text stringlengths 1 93.6k |
|---|
"optimizer": optimizer.state_dict(),
|
"epoch": epoch + 1,
|
"args": args,
|
}
|
if fp16_scaler is not None:
|
save_dict["fp16_scaler"] = fp16_scaler.state_dict()
|
if dist.get_rank() == 0:
|
torch.save(save_dict, os.path.join(args.output_dir, "checkpoint.pth"))
|
if args.saveckpt_freq and epoch % args.saveckpt_freq == 0:
|
shutil.copy(
|
os.path.join(args.output_dir, "checkpoint.pth"),
|
os.path.join(args.output_dir, f"checkpoint_{epoch:04}.pth"),
|
)
|
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(
|
model,
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teachers,
|
teacher_ft_stats,
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data_loader,
|
optimizer,
|
epoch,
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fp16_scaler,
|
ext_logger,
|
args,
|
):
|
logger.info("-" * 50)
|
logger.info("Starting training epoch {}".format(epoch))
|
metrics_file = os.path.join(args.output_dir, "metrics_training.json")
|
metric_logger = MetricLogger(delimiter=" ", output_file=metrics_file)
|
header = "Training - Epoch: [{}/{}]".format(epoch, args.epochs)
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model.train()
|
for it, (image, target) in enumerate(
|
metric_logger.log_every(
|
data_loader,
|
10,
|
header,
|
start_iteration=epoch * len(data_loader),
|
ext_logger=ext_logger,
|
ext_logger_prefix="train/batch/",
|
)
|
):
|
image = image.cuda(non_blocking=True)
|
target = target.cuda(non_blocking=True)
|
it = len(data_loader) * epoch + it
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for i, param_group in enumerate(optimizer.param_groups):
|
param_group["lr"] = args.lr_schedule[it]
|
if i == 0:
|
param_group["weight_decay"] = args.wd
|
metric_dict = {
|
"lr": optimizer.param_groups[0]["lr"],
|
"wd": optimizer.param_groups[0]["weight_decay"],
|
"tnorm_ema_momentum": args.tnorm_ema_schedule[it],
|
}
|
with torch.cuda.amp.autocast(fp16_scaler is not None):
|
student_output = model(image)
|
with torch.no_grad():
|
teacher_output = get_teacher_output(
|
image, teachers, teacher_ft_stats, args.tnorm_ema_schedule[it]
|
)
|
loss, _ = unic_loss(
|
student_output,
|
teacher_output,
|
args.lam_lcos,
|
args.lam_lsl1,
|
args.t_drop_prob,
|
metric_dict=metric_dict,
|
)
|
if not math.isfinite(loss.item()):
|
logger.info("Loss is {}, stopping training".format(loss.item()))
|
sys.exit(1)
|
optimizer.zero_grad()
|
grad_norms = None
|
if fp16_scaler is None:
|
loss.backward()
|
if args.clip_grad > 0:
|
grad_norms = utils.clip_gradients(model, args.clip_grad)
|
optimizer.step()
|
else:
|
fp16_scaler.scale(loss).backward()
|
if args.clip_grad > 0:
|
fp16_scaler.unscale_(optimizer)
|
grad_norms = utils.clip_gradients(model, args.clip_grad)
|
fp16_scaler.step(optimizer)
|
fp16_scaler.update()
|
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