
logger.info(f'EMAMax accuracy: {max_ema_accuracy:.2f}%')
if max_ema_accuracy == acc1 and dist.get_rank() == 0:
best_ema_path = os.path.join(config.OUTPUT, 'best_ema.pth')
logger.info(f"{best_ema_path} best EMA saving......")
torch.save(unwrap_model(model_ema).state_dict(), best_ema_path)
else:
latest_ema_path = os.path.join(config.OUTPUT, 'latest_ema.pth')
logger.info(f"{latest_ema_path} latest EMA saving......")
torch.save(unwrap_model(model_ema).state_dict(), latest_ema_path)
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 train_one_epoch(config, model, criterion, data_loader, optimizer, epoch, mixup_fn, lr_scheduler, model_ema=None):
model.train()
optimizer.zero_grad()
num_steps = len(data_loader)
batch_time = AverageMeter()
loss_meter = AverageMeter()
norm_meter = AverageMeter()
start = time.time()
end = time.time()
for idx, (samples, targets) in enumerate(data_loader):
samples = samples.cuda(non_blocking=True)
targets = targets.cuda(non_blocking=True)
if mixup_fn is not None:
samples, targets = mixup_fn(samples, targets)
outputs = model(samples)
if type(outputs) is dict:
loss = 0.0
for name, pred in outputs.items():
if 'aux' in name:
loss += 0.1 * criterion(pred, targets)
else:
loss += criterion(pred, targets)
else:
loss = criterion(outputs, targets)
if config.TRAIN.ACCUMULATION_STEPS > 1:
loss = loss / config.TRAIN.ACCUMULATION_STEPS
if config.AMP_OPT_LEVEL != "O0":
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
if config.TRAIN.CLIP_GRAD:
grad_norm = torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), config.TRAIN.CLIP_GRAD)
else:
grad_norm = get_grad_norm(amp.master_params(optimizer))
else:
loss.backward()
if config.TRAIN.CLIP_GRAD:
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:
optimizer.step()
optimizer.zero_grad()
lr_scheduler.step_update(epoch * num_steps + idx)
else:
optimizer.zero_grad()
if config.AMP_OPT_LEVEL != "O0":
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
if config.TRAIN.CLIP_GRAD:
grad_norm = torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), config.TRAIN.CLIP_GRAD)
else:
grad_norm = get_grad_norm(amp.master_params(optimizer))
else:
loss.backward()
if config.TRAIN.CLIP_GRAD:
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.TRAIN.CLIP_GRAD)
else:
grad_norm = get_grad_norm(model.parameters())
optimizer.step()
lr_scheduler.step_update(epoch * num_steps + idx)
torch.cuda.synchronize()
loss_meter.update(loss.item(), targets.size(0))
norm_meter.update(grad_norm)
batch_time.update(time.time() - end)
if model_ema is not None:
update_model_ema(config, dist.get_world_size(), model=model, model_ema=model_ema, cur_epoch=epoch, cur_iter=idx)
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'loss {loss_meter.val:.4f} ({loss_meter.avg:.4f})\t'
f'grad_norm {norm_meter.val:.4f} ({norm_meter.avg:.4f})\t'
f'mem {memory_used:.0f}MB')
epoch_time = time.time() - start
logger.info(f"EPOCH {epoch} training takes {datetime.timedelta(seconds=int(epoch_time))}")
@torch.no_grad()
def validate(config, data_loader, model):
criterion = torch.nn.CrossEntropyLoss()
model.eval()
batch_time = AverageMeter()
loss_meter = AverageMeter()
acc1_meter = AverageMeter()
acc5_meter = AverageMeter()
end = time.time()
for idx, (images, target) in enumerate(data_loader):
images = images.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
# compute output
output = model(images)
# =============================== deepsup part
if type(output) is dict:
output = output['main']
# measure accuracy and record loss
loss = criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
acc1 = reduce_tensor(acc1)
acc5 = reduce_tensor(acc5)
loss = reduce_tensor(loss)
loss_meter.update(loss.item(), target.size(0))
acc1_meter.update(acc1.item(), target.size(0))
acc5_meter.update(acc5.item(), target.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if idx % config.PRINT_FREQ == 0:
memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0)
logger.info(
f'Test: [{idx}/{len(data_loader)}]\t'
f'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
f'Loss {loss_meter.val:.4f} ({loss_meter.avg:.4f})\t'
f'Acc@1 {acc1_meter.val:.3f} ({acc1_meter.avg:.3f})\t'
f'Acc@5 {acc5_meter.val:.3f} ({acc5_meter.avg:.3f})\t'
f'Mem {memory_used:.0f}MB')
logger.info(f' * Acc@1 {acc1_meter.avg:.3f} Acc@5 {acc5_meter.avg:.3f}')
return acc1_meter.avg, acc5_meter.avg, loss_meter.avg
@torch.no_grad()
def throughput(data_loader, model, logger):
model.eval()
for idx, (images, _) in enumerate(data_loader):
images = images.cuda(non_blocking=True)
batch_size = images.shape[0]
for i in range(50):
model(images)
torch.cuda.synchronize()
logger.info(f"throughput averaged with 30 times")
tic1 = time.time()
for i in range(30):
model(images)
torch.cuda.synchronize()
tic2 = time.time()
throughput = 30 * batch_size / (tic2 - tic1)
logger.info(f"batch_size {batch_size} throughput {throughput}")
return
import os
if __name__ == '__main__':
args, config = parse_option()
if config.AMP_OPT_LEVEL != "O0":
assert amp is not None, "amp not installed!"
if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
rank = int(os.environ["RANK"])
world_size = int(os.environ['WORLD_SIZE'])
else:
rank = -1
world_size = -1
torch.cuda.set_device(config.LOCAL_RANK)
torch.distributed.init_process_group(backend='nccl', init_method='env:https://', world_size=world_size, rank=rank)
torch.distributed.barrier()
seed = config.SEED + dist.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
cudnn.benchmark = True
if not config.EVAL_MODE:
# linear scale the learning rate according to total batch size, may not be optimal
linear_scaled_lr = config.TRAIN.BASE_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 256.0
linear_scaled_warmup_lr = config.TRAIN.WARMUP_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 256.0
linear_scaled_min_lr = config.TRAIN.MIN_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 256.0
# gradient accumulation also need to scale the learning rate
if config.TRAIN.ACCUMULATION_STEPS > 1:
linear_scaled_lr = linear_scaled_lr * config.TRAIN.ACCUMULATION_STEPS
linear_scaled_warmup_lr = linear_scaled_warmup_lr * config.TRAIN.ACCUMULATION_STEPS
linear_scaled_min_lr = linear_scaled_min_lr * config.TRAIN.ACCUMULATION_STEPS
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()
print('==========================================')
print('real base lr: ', config.TRAIN.BASE_LR)
print('==========================================')
os.makedirs(config.OUTPUT, exist_ok=True)
logger = create_logger(output_dir=config.OUTPUT, dist_rank=0 if torch.cuda.device_count() == 1 else dist.get_rank(), name=f"{config.MODEL.ARCH}")
if torch.cuda.device_count() == 1 or 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}")
# print config
logger.info(config.dump())
main(config)