text stringlengths 1 93.6k |
|---|
return args
|
def main(args):
|
utils.init_distributed_mode(args)
|
utils.fix_random_seeds(args.seed + get_global_rank())
|
torch.backends.cuda.matmul.allow_tf32 = True
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cudnn.benchmark = True
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setup_logging(os.path.join(args.output_dir, "log.txt"), level=logging.INFO)
|
utils.print_program_info(args)
|
ext_logger = ExternalLogger(args.output_dir)
|
logger.info("Creating data loaders ...")
|
train_loader, val_loader = get_dataloaders(args)
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logger.info("Loading teachers ...")
|
teachers, teacher_ft_stats = build_teachers(args.teachers)
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logger.info("Creating student model")
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model = build_student_from_args(args)
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model = model.cuda()
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model = nn.parallel.DistributedDataParallel(
|
model, device_ids=[args.gpu], find_unused_parameters=True
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)
|
utils.save_model_defn(model.module, os.path.join(args.output_dir, "model_defn.txt"))
|
optimizer = torch.optim.AdamW(
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utils.get_params_groups(
|
model,
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save_file_path=os.path.join(args.output_dir, "params_groups.txt"),
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),
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lr=0,
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**eval(args.optim_args),
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)
|
logger.info("Optimizer: {}".format(optimizer))
|
fp16_scaler = None
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if args.use_fp16:
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fp16_scaler = torch.cuda.amp.GradScaler()
|
args.lr_schedule = utils.cosine_scheduler(
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args.lr * (args.batch_size_per_gpu * dist.get_world_size()) / 256.0,
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args.min_lr,
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args.epochs,
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len(train_loader),
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warmup_epochs=args.warmup_epochs,
|
)
|
args.tnorm_ema_schedule = utils.cosine_scheduler(
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args.tnorm_ema_momentum_start,
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args.tnorm_ema_momentum_end,
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args.epochs,
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len(train_loader),
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warmup_epochs=0,
|
)
|
to_restore = {"epoch": 0, "teacher_ft_stats": teacher_ft_stats}
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utils.restart_from_checkpoint(
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os.path.join(args.output_dir, "checkpoint.pth"),
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run_variables=to_restore,
|
model=model,
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optimizer=optimizer,
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fp16_scaler=fp16_scaler,
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)
|
start_epoch = to_restore["epoch"]
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teacher_ft_stats = to_restore["teacher_ft_stats"]
|
logger.info("Training starts ...")
|
start_time = time.time()
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for epoch in range(start_epoch, args.epochs):
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train_loader.sampler.set_epoch(epoch)
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train_one_epoch(
|
model,
|
teachers,
|
teacher_ft_stats,
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train_loader,
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optimizer,
|
epoch,
|
fp16_scaler,
|
ext_logger,
|
args,
|
)
|
evaluate(
|
model,
|
teachers,
|
teacher_ft_stats,
|
val_loader,
|
epoch,
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ext_logger,
|
args,
|
)
|
save_dict = {
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"model": model.state_dict(),
|
"teacher_ft_stats": teacher_ft_stats,
|
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