def trn(cfg, model): cfg.logger.info(cfg) if cfg.seed is not None: random.seed(cfg.seed) torch.manual_seed(cfg.seed) torch.cuda.manual_seed(cfg.seed) torch.cuda.manual_seed_all(cfg.seed) train, validate = get_trainer(cfg) if cfg.gpu is not None: cfg.logger.info("Use GPU: {} for training".format(cfg.gpu)) linear_classifier_layer = model.module[1] optimizer = get_optimizer(cfg, linear_classifier_layer) cfg.logger.info(f"=> Getting {cfg.set} dataset") dataset = getattr(data, cfg.set)(cfg) lr_policy = get_policy(cfg.lr_policy)(optimizer, cfg) softmax_criterion = nn.CrossEntropyLoss().cuda() criterion = lambda output, target: softmax_criterion(output, target) # optionally resume from a checkpoint best_val_acc1 = 0.0 best_val_acc5 = 0.0 best_train_acc1 = 0.0 best_train_acc5 = 0.0 if cfg.resume: best_val_acc1 = resume(cfg, model, optimizer) run_base_dir, ckpt_base_dir, log_base_dir = path_utils.get_directories( cfg, cfg.gpu) cfg.ckpt_base_dir = ckpt_base_dir writer = SummaryWriter(log_dir=log_base_dir) epoch_time = AverageMeter("epoch_time", ":.4f", write_avg=False) validation_time = AverageMeter("validation_time", ":.4f", write_avg=False) train_time = AverageMeter("train_time", ":.4f", write_avg=False) progress_overall = ProgressMeter(1, [epoch_time, validation_time, train_time], cfg, prefix="Overall Timing") end_epoch = time.time() cfg.start_epoch = cfg.start_epoch or 0 last_val_acc1 = None start_time = time.time() gpu_info = gpu_utils.GPU_Utils(gpu_index=cfg.gpu) # Start training for epoch in range(cfg.start_epoch, cfg.epochs): cfg.logger.info('Model conv 1 {} at epoch {}'.format( torch.sum(model.module[0].conv1.weight), epoch)) ## make sure backbone is not updated if cfg.world_size > 1: dataset.sampler.set_epoch(epoch) lr_policy(epoch, iteration=None) cur_lr = net_utils.get_lr(optimizer) start_train = time.time() train_acc1, train_acc5 = train(dataset.trn_loader, model, criterion, optimizer, epoch, cfg, writer=writer) train_time.update((time.time() - start_train) / 60) if (epoch + 1) % cfg.test_interval == 0: if cfg.gpu == cfg.base_gpu: # evaluate on validation set start_validation = time.time() last_val_acc1, last_val_acc5 = validate( dataset.val_loader, model.module, criterion, cfg, writer, epoch) validation_time.update((time.time() - start_validation) / 60) # remember best acc@1 and save checkpoint is_best = last_val_acc1 > best_val_acc1 best_val_acc1 = max(last_val_acc1, best_val_acc1) best_val_acc5 = max(last_val_acc5, best_val_acc5) best_train_acc1 = max(train_acc1, best_train_acc1) best_train_acc5 = max(train_acc5, best_train_acc5) save = (((epoch + 1) % cfg.save_every) == 0) and cfg.save_every > 0 if save or epoch == cfg.epochs - 1: if is_best: cfg.logger.info( f"==> best {last_val_acc1:.02f} saving at {ckpt_base_dir / 'model_best.pth'}" ) net_utils.save_checkpoint( { "epoch": epoch + 1, "arch": cfg.arch, "state_dict": model.state_dict(), "best_acc1": best_val_acc1, "best_acc5": best_val_acc5, "best_train_acc1": best_train_acc1, "best_train_acc5": best_train_acc5, "optimizer": optimizer.state_dict(), "curr_acc1": last_val_acc1, "curr_acc5": last_val_acc5, }, is_best, filename=ckpt_base_dir / f"epoch_{epoch}.state", save=save or epoch == cfg.epochs - 1, ) elapsed_time = time.time() - start_time seconds_todo = (cfg.epochs - epoch) * (elapsed_time / cfg.test_interval) estimated_time_complete = timedelta(seconds=int(seconds_todo)) start_time = time.time() cfg.logger.info( f"==> ETA: {estimated_time_complete}\tGPU-M: {gpu_info.gpu_mem_usage()}\tGPU-U: {gpu_info.gpu_utilization()}" ) epoch_time.update((time.time() - end_epoch) / 60) progress_overall.display(epoch) progress_overall.write_to_tensorboard(writer, prefix="diagnostics", global_step=epoch) writer.add_scalar("test/lr", cur_lr, epoch) end_epoch = time.time() if cfg.world_size > 1: dist.barrier()
def main_worker(args): train, validate, modifier = get_trainer(args) if args.gpu is not None: print("Use GPU: {} for training".format(args.gpu)) # create model and optimizer model = get_model(args) model = set_gpu(args, model) wandb.watch(model) if args.pretrained: pretrained(args, model) optimizer = get_optimizer(args, model) data = get_dataset(args) lr_policy = get_policy(args.lr_policy)(optimizer, args) if args.label_smoothing is None: criterion = nn.CrossEntropyLoss().cuda() else: criterion = LabelSmoothing(smoothing=args.label_smoothing) # optionally resume from a checkpoint best_acc1 = 0.0 best_acc5 = 0.0 best_train_acc1 = 0.0 best_train_acc5 = 0.0 if args.resume: best_acc1 = resume(args, model, optimizer) # Data loading code if args.evaluate: acc1, acc5 = validate(data.val_loader, model, criterion, args, writer=None, epoch=args.start_epoch) return # Set up directories run_base_dir, ckpt_base_dir, log_base_dir = get_directories(args) args.ckpt_base_dir = ckpt_base_dir writer = SummaryWriter(log_dir=log_base_dir) epoch_time = AverageMeter("epoch_time", ":.4f", write_avg=False) validation_time = AverageMeter("validation_time", ":.4f", write_avg=False) train_time = AverageMeter("train_time", ":.4f", write_avg=False) progress_overall = ProgressMeter(1, [epoch_time, validation_time, train_time], prefix="Overall Timing") end_epoch = time.time() args.start_epoch = args.start_epoch or 0 acc1 = None # Save the initial state save_checkpoint( { "epoch": 0, "arch": args.arch, "state_dict": model.state_dict(), "best_acc1": best_acc1, "best_acc5": best_acc5, "best_train_acc1": best_train_acc1, "best_train_acc5": best_train_acc5, "optimizer": optimizer.state_dict(), "curr_acc1": acc1 if acc1 else "Not evaluated", }, False, filename=ckpt_base_dir / f"initial.state", save=False, ) # Start training for epoch in range(args.start_epoch, args.epochs): lr_policy(epoch, iteration=None) modifier(args, epoch, model) cur_lr = get_lr(optimizer) # train for one epoch start_train = time.time() train_acc1, train_acc5 = train(data.train_loader, model, criterion, optimizer, epoch, args, writer=writer) train_time.update((time.time() - start_train) / 60) # evaluate on validation set start_validation = time.time() acc1, acc5 = validate(data.val_loader, model, criterion, args, writer, epoch) validation_time.update((time.time() - start_validation) / 60) # remember best acc@1 and save checkpoint is_best = acc1 > best_acc1 best_acc1 = max(acc1, best_acc1) best_acc5 = max(acc5, best_acc5) best_train_acc1 = max(train_acc1, best_train_acc1) best_train_acc5 = max(train_acc5, best_train_acc5) save = ((epoch % args.save_every) == 0) and args.save_every > 0 if is_best or save or epoch == args.epochs - 1: if is_best: print( f"==> New best, saving at {ckpt_base_dir / 'model_best.pth'}" ) save_checkpoint( { "epoch": epoch + 1, "arch": args.arch, "state_dict": model.state_dict(), "best_acc1": best_acc1, "best_acc5": best_acc5, "best_train_acc1": best_train_acc1, "best_train_acc5": best_train_acc5, "optimizer": optimizer.state_dict(), "curr_acc1": acc1, "curr_acc5": acc5, }, is_best, filename=ckpt_base_dir / f"epoch_{epoch}.state", save=save, ) wandb.log({ "curr_acc1": acc1, "curr_acc5": acc5, }) epoch_time.update((time.time() - end_epoch) / 60) progress_overall.display(epoch) progress_overall.write_to_tensorboard(writer, prefix="diagnostics", global_step=epoch) if args.conv_type == "SampleSubnetConv": count = 0 sum_pr = 0.0 for n, m in model.named_modules(): if isinstance(m, SampleSubnetConv): # avg pr across 10 samples pr = 0.0 for _ in range(10): pr += ((torch.rand_like(m.clamped_scores) >= m.clamped_scores).float().mean().item()) pr /= 10.0 writer.add_scalar("pr/{}".format(n), pr, epoch) sum_pr += pr count += 1 args.prune_rate = sum_pr / count writer.add_scalar("pr/average", args.prune_rate, epoch) writer.add_scalar("test/lr", cur_lr, epoch) end_epoch = time.time() write_result_to_csv( best_acc1=best_acc1, best_acc5=best_acc5, best_train_acc1=best_train_acc1, best_train_acc5=best_train_acc5, prune_rate=args.prune_rate, curr_acc1=acc1, curr_acc5=acc5, base_config=args.config, name=args.name, )
def trn(cfg,model): cfg.logger.info(cfg) if cfg.seed is not None: random.seed(cfg.seed) torch.manual_seed(cfg.seed) torch.cuda.manual_seed(cfg.seed) torch.cuda.manual_seed_all(cfg.seed) train, validate_knn = get_trainer(cfg) if cfg.gpu is not None: cfg.logger.info("Use GPU: {} for training".format(cfg.gpu)) # if cfg.pretrained: # net_utils.load_pretrained(cfg.pretrained,cfg.multigpu[0], model) optimizer = get_optimizer(cfg, model) cfg.logger.info(f"=> Getting {cfg.set} dataset") dataset = getattr(data, cfg.set)(cfg) lr_policy = get_policy(cfg.lr_policy)(optimizer, cfg) if cfg.arch == 'SimSiam': # L = D(p1, z2) / 2 + D(p2, z1) / 2 base_criterion = lambda bb1_z1_p1_emb, bb2_z2_p2_emb: simsiam.SimSaimLoss(bb1_z1_p1_emb[2], bb2_z2_p2_emb[1]) / 2 +\ simsiam.SimSaimLoss(bb2_z2_p2_emb[2], bb1_z1_p1_emb[1]) / 2 elif cfg.arch == 'SimCLR': base_criterion = lambda z1,z2 : simclr.NT_XentLoss(z1, z2) else: raise NotImplemented run_base_dir, ckpt_base_dir, log_base_dir = path_utils.get_directories(cfg,cfg.gpu) _, zero_gpu_ckpt_base_dir, _ = path_utils.get_directories(cfg, 0) # if cfg.resume: saved_epochs = sorted(glob.glob(str(zero_gpu_ckpt_base_dir) + '/epoch_*.state'), key=os.path.getmtime) # assert len(epochs) < 2, 'Should be only one saved epoch -- the last one' if len(saved_epochs) > 0: cfg.resume = saved_epochs[-1] resume(cfg, model, optimizer) cfg.ckpt_base_dir = ckpt_base_dir writer = SummaryWriter(log_dir=log_base_dir) epoch_time = AverageMeter("epoch_time", ":.4f", write_avg=False) validation_time = AverageMeter("validation_time", ":.4f", write_avg=False) train_time = AverageMeter("train_time", ":.4f", write_avg=False) progress_overall = ProgressMeter( 1, [epoch_time, validation_time, train_time], cfg, prefix="Overall Timing" ) end_epoch = time.time() cfg.start_epoch = cfg.start_epoch or 0 start_time = time.time() gpu_info = gpu_utils.GPU_Utils(gpu_index=cfg.gpu) cfg.logger.info('Start Training: Model conv 1 initialization {}'.format(torch.sum(model.module.backbone.conv1.weight))) # Start training for n,m in model.module.named_modules(): if hasattr(m, "weight") and m.weight is not None: cfg.logger.info('{} ({}): {}'.format(n,type(m).__name__,m.weight.shape)) criterion = base_criterion cfg.logger.info('Using Vanilla Criterion') for epoch in range(cfg.start_epoch, cfg.epochs): if cfg.world_size > 1: dataset.sampler.set_epoch(epoch) lr_policy(epoch, iteration=None) cur_lr = net_utils.get_lr(optimizer) start_train = time.time() train(dataset.trn_loader, model,criterion, optimizer, epoch, cfg, writer=writer) train_time.update((time.time() - start_train) / 60) if (epoch + 1) % cfg.test_interval == 0: if cfg.gpu == cfg.base_gpu: # evaluate on validation set start_validation = time.time() acc = validate_knn(dataset.trn_loader, dataset.val_loader, model.module, cfg, writer, epoch) validation_time.update((time.time() - start_validation) / 60) csv_utils.write_generic_result_to_csv(path=cfg.exp_dir,name=os.path.basename(cfg.exp_dir[:-1]), epoch=epoch, knn_acc=acc) save = (((epoch+1) % cfg.save_every) == 0) and cfg.save_every > 0 if save or epoch == cfg.epochs - 1: # if is_best: # print(f"==> best {last_val_acc1:.02f} saving at {ckpt_base_dir / 'model_best.pth'}") net_utils.save_checkpoint( { "epoch": epoch + 1, "arch": cfg.arch, "state_dict": model.state_dict(), "ACC": acc, "optimizer": optimizer.state_dict(), }, is_best=False, filename=ckpt_base_dir / f"epoch_{epoch:04d}.state", save=save or epoch == cfg.epochs - 1, ) elapsed_time = time.time() - start_time seconds_todo = (cfg.epochs - epoch) * (elapsed_time / cfg.test_interval) estimated_time_complete = timedelta(seconds=int(seconds_todo)) start_time = time.time() cfg.logger.info( f"==> ETA: {estimated_time_complete}\tGPU-M: {gpu_info.gpu_mem_usage()}\tGPU-U: {gpu_info.gpu_utilization()}") epoch_time.update((time.time() - end_epoch) / 60) progress_overall.display(epoch) progress_overall.write_to_tensorboard( writer, prefix="diagnostics", global_step=epoch ) writer.add_scalar("test/lr", cur_lr, epoch) end_epoch = time.time() if cfg.world_size > 1: # cfg.logger.info('GPU {} going into the barrier'.format(cfg.gpu)) dist.barrier()
def main_worker(args): # NEW: equivalent to MPI init. print("world size ", os.environ['OMPI_COMM_WORLD_SIZE']) print("rank ", os.environ['OMPI_COMM_WORLD_RANK']) torch.distributed.init_process_group( backend="nccl", init_method="env://", world_size=int(os.environ['OMPI_COMM_WORLD_SIZE']), rank=int(os.environ['OMPI_COMM_WORLD_RANK'])) # NEW: lookup number of ranks in the job, and our rank args.world_size = torch.distributed.get_world_size() print("world size ", args.world_size) args.rank = torch.distributed.get_rank() print("rank ", args.rank) ngpus_per_node = torch.cuda.device_count() print("ngpus_per_node ", ngpus_per_node) local_rank = args.rank % ngpus_per_node print("local_rank ", local_rank) # NEW: Globalize variables global best_acc1 global best_acc5 global best_train_acc1 global best_train_acc5 #args.gpu = None # NEW: Specify gpu args.gpu = local_rank train, validate, modifier = get_trainer(args) if args.gpu is not None: print("Use GPU: {} for training".format(args.gpu)) # create model and optimizer model = get_model(args) # NEW: Distributed data #if args.distributed: args.batch_size = int(args.batch_size / ngpus_per_node) args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node) #model = set_gpu(args, model) # NEW: Modified function for loading gpus on multinode setups model = lassen_set_gpu(args, model) if args.pretrained: pretrained(args, model) optimizer = get_optimizer(args, model) data = get_dataset(args) lr_policy = get_policy(args.lr_policy)(optimizer, args) if args.label_smoothing is None: #criterion = nn.CrossEntropyLoss().cuda() # NEW: Specify gpu criterion = nn.CrossEntropyLoss().cuda(args.gpu) else: criterion = LabelSmoothing(smoothing=args.label_smoothing) # optionally resume from a checkpoint best_acc1 = 0.0 best_acc5 = 0.0 best_train_acc1 = 0.0 best_train_acc5 = 0.0 if args.resume: best_acc1 = resume(args, model, optimizer) # Data loading code if args.evaluate: acc1, acc5 = validate(data.val_loader, model, criterion, args, writer=None, epoch=args.start_epoch) return # Set up directories # NEW: Only do for main processor (one with global rank 0) if args.rank == 0: run_base_dir, ckpt_base_dir, log_base_dir = get_directories(args) args.ckpt_base_dir = ckpt_base_dir # NEW: Only do for main processor (one with global rank 0) if args.rank == 0: writer = SummaryWriter(log_dir=log_base_dir) else: writer = None epoch_time = AverageMeter("epoch_time", ":.4f", write_avg=False) validation_time = AverageMeter("validation_time", ":.4f", write_avg=False) train_time = AverageMeter("train_time", ":.4f", write_avg=False) # NEW: Only do for main processor (one with global rank 0) if args.rank == 0: progress_overall = ProgressMeter( 1, [epoch_time, validation_time, train_time], prefix="Overall Timing") end_epoch = time.time() args.start_epoch = args.start_epoch or 0 acc1 = None # Save the initial state # NEW: Only do for main processor (one with global rank 0) if args.rank == 0: save_checkpoint( { "epoch": 0, "arch": args.arch, "state_dict": model.state_dict(), "best_acc1": best_acc1, "best_acc5": best_acc5, "best_train_acc1": best_train_acc1, "best_train_acc5": best_train_acc5, "optimizer": optimizer.state_dict(), "curr_acc1": acc1 if acc1 else "Not evaluated", }, False, filename=ckpt_base_dir / f"initial.state", save=False, ) # Start training for epoch in range(args.start_epoch, args.epochs): # NEW: Distributed data #if args.distributed: data.train_sampler.set_epoch(epoch) data.val_sampler.set_epoch(epoch) lr_policy(epoch, iteration=None) #modifier(args, epoch, model) cur_lr = get_lr(optimizer) # train for one epoch start_train = time.time() train_acc1, train_acc5 = train(data.train_loader, model, criterion, optimizer, epoch, args, writer=writer) #train_acc1, train_acc5 = train( # data.train_loader, model, criterion, optimizer, epoch, args, writer=None #) train_time.update((time.time() - start_train) / 60) # evaluate on validation set start_validation = time.time() # NEW: Only write values to tensorboard for main processor (one with global rank 0) if args.rank == 0: acc1, acc5 = validate(data.val_loader, model, criterion, args, writer, epoch) else: acc1, acc5 = validate(data.val_loader, model, criterion, args, None, epoch) validation_time.update((time.time() - start_validation) / 60) # remember best acc@1 and save checkpoint is_best = acc1 > best_acc1 best_acc1 = max(acc1, best_acc1) best_acc5 = max(acc5, best_acc5) best_train_acc1 = max(train_acc1, best_train_acc1) best_train_acc5 = max(train_acc5, best_train_acc5) save = ((epoch % args.save_every) == 0) and args.save_every > 0 # NEW: Only do for main processor (one with global rank 0) if args.rank == 0: if is_best or save or epoch == args.epochs - 1: if is_best: print( f"==> New best, saving at {ckpt_base_dir / 'model_best.pth'}" ) save_checkpoint( { "epoch": epoch + 1, "arch": args.arch, "state_dict": model.state_dict(), "best_acc1": best_acc1, "best_acc5": best_acc5, "best_train_acc1": best_train_acc1, "best_train_acc5": best_train_acc5, "optimizer": optimizer.state_dict(), "curr_acc1": acc1, "curr_acc5": acc5, }, is_best, filename=ckpt_base_dir / f"epoch_most_recent.state", save=save, ) #filename=ckpt_base_dir / f"epoch_{epoch}.state", epoch_time.update((time.time() - end_epoch) / 60) # NEW: Only do for main processor (one with global rank 0) if args.rank == 0: progress_overall.display(epoch) progress_overall.write_to_tensorboard(writer, prefix="diagnostics", global_step=epoch) if args.conv_type == "SampleSubnetConv": count = 0 sum_pr = 0.0 for n, m in model.named_modules(): if isinstance(m, SampleSubnetConv): # avg pr across 10 samples pr = 0.0 for _ in range(10): pr += ((torch.rand_like(m.clamped_scores) >= m.clamped_scores).float().mean().item()) pr /= 10.0 writer.add_scalar("pr/{}".format(n), pr, epoch) sum_pr += pr count += 1 args.prune_rate = sum_pr / count writer.add_scalar("pr/average", args.prune_rate, epoch) # NEW: Only do for main processor (one with global rank 0) if args.rank == 0: writer.add_scalar("test/lr", cur_lr, epoch) end_epoch = time.time() # NEW: Only do for main processor (one with global rank 0) if args.rank == 0: write_result_to_csv( best_acc1=best_acc1, best_acc5=best_acc5, best_train_acc1=best_train_acc1, best_train_acc5=best_train_acc5, prune_rate=args.prune_rate, curr_acc1=acc1, curr_acc5=acc5, base_config=args.config, name=args.name, )
def main_worker(args): args.gpu = None if args.gpu is not None: print("Use GPU: {} for training".format(args.gpu)) # create model and optimizer model = get_model(args) model = set_gpu(args, model) # Set up directories run_base_dir, ckpt_base_dir, log_base_dir = get_directories(args) # Loading pretrained model if args.pretrained: pretrained(args, model) # Saving a DenseConv (nn.Conv2d) compatible model if args.dense_conv_model: print( f"==> DenseConv compatible model, saving at {ckpt_base_dir / 'model_best.pth'}" ) save_checkpoint( { "epoch": 0, "arch": args.arch, "state_dict": model.state_dict(), }, True, filename=ckpt_base_dir / f"epoch_pretrained.state", save=True, ) return optimizer = get_optimizer(args, model) data = get_dataset(args) lr_policy = get_policy(args.lr_policy)(optimizer, args) if args.label_smoothing is None: criterion = nn.CrossEntropyLoss().cuda() else: criterion = LabelSmoothing(smoothing=args.label_smoothing) # optionally resume from a checkpoint best_acc1 = 0.0 best_acc5 = 0.0 best_train_acc1 = 0.0 best_train_acc5 = 0.0 if args.resume: best_acc1 = resume(args, model, optimizer) # Evaulation of a model if args.evaluate: acc1, acc5 = validate(data.val_loader, model, criterion, args, writer=None, epoch=args.start_epoch) return writer = SummaryWriter(log_dir=log_base_dir) epoch_time = AverageMeter("epoch_time", ":.4f", write_avg=False) validation_time = AverageMeter("validation_time", ":.4f", write_avg=False) train_time = AverageMeter("train_time", ":.4f", write_avg=False) progress_overall = ProgressMeter(1, [epoch_time, validation_time, train_time], prefix="Overall Timing") end_epoch = time.time() args.start_epoch = args.start_epoch or 0 acc1 = None # Save the initial state save_checkpoint( { "epoch": 0, "arch": args.arch, "state_dict": model.state_dict(), "best_acc1": best_acc1, "best_acc5": best_acc5, "best_train_acc1": best_train_acc1, "best_train_acc5": best_train_acc5, "optimizer": optimizer.state_dict(), "curr_acc1": acc1 if acc1 else "Not evaluated", }, False, filename=ckpt_base_dir / f"initial.state", save=False, ) # Start training for epoch in range(args.start_epoch, args.epochs): lr_policy(epoch, iteration=None) cur_lr = get_lr(optimizer) # Gradual pruning in GMP experiments if args.conv_type == "GMPConv" and epoch >= args.init_prune_epoch and epoch <= args.final_prune_epoch: total_prune_epochs = args.final_prune_epoch - args.init_prune_epoch + 1 for n, m in model.named_modules(): if hasattr(m, 'set_curr_prune_rate'): prune_decay = ( 1 - ((args.curr_prune_epoch - args.init_prune_epoch) / total_prune_epochs))**3 curr_prune_rate = m.prune_rate - (m.prune_rate * prune_decay) m.set_curr_prune_rate(curr_prune_rate) # train for one epoch start_train = time.time() train_acc1, train_acc5 = train(data.train_loader, model, criterion, optimizer, epoch, args, writer=writer) train_time.update((time.time() - start_train) / 60) # evaluate on validation set start_validation = time.time() acc1, acc5 = validate(data.val_loader, model, criterion, args, writer, epoch) validation_time.update((time.time() - start_validation) / 60) # remember best acc@1 and save checkpoint is_best = acc1 > best_acc1 best_acc1 = max(acc1, best_acc1) best_acc5 = max(acc5, best_acc5) best_train_acc1 = max(train_acc1, best_train_acc1) best_train_acc5 = max(train_acc5, best_train_acc5) save = ((epoch % args.save_every) == 0) and args.save_every > 0 if is_best or save or epoch == args.epochs - 1: if is_best: print( f"==> New best, saving at {ckpt_base_dir / 'model_best.pth'}" ) save_checkpoint( { "epoch": epoch + 1, "arch": args.arch, "state_dict": model.state_dict(), "best_acc1": best_acc1, "best_acc5": best_acc5, "best_train_acc1": best_train_acc1, "best_train_acc5": best_train_acc5, "optimizer": optimizer.state_dict(), "curr_acc1": acc1, "curr_acc5": acc5, }, is_best, filename=ckpt_base_dir / f"epoch_{epoch}.state", save=save, ) epoch_time.update((time.time() - end_epoch) / 60) progress_overall.display(epoch) progress_overall.write_to_tensorboard(writer, prefix="diagnostics", global_step=epoch) writer.add_scalar("test/lr", cur_lr, epoch) end_epoch = time.time() # Storing sparsity and threshold statistics for STRConv models if args.conv_type == "STRConv": count = 0 sum_sparse = 0.0 for n, m in model.named_modules(): if isinstance(m, STRConv): sparsity, total_params, thresh = m.getSparsity() writer.add_scalar("sparsity/{}".format(n), sparsity, epoch) writer.add_scalar("thresh/{}".format(n), thresh, epoch) sum_sparse += int(((100 - sparsity) / 100) * total_params) count += total_params total_sparsity = 100 - (100 * sum_sparse / count) writer.add_scalar("sparsity/total", total_sparsity, epoch) writer.add_scalar("test/lr", cur_lr, epoch) end_epoch = time.time() write_result_to_csv( best_acc1=best_acc1, best_acc5=best_acc5, best_train_acc1=best_train_acc1, best_train_acc5=best_train_acc5, prune_rate=args.prune_rate, curr_acc1=acc1, curr_acc5=acc5, base_config=args.config, name=args.name, ) if args.conv_type == "STRConv": json_data = {} json_thres = {} for n, m in model.named_modules(): if isinstance(m, STRConv): sparsity = m.getSparsity() json_data[n] = sparsity[0] sum_sparse += int(((100 - sparsity[0]) / 100) * sparsity[1]) count += sparsity[1] json_thres[n] = sparsity[2] json_data["total"] = 100 - (100 * sum_sparse / count) if not os.path.exists("runs/layerwise_sparsity"): os.mkdir("runs/layerwise_sparsity") if not os.path.exists("runs/layerwise_threshold"): os.mkdir("runs/layerwise_threshold") with open("runs/layerwise_sparsity/{}.json".format(args.name), "w") as f: json.dump(json_data, f) with open("runs/layerwise_threshold/{}.json".format(args.name), "w") as f: json.dump(json_thres, f)
def main_worker(args): args.gpu = None train, validate, modifier = get_trainer(args) if args.gpu is not None: print("Use GPU: {} for training".format(args.gpu)) # create model and optimizer model = get_model(args) model = set_gpu(args, model) if args.pretrained: pretrained(args, model) # SJT modification: if args.exp_mode: # pretraining/pruning/funetuning exp_mode = args.exp_mode if exp_mode == "pretraining": # YHT modefication, setting the pruning rate to 0 print( "Figure out your exp_mode is pretraining, setting prune-rate to 0" ) args.prune_rate = 0 unfreeze_model_weights(model) freeze_model_subnet(model) # End of SJT modification optimizer = get_optimizer(args, model) data = get_dataset(args) lr_policy = get_policy(args.lr_policy)(optimizer, args) if args.label_smoothing is None: criterion = nn.CrossEntropyLoss().cuda() else: criterion = LabelSmoothing(smoothing=args.label_smoothing) # optionally resume from a checkpoint best_acc1 = 0.0 best_acc5 = 0.0 best_train_acc1 = 0.0 best_train_acc5 = 0.0 if args.resume: # SJT modification if args.exp_mode: if args.exp_mode == "pruning": optimizer = resume_pruning(args, model) else: # Only can be "finetuning" if args.exp_mode != "finetuning": print( "resume method should be combined with pruning/finetuning exp_mode together!" ) return else: optimizer = resume_finetuning(args, model) # YHT: not sure whether it is needed #lr_policy = get_policy(args.lr_policy)(optimizer, args) #print("#####################DEBUG PRINT : VALIDATE FIRST#####################") #validate(data.val_loader, model, criterion, args, writer= None, epoch=args.start_epoch) else: best_acc1 = resume(args, model, optimizer) # End of SJT modification else: # YHT modification if args.exp_mode: if args.exp_mode == "finetuning": #here, we suppose the user want to use init prun-rate vector to do the finetuning(subnetwork) print( "Using finetuning mode without resume, which is supposed to be innit fientune." ) optimizer = resume_finetuning(args, model) # YHT: not sure whether it is needed lr_policy = get_policy(args.lr_policy)(optimizer, args) # End of modification # Data loading code if args.evaluate: acc1, acc5 = validate(data.val_loader, model, criterion, args, writer=None, epoch=args.start_epoch) return # Set up directories run_base_dir, ckpt_base_dir, log_base_dir = get_directories(args) args.ckpt_base_dir = ckpt_base_dir writer = SummaryWriter(log_dir=log_base_dir) epoch_time = AverageMeter("epoch_time", ":.4f", write_avg=False) validation_time = AverageMeter("validation_time", ":.4f", write_avg=False) train_time = AverageMeter("train_time", ":.4f", write_avg=False) progress_overall = ProgressMeter(1, [epoch_time, validation_time, train_time], prefix="Overall Timing") end_epoch = time.time() args.start_epoch = args.start_epoch or 0 acc1 = None # Save the initial state save_checkpoint( { "epoch": 0, "arch": args.arch, "state_dict": model.state_dict(), "best_acc1": best_acc1, "best_acc5": best_acc5, "best_train_acc1": best_train_acc1, "best_train_acc5": best_train_acc5, "optimizer": optimizer.state_dict(), "curr_acc1": acc1 if acc1 else "Not evaluated", }, False, filename=ckpt_base_dir / f"initial.state", save=False, ) if args.gp_warm_up: record_prune_rate = args.prune_rate if args.print_more: print_global_layerwise_prune_rate(model, args.prune_rate) # YHT modification May 20 # till here, we have every prune-rate is accurate # Now we need to create mask if prandom is true using if args.prandom: make_prandom_mask(model) # End of modification # Start training for epoch in range(args.start_epoch, args.epochs): lr_policy(epoch, iteration=None) modifier(args, epoch, model) cur_lr = get_lr(optimizer) if args.print_more: print("In epoch{epoch}, lr = {cur_lr}") # train for one epoch start_train = time.time() # WHN modeification add global pruning if args.pscale == "global": if args.gp_warm_up: if epoch < args.gp_warm_up_epochs: args.prune_rate = 0 else: args.prune_rate = record_prune_rate if not args.prandom: args.score_threshold = get_global_score_threshold( model, args.prune_rate) # YHT modification if args.print_more: print_global_layerwise_prune_rate(model, args.prune_rate) # End of modification train_acc1, train_acc5 = train(data.train_loader, model, criterion, optimizer, epoch, args, writer=writer) train_time.update((time.time() - start_train) / 60) # evaluate on validation set start_validation = time.time() # if random labeled, evaluate on training set (by yty) if args.shuffle: acc1, acc5 = train_acc1, train_acc5 else: acc1, acc5 = validate(data.val_loader, model, criterion, args, writer, epoch) validation_time.update((time.time() - start_validation) / 60) # remember best acc@1 and save checkpoint is_best = acc1 > best_acc1 best_acc1 = max(acc1, best_acc1) best_acc5 = max(acc5, best_acc5) best_train_acc1 = max(train_acc1, best_train_acc1) best_train_acc5 = max(train_acc5, best_train_acc5) save = ((epoch % args.save_every) == 0) and args.save_every > 0 if is_best or save or epoch == args.epochs - 1: if is_best: print( f"==> New best, saving at {ckpt_base_dir / 'model_best.pth'}" ) save_checkpoint( { "epoch": epoch + 1, "arch": args.arch, "state_dict": model.state_dict(), "best_acc1": best_acc1, "best_acc5": best_acc5, "best_train_acc1": best_train_acc1, "best_train_acc5": best_train_acc5, "optimizer": optimizer.state_dict(), "curr_acc1": acc1, "curr_acc5": acc5, }, is_best, filename=ckpt_base_dir / f"epoch_{epoch}.state", save=save, ) epoch_time.update((time.time() - end_epoch) / 60) progress_overall.display(epoch) progress_overall.write_to_tensorboard(writer, prefix="diagnostics", global_step=epoch) if args.conv_type == "SampleSubnetConv": count = 0 sum_pr = 0.0 for n, m in model.named_modules(): if isinstance(m, SampleSubnetConv): # avg pr across 10 samples pr = 0.0 for _ in range(10): pr += ((torch.rand_like(m.clamped_scores) >= m.clamped_scores).float().mean().item()) pr /= 10.0 writer.add_scalar("pr/{}".format(n), pr, epoch) sum_pr += pr count += 1 args.prune_rate = sum_pr / count writer.add_scalar("pr/average", args.prune_rate, epoch) writer.add_scalar("test/lr", cur_lr, epoch) end_epoch = time.time() write_result_to_csv( best_acc1=best_acc1, best_acc5=best_acc5, best_train_acc1=best_train_acc1, best_train_acc5=best_train_acc5, prune_rate=args.prune_rate, curr_acc1=acc1, curr_acc5=acc5, base_config=args.config, name=args.name, )