""" Train a noised image classifier on ImageNet. """ import argparse import os import blobfile as bf import torch as th import torch.distributed as dist import torch.nn.functional as F from torch.nn.parallel.distributed import DistributedDataParallel as DDP from torch.optim import AdamW from guided_diffusion import dist_util, logger from guided_diffusion.fp16_util import MixedPrecisionTrainer from guided_diffusion.image_datasets import load_data from guided_diffusion.resample import create_named_schedule_sampler from guided_diffusion.script_util import ( add_dict_to_argparser, args_to_dict, classifier_and_diffusion_defaults, create_classifier_and_diffusion, ) from guided_diffusion.train_util import parse_resume_step_from_filename, log_loss_dict def main(): args = create_argparser().parse_args() dist_util.setup_dist() logger.configure() logger.log("creating model and diffusion...") model, diffusion = create_classifier_and_diffusion( **args_to_dict(args, classifier_and_diffusion_defaults().keys()) ) model.to(dist_util.dev()) if args.noised: schedule_sampler = create_named_schedule_sampler( args.schedule_sampler, diffusion ) resume_step = 0 if args.resume_checkpoint: resume_step = parse_resume_step_from_filename(args.resume_checkpoint) if dist.get_rank() == 0: logger.log( f"loading model from checkpoint: {args.resume_checkpoint}... at {resume_step} step" ) model.load_state_dict( dist_util.load_state_dict( args.resume_checkpoint, map_location=dist_util.dev() ) ) # Needed for creating correct EMAs and fp16 parameters. dist_util.sync_params(model.parameters()) mp_trainer = MixedPrecisionTrainer( model=model, use_fp16=args.classifier_use_fp16, initial_lg_loss_scale=16.0 ) model = DDP( model, device_ids=[dist_util.dev()], output_device=dist_util.dev(), broadcast_buffers=False, bucket_cap_mb=128, find_unused_parameters=False, ) logger.log("creating data loader...") data = load_data( data_dir=args.data_dir, batch_size=args.batch_size, image_size=args.image_size, class_cond=True, random_crop=True, ) if args.val_data_dir: val_data = load_data( data_dir=args.val_data_dir, batch_size=args.batch_size, image_size=args.image_size, class_cond=True, ) else: val_data = None logger.log(f"creating optimizer...") opt = AdamW(mp_trainer.master_params, lr=args.lr, weight_decay=args.weight_decay) if args.resume_checkpoint: opt_checkpoint = bf.join( bf.dirname(args.resume_checkpoint), f"opt{resume_step:06}.pt" ) logger.log(f"loading optimizer state from checkpoint: {opt_checkpoint}") opt.load_state_dict( dist_util.load_state_dict(opt_checkpoint, map_location=dist_util.dev()) ) logger.log("training classifier model...") def forward_backward_log(data_loader, prefix="train"): batch, extra = next(data_loader) labels = extra["y"].to(dist_util.dev()) batch = batch.to(dist_util.dev()) # Noisy images if args.noised: t, _ = schedule_sampler.sample(batch.shape[0], dist_util.dev()) batch = diffusion.q_sample(batch, t) else: t = th.zeros(batch.shape[0], dtype=th.long, device=dist_util.dev()) for i, (sub_batch, sub_labels, sub_t) in enumerate( split_microbatches(args.microbatch, batch, labels, t) ): logits = model(sub_batch, timesteps=sub_t) loss = F.cross_entropy(logits, sub_labels, reduction="none") losses = {} losses[f"{prefix}_loss"] = loss.detach() losses[f"{prefix}_acc@1"] = compute_top_k( logits, sub_labels, k=1, reduction="none" ) losses[f"{prefix}_acc@5"] = compute_top_k( logits, sub_labels, k=5, reduction="none" ) log_loss_dict(diffusion, sub_t, losses) del losses loss = loss.mean() if loss.requires_grad: if i == 0: mp_trainer.zero_grad() mp_trainer.backward(loss * len(sub_batch) / len(batch)) for step in range(args.iterations - resume_step): logger.logkv("step", step + resume_step) logger.logkv( "samples", (step + resume_step + 1) * args.batch_size * dist.get_world_size(), ) if args.anneal_lr: set_annealed_lr(opt, args.lr, (step + resume_step) / args.iterations) forward_backward_log(data) mp_trainer.optimize(opt) if val_data is not None and not step % args.eval_interval: with th.no_grad(): with model.no_sync(): model.eval() forward_backward_log(val_data, prefix="val") model.train() if not step % args.log_interval: logger.dumpkvs() if ( step and dist.get_rank() == 0 and not (step + resume_step) % args.save_interval ): logger.log("saving model...") save_model(mp_trainer, opt, step + resume_step) if dist.get_rank() == 0: logger.log("saving model...") save_model(mp_trainer, opt, step + resume_step) dist.barrier() def set_annealed_lr(opt, base_lr, frac_done): lr = base_lr * (1 - frac_done) for param_group in opt.param_groups: param_group["lr"] = lr def save_model(mp_trainer, opt, step): if dist.get_rank() == 0: th.save( mp_trainer.master_params_to_state_dict(mp_trainer.master_params), os.path.join(logger.get_dir(), f"model{step:06d}.pt"), ) th.save(opt.state_dict(), os.path.join(logger.get_dir(), f"opt{step:06d}.pt")) def compute_top_k(logits, labels, k, reduction="mean"): _, top_ks = th.topk(logits, k, dim=-1) if reduction == "mean": return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item() elif reduction == "none": return (top_ks == labels[:, None]).float().sum(dim=-1) def split_microbatches(microbatch, *args): bs = len(args[0]) if microbatch == -1 or microbatch >= bs: yield tuple(args) else: for i in range(0, bs, microbatch): yield tuple(x[i : i + microbatch] if x is not None else None for x in args) def create_argparser(): defaults = dict( data_dir="", val_data_dir="", noised=True, iterations=150000, lr=3e-4, weight_decay=0.0, anneal_lr=False, batch_size=4, microbatch=-1, schedule_sampler="uniform", resume_checkpoint="", log_interval=10, eval_interval=5, save_interval=10000, ) defaults.update(classifier_and_diffusion_defaults()) parser = argparse.ArgumentParser() add_dict_to_argparser(parser, defaults) return parser if __name__ == "__main__": main()