def test_api(api, sss_or_tss=True): print("{:} start testing the api : {:}".format(time_string(), api)) api.clear_params(12) api.reload(index=12) # Query the informations of 1113-th architecture info_strs = api.query_info_str_by_arch(1113) print(info_strs) info = api.query_by_index(113) print("{:}\n".format(info)) info = api.query_by_index(113, "cifar100") print("{:}\n".format(info)) info = api.query_meta_info_by_index(115, "90" if sss_or_tss else "200") print("{:}\n".format(info)) for dataset in ["cifar10", "cifar100", "ImageNet16-120"]: for xset in ["train", "test", "valid"]: best_index, highest_accuracy = api.find_best(dataset, xset) print("") params = api.get_net_param(12, "cifar10", None) # Obtain the config and create the network config = api.get_net_config(12, "cifar10") print("{:}\n".format(config)) network = get_cell_based_tiny_net(config) network.load_state_dict(next(iter(params.values()))) # Obtain the cost information info = api.get_cost_info(12, "cifar10") print("{:}\n".format(info)) info = api.get_latency(12, "cifar10") print("{:}\n".format(info)) for index in [13, 15, 19, 200]: info = api.get_latency(index, "cifar10") # Count the number of architectures info = api.statistics("cifar100", "12") print("{:} statistics results : {:}\n".format(time_string(), info)) # Show the information of the 123-th architecture api.show(123) # Obtain both cost and performance information info = api.get_more_info(1234, "cifar10") print("{:}\n".format(info)) print("{:} finish testing the api : {:}".format(time_string(), api)) if not sss_or_tss: arch_str = "|nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1|+|skip_connect~0|nor_conv_3x3~1|skip_connect~2|" matrix = api.str2matrix(arch_str) print("Compute the adjacency matrix of {:}".format(arch_str)) print(matrix) info = api.simulate_train_eval(123, "cifar10") print("simulate_train_eval : {:}\n\n".format(info))
def main(xargs): assert torch.cuda.is_available(), "CUDA is not available." torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.set_num_threads(xargs.workers) prepare_seed(xargs.rand_seed) logger = prepare_logger(args) train_data, valid_data, xshape, class_num = get_datasets( xargs.dataset, xargs.data_path, -1 ) # config_path = 'configs/nas-benchmark/algos/DARTS.config' config = load_config( xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger ) search_loader, _, valid_loader = get_nas_search_loaders( train_data, valid_data, xargs.dataset, "configs/nas-benchmark/", config.batch_size, xargs.workers, ) logger.log( "||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format( xargs.dataset, len(search_loader), len(valid_loader), config.batch_size ) ) logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config)) search_space = get_search_spaces("cell", xargs.search_space_name) if xargs.model_config is None: model_config = dict2config( { "name": "DARTS-V1", "C": xargs.channel, "N": xargs.num_cells, "max_nodes": xargs.max_nodes, "num_classes": class_num, "space": search_space, "affine": False, "track_running_stats": bool(xargs.track_running_stats), }, None, ) else: model_config = load_config( xargs.model_config, { "num_classes": class_num, "space": search_space, "affine": False, "track_running_stats": bool(xargs.track_running_stats), }, None, ) search_model = get_cell_based_tiny_net(model_config) logger.log("search-model :\n{:}".format(search_model)) w_optimizer, w_scheduler, criterion = get_optim_scheduler( search_model.get_weights(), config ) a_optimizer = torch.optim.Adam( search_model.get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay, ) logger.log("w-optimizer : {:}".format(w_optimizer)) logger.log("a-optimizer : {:}".format(a_optimizer)) logger.log("w-scheduler : {:}".format(w_scheduler)) logger.log("criterion : {:}".format(criterion)) flop, param = get_model_infos(search_model, xshape) # logger.log('{:}'.format(search_model)) logger.log("FLOP = {:.2f} M, Params = {:.2f} MB".format(flop, param)) if xargs.arch_nas_dataset is None: api = None else: api = API(xargs.arch_nas_dataset) logger.log("{:} create API = {:} done".format(time_string(), api)) last_info, model_base_path, model_best_path = ( logger.path("info"), logger.path("model"), logger.path("best"), ) network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda() if last_info.exists(): # automatically resume from previous checkpoint logger.log( "=> loading checkpoint of the last-info '{:}' start".format(last_info) ) last_info = torch.load(last_info) start_epoch = last_info["epoch"] checkpoint = torch.load(last_info["last_checkpoint"]) genotypes = checkpoint["genotypes"] valid_accuracies = checkpoint["valid_accuracies"] search_model.load_state_dict(checkpoint["search_model"]) w_scheduler.load_state_dict(checkpoint["w_scheduler"]) w_optimizer.load_state_dict(checkpoint["w_optimizer"]) a_optimizer.load_state_dict(checkpoint["a_optimizer"]) logger.log( "=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format( last_info, start_epoch ) ) else: logger.log("=> do not find the last-info file : {:}".format(last_info)) start_epoch, valid_accuracies, genotypes = ( 0, {"best": -1}, {-1: search_model.genotype()}, ) # start training start_time, search_time, epoch_time, total_epoch = ( time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup, ) for epoch in range(start_epoch, total_epoch): w_scheduler.update(epoch, 0.0) need_time = "Time Left: {:}".format( convert_secs2time(epoch_time.val * (total_epoch - epoch), True) ) epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch) logger.log( "\n[Search the {:}-th epoch] {:}, LR={:}".format( epoch_str, need_time, min(w_scheduler.get_lr()) ) ) search_w_loss, search_w_top1, search_w_top5 = search_func( search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger, xargs.gradient_clip, ) search_time.update(time.time() - start_time) logger.log( "[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s".format( epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum ) ) valid_a_loss, valid_a_top1, valid_a_top5 = valid_func( valid_loader, network, criterion ) logger.log( "[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format( epoch_str, valid_a_loss, valid_a_top1, valid_a_top5 ) ) # check the best accuracy valid_accuracies[epoch] = valid_a_top1 if valid_a_top1 > valid_accuracies["best"]: valid_accuracies["best"] = valid_a_top1 genotypes["best"] = search_model.genotype() find_best = True else: find_best = False genotypes[epoch] = search_model.genotype() logger.log( "<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch]) ) # save checkpoint save_path = save_checkpoint( { "epoch": epoch + 1, "args": deepcopy(xargs), "search_model": search_model.state_dict(), "w_optimizer": w_optimizer.state_dict(), "a_optimizer": a_optimizer.state_dict(), "w_scheduler": w_scheduler.state_dict(), "genotypes": genotypes, "valid_accuracies": valid_accuracies, }, model_base_path, logger, ) last_info = save_checkpoint( { "epoch": epoch + 1, "args": deepcopy(args), "last_checkpoint": save_path, }, logger.path("info"), logger, ) if find_best: logger.log( "<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.".format( epoch_str, valid_a_top1 ) ) copy_checkpoint(model_base_path, model_best_path, logger) with torch.no_grad(): # logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() )) logger.log("{:}".format(search_model.show_alphas())) if api is not None: logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200"))) # measure elapsed time epoch_time.update(time.time() - start_time) start_time = time.time() logger.log("\n" + "-" * 100) logger.log( "DARTS-V1 : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format( total_epoch, search_time.sum, genotypes[total_epoch - 1] ) ) if api is not None: logger.log("{:}".format(api.query_by_arch(genotypes[total_epoch - 1], "200"))) logger.close()
def main(xargs): assert torch.cuda.is_available(), "CUDA is not available." torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.set_num_threads(xargs.workers) prepare_seed(xargs.rand_seed) logger = prepare_logger(args) train_data, valid_data, xshape, class_num = get_datasets( xargs.dataset, xargs.data_path, -1) config = load_config(xargs.config_path, { "class_num": class_num, "xshape": xshape }, logger) search_loader, _, valid_loader = get_nas_search_loaders( train_data, valid_data, xargs.dataset, "configs/nas-benchmark/", (config.batch_size, config.test_batch_size), xargs.workers, ) logger.log( "||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}" .format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size)) logger.log("||||||| {:10s} ||||||| Config={:}".format( xargs.dataset, config)) search_space = get_search_spaces("cell", xargs.search_space_name) if xargs.model_config is None: model_config = dict2config( dict( name="SETN", C=xargs.channel, N=xargs.num_cells, max_nodes=xargs.max_nodes, num_classes=class_num, space=search_space, affine=False, track_running_stats=bool(xargs.track_running_stats), ), None, ) else: model_config = load_config( xargs.model_config, dict( num_classes=class_num, space=search_space, affine=False, track_running_stats=bool(xargs.track_running_stats), ), None, ) logger.log("search space : {:}".format(search_space)) search_model = get_cell_based_tiny_net(model_config) w_optimizer, w_scheduler, criterion = get_optim_scheduler( search_model.get_weights(), config) a_optimizer = torch.optim.Adam( search_model.get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay, ) logger.log("w-optimizer : {:}".format(w_optimizer)) logger.log("a-optimizer : {:}".format(a_optimizer)) logger.log("w-scheduler : {:}".format(w_scheduler)) logger.log("criterion : {:}".format(criterion)) flop, param = get_model_infos(search_model, xshape) logger.log("FLOP = {:.2f} M, Params = {:.2f} MB".format(flop, param)) logger.log("search-space : {:}".format(search_space)) if xargs.arch_nas_dataset is None: api = None else: api = API(xargs.arch_nas_dataset) logger.log("{:} create API = {:} done".format(time_string(), api)) last_info, model_base_path, model_best_path = ( logger.path("info"), logger.path("model"), logger.path("best"), ) network, criterion = torch.nn.DataParallel( search_model).cuda(), criterion.cuda() if last_info.exists(): # automatically resume from previous checkpoint logger.log("=> loading checkpoint of the last-info '{:}' start".format( last_info)) last_info = torch.load(last_info) start_epoch = last_info["epoch"] checkpoint = torch.load(last_info["last_checkpoint"]) genotypes = checkpoint["genotypes"] valid_accuracies = checkpoint["valid_accuracies"] search_model.load_state_dict(checkpoint["search_model"]) w_scheduler.load_state_dict(checkpoint["w_scheduler"]) w_optimizer.load_state_dict(checkpoint["w_optimizer"]) a_optimizer.load_state_dict(checkpoint["a_optimizer"]) logger.log( "=> loading checkpoint of the last-info '{:}' start with {:}-th epoch." .format(last_info, start_epoch)) else: logger.log("=> do not find the last-info file : {:}".format(last_info)) init_genotype, _ = get_best_arch(valid_loader, network, xargs.select_num) start_epoch, valid_accuracies, genotypes = 0, { "best": -1 }, { -1: init_genotype } # start training start_time, search_time, epoch_time, total_epoch = ( time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup, ) for epoch in range(start_epoch, total_epoch): w_scheduler.update(epoch, 0.0) need_time = "Time Left: {:}".format( convert_secs2time(epoch_time.val * (total_epoch - epoch), True)) epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch) logger.log("\n[Search the {:}-th epoch] {:}, LR={:}".format( epoch_str, need_time, min(w_scheduler.get_lr()))) ( search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5, ) = search_func( search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger, ) search_time.update(time.time() - start_time) logger.log( "[{:}] search [base] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s" .format(epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum)) logger.log( "[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%" .format(epoch_str, search_a_loss, search_a_top1, search_a_top5)) genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num) network.module.set_cal_mode("dynamic", genotype) valid_a_loss, valid_a_top1, valid_a_top5 = valid_func( valid_loader, network, criterion) logger.log( "[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}" .format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype)) # search_model.set_cal_mode('urs') # valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) # logger.log('[{:}] URS---evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5)) # search_model.set_cal_mode('joint') # valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) # logger.log('[{:}] JOINT-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5)) # search_model.set_cal_mode('select') # valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) # logger.log('[{:}] Selec-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5)) # check the best accuracy valid_accuracies[epoch] = valid_a_top1 genotypes[epoch] = genotype logger.log("<<<--->>> The {:}-th epoch : {:}".format( epoch_str, genotypes[epoch])) # save checkpoint save_path = save_checkpoint( { "epoch": epoch + 1, "args": deepcopy(xargs), "search_model": search_model.state_dict(), "w_optimizer": w_optimizer.state_dict(), "a_optimizer": a_optimizer.state_dict(), "w_scheduler": w_scheduler.state_dict(), "genotypes": genotypes, "valid_accuracies": valid_accuracies, }, model_base_path, logger, ) last_info = save_checkpoint( { "epoch": epoch + 1, "args": deepcopy(args), "last_checkpoint": save_path, }, logger.path("info"), logger, ) with torch.no_grad(): logger.log("{:}".format(search_model.show_alphas())) if api is not None: logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200"))) # measure elapsed time epoch_time.update(time.time() - start_time) start_time = time.time() # the final post procedure : count the time start_time = time.time() genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num) search_time.update(time.time() - start_time) network.module.set_cal_mode("dynamic", genotype) valid_a_loss, valid_a_top1, valid_a_top5 = valid_func( valid_loader, network, criterion) logger.log( "Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%." .format(genotype, valid_a_top1)) logger.log("\n" + "-" * 100) # check the performance from the architecture dataset logger.log( "SETN : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format( total_epoch, search_time.sum, genotype)) if api is not None: logger.log("{:}".format(api.query_by_arch(genotype, "200"))) logger.close()
def main(xargs): assert torch.cuda.is_available(), "CUDA is not available." torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.set_num_threads(xargs.workers) prepare_seed(xargs.rand_seed) logger = prepare_logger(args) train_data, test_data, xshape, class_num = get_datasets( xargs.dataset, xargs.data_path, -1) logger.log("use config from : {:}".format(xargs.config_path)) config = load_config(xargs.config_path, { "class_num": class_num, "xshape": xshape }, logger) _, train_loader, valid_loader = get_nas_search_loaders( train_data, test_data, xargs.dataset, "configs/nas-benchmark/", config.batch_size, xargs.workers, ) # since ENAS will train the controller on valid-loader, we need to use train transformation for valid-loader valid_loader.dataset.transform = deepcopy(train_loader.dataset.transform) if hasattr(valid_loader.dataset, "transforms"): valid_loader.dataset.transforms = deepcopy( train_loader.dataset.transforms) # data loader logger.log( "||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}" .format(xargs.dataset, len(train_loader), len(valid_loader), config.batch_size)) logger.log("||||||| {:10s} ||||||| Config={:}".format( xargs.dataset, config)) search_space = get_search_spaces("cell", xargs.search_space_name) model_config = dict2config( { "name": "ENAS", "C": xargs.channel, "N": xargs.num_cells, "max_nodes": xargs.max_nodes, "num_classes": class_num, "space": search_space, "affine": False, "track_running_stats": bool(xargs.track_running_stats), }, None, ) shared_cnn = get_cell_based_tiny_net(model_config) controller = shared_cnn.create_controller() w_optimizer, w_scheduler, criterion = get_optim_scheduler( shared_cnn.parameters(), config) a_optimizer = torch.optim.Adam( controller.parameters(), lr=config.controller_lr, betas=config.controller_betas, eps=config.controller_eps, ) logger.log("w-optimizer : {:}".format(w_optimizer)) logger.log("a-optimizer : {:}".format(a_optimizer)) logger.log("w-scheduler : {:}".format(w_scheduler)) logger.log("criterion : {:}".format(criterion)) # flop, param = get_model_infos(shared_cnn, xshape) # logger.log('{:}'.format(shared_cnn)) # logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param)) logger.log("search-space : {:}".format(search_space)) if xargs.arch_nas_dataset is None: api = None else: api = API(xargs.arch_nas_dataset) logger.log("{:} create API = {:} done".format(time_string(), api)) shared_cnn, controller, criterion = ( torch.nn.DataParallel(shared_cnn).cuda(), controller.cuda(), criterion.cuda(), ) last_info, model_base_path, model_best_path = ( logger.path("info"), logger.path("model"), logger.path("best"), ) if last_info.exists(): # automatically resume from previous checkpoint logger.log("=> loading checkpoint of the last-info '{:}' start".format( last_info)) last_info = torch.load(last_info) start_epoch = last_info["epoch"] checkpoint = torch.load(last_info["last_checkpoint"]) genotypes = checkpoint["genotypes"] baseline = checkpoint["baseline"] valid_accuracies = checkpoint["valid_accuracies"] shared_cnn.load_state_dict(checkpoint["shared_cnn"]) controller.load_state_dict(checkpoint["controller"]) w_scheduler.load_state_dict(checkpoint["w_scheduler"]) w_optimizer.load_state_dict(checkpoint["w_optimizer"]) a_optimizer.load_state_dict(checkpoint["a_optimizer"]) logger.log( "=> loading checkpoint of the last-info '{:}' start with {:}-th epoch." .format(last_info, start_epoch)) else: logger.log("=> do not find the last-info file : {:}".format(last_info)) start_epoch, valid_accuracies, genotypes, baseline = 0, { "best": -1 }, {}, None # start training start_time, search_time, epoch_time, total_epoch = ( time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup, ) for epoch in range(start_epoch, total_epoch): w_scheduler.update(epoch, 0.0) need_time = "Time Left: {:}".format( convert_secs2time(epoch_time.val * (total_epoch - epoch), True)) epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch) logger.log( "\n[Search the {:}-th epoch] {:}, LR={:}, baseline={:}".format( epoch_str, need_time, min(w_scheduler.get_lr()), baseline)) cnn_loss, cnn_top1, cnn_top5 = train_shared_cnn( train_loader, shared_cnn, controller, criterion, w_scheduler, w_optimizer, epoch_str, xargs.print_freq, logger, ) logger.log( "[{:}] shared-cnn : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%" .format(epoch_str, cnn_loss, cnn_top1, cnn_top5)) ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline = train_controller( valid_loader, shared_cnn, controller, criterion, a_optimizer, dict2config( { "baseline": baseline, "ctl_train_steps": xargs.controller_train_steps, "ctl_num_aggre": xargs.controller_num_aggregate, "ctl_entropy_w": xargs.controller_entropy_weight, "ctl_bl_dec": xargs.controller_bl_dec, }, None, ), epoch_str, xargs.print_freq, logger, ) search_time.update(time.time() - start_time) logger.log( "[{:}] controller : loss={:.2f}, accuracy={:.2f}%, baseline={:.2f}, reward={:.2f}, current-baseline={:.4f}, time-cost={:.1f} s" .format( epoch_str, ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline, search_time.sum, )) best_arch, _ = get_best_arch(controller, shared_cnn, valid_loader) shared_cnn.module.update_arch(best_arch) _, best_valid_acc, _ = valid_func(valid_loader, shared_cnn, criterion) genotypes[epoch] = best_arch # check the best accuracy valid_accuracies[epoch] = best_valid_acc if best_valid_acc > valid_accuracies["best"]: valid_accuracies["best"] = best_valid_acc genotypes["best"] = best_arch find_best = True else: find_best = False logger.log("<<<--->>> The {:}-th epoch : {:}".format( epoch_str, genotypes[epoch])) # save checkpoint save_path = save_checkpoint( { "epoch": epoch + 1, "args": deepcopy(xargs), "baseline": baseline, "shared_cnn": shared_cnn.state_dict(), "controller": controller.state_dict(), "w_optimizer": w_optimizer.state_dict(), "a_optimizer": a_optimizer.state_dict(), "w_scheduler": w_scheduler.state_dict(), "genotypes": genotypes, "valid_accuracies": valid_accuracies, }, model_base_path, logger, ) last_info = save_checkpoint( { "epoch": epoch + 1, "args": deepcopy(args), "last_checkpoint": save_path, }, logger.path("info"), logger, ) if find_best: logger.log( "<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%." .format(epoch_str, best_valid_acc)) copy_checkpoint(model_base_path, model_best_path, logger) if api is not None: logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200"))) # measure elapsed time epoch_time.update(time.time() - start_time) start_time = time.time() logger.log("\n" + "-" * 100) logger.log("During searching, the best architecture is {:}".format( genotypes["best"])) logger.log("Its accuracy is {:.2f}%".format(valid_accuracies["best"])) logger.log("Randomly select {:} architectures and select the best.".format( xargs.controller_num_samples)) start_time = time.time() final_arch, _ = get_best_arch(controller, shared_cnn, valid_loader, xargs.controller_num_samples) search_time.update(time.time() - start_time) shared_cnn.module.update_arch(final_arch) final_loss, final_top1, final_top5 = valid_func(valid_loader, shared_cnn, criterion) logger.log("The Selected Final Architecture : {:}".format(final_arch)) logger.log("Loss={:.3f}, Accuracy@1={:.2f}%, Accuracy@5={:.2f}%".format( final_loss, final_top1, final_top5)) logger.log( "ENAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format( total_epoch, search_time.sum, final_arch)) if api is not None: logger.log("{:}".format(api.query_by_arch(final_arch))) logger.close()
def main(xargs): assert torch.cuda.is_available(), "CUDA is not available." torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True # torch.set_num_threads(xargs.workers) prepare_seed(xargs.rand_seed) logger = prepare_logger(args) train_data, valid_data, xshape, class_num = get_datasets( xargs.dataset, xargs.data_path, -1 ) if xargs.overwite_epochs is None: extra_info = {"class_num": class_num, "xshape": xshape} else: extra_info = { "class_num": class_num, "xshape": xshape, "epochs": xargs.overwite_epochs, } config = load_config(xargs.config_path, extra_info, logger) search_loader, train_loader, valid_loader = get_nas_search_loaders( train_data, valid_data, xargs.dataset, "configs/nas-benchmark/", (config.batch_size, config.test_batch_size), xargs.workers, ) logger.log( "||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format( xargs.dataset, len(search_loader), len(valid_loader), config.batch_size ) ) logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config)) search_space = get_search_spaces(xargs.search_space, "nats-bench") model_config = dict2config( dict( name="generic", super_type="search-shape", candidate_Cs=search_space["candidates"], max_num_Cs=search_space["numbers"], num_classes=class_num, genotype=args.genotype, affine=bool(xargs.affine), track_running_stats=bool(xargs.track_running_stats), ), None, ) logger.log("search space : {:}".format(search_space)) logger.log("model config : {:}".format(model_config)) search_model = get_cell_based_tiny_net(model_config) search_model.set_algo(xargs.algo) logger.log("{:}".format(search_model)) w_optimizer, w_scheduler, criterion = get_optim_scheduler( search_model.weights, config ) a_optimizer = torch.optim.Adam( search_model.alphas, lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay, eps=xargs.arch_eps, ) logger.log("w-optimizer : {:}".format(w_optimizer)) logger.log("a-optimizer : {:}".format(a_optimizer)) logger.log("w-scheduler : {:}".format(w_scheduler)) logger.log("criterion : {:}".format(criterion)) params = count_parameters_in_MB(search_model) logger.log("The parameters of the search model = {:.2f} MB".format(params)) logger.log("search-space : {:}".format(search_space)) if bool(xargs.use_api): api = create(None, "size", fast_mode=True, verbose=False) else: api = None logger.log("{:} create API = {:} done".format(time_string(), api)) last_info, model_base_path, model_best_path = ( logger.path("info"), logger.path("model"), logger.path("best"), ) network, criterion = search_model.cuda(), criterion.cuda() # use a single GPU last_info, model_base_path, model_best_path = ( logger.path("info"), logger.path("model"), logger.path("best"), ) if last_info.exists(): # automatically resume from previous checkpoint logger.log( "=> loading checkpoint of the last-info '{:}' start".format(last_info) ) last_info = torch.load(last_info) start_epoch = last_info["epoch"] checkpoint = torch.load(last_info["last_checkpoint"]) genotypes = checkpoint["genotypes"] valid_accuracies = checkpoint["valid_accuracies"] search_model.load_state_dict(checkpoint["search_model"]) w_scheduler.load_state_dict(checkpoint["w_scheduler"]) w_optimizer.load_state_dict(checkpoint["w_optimizer"]) a_optimizer.load_state_dict(checkpoint["a_optimizer"]) logger.log( "=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format( last_info, start_epoch ) ) else: logger.log("=> do not find the last-info file : {:}".format(last_info)) start_epoch, valid_accuracies, genotypes = 0, {"best": -1}, {-1: network.random} # start training start_time, search_time, epoch_time, total_epoch = ( time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup, ) for epoch in range(start_epoch, total_epoch): w_scheduler.update(epoch, 0.0) need_time = "Time Left: {:}".format( convert_secs2time(epoch_time.val * (total_epoch - epoch), True) ) epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch) if ( xargs.warmup_ratio is None or xargs.warmup_ratio <= float(epoch) / total_epoch ): enable_controller = True network.set_warmup_ratio(None) else: enable_controller = False network.set_warmup_ratio( 1.0 - float(epoch) / total_epoch / xargs.warmup_ratio ) logger.log( "\n[Search the {:}-th epoch] {:}, LR={:}, controller-warmup={:}, enable_controller={:}".format( epoch_str, need_time, min(w_scheduler.get_lr()), network.warmup_ratio, enable_controller, ) ) if xargs.algo == "mask_gumbel" or xargs.algo == "tas": network.set_tau( xargs.tau_max - (xargs.tau_max - xargs.tau_min) * epoch / (total_epoch - 1) ) logger.log("[RESET tau as : {:}]".format(network.tau)) ( search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5, ) = search_func( search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, enable_controller, xargs.algo, epoch_str, xargs.print_freq, logger, ) search_time.update(time.time() - start_time) logger.log( "[{:}] search [base] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s".format( epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum ) ) logger.log( "[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format( epoch_str, search_a_loss, search_a_top1, search_a_top5 ) ) genotype = network.genotype logger.log("[{:}] - [get_best_arch] : {:}".format(epoch_str, genotype)) valid_a_loss, valid_a_top1, valid_a_top5 = valid_func( valid_loader, network, criterion, logger ) logger.log( "[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}".format( epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype ) ) valid_accuracies[epoch] = valid_a_top1 genotypes[epoch] = genotype logger.log( "<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch]) ) # save checkpoint save_path = save_checkpoint( { "epoch": epoch + 1, "args": deepcopy(xargs), "search_model": search_model.state_dict(), "w_optimizer": w_optimizer.state_dict(), "a_optimizer": a_optimizer.state_dict(), "w_scheduler": w_scheduler.state_dict(), "genotypes": genotypes, "valid_accuracies": valid_accuracies, }, model_base_path, logger, ) last_info = save_checkpoint( { "epoch": epoch + 1, "args": deepcopy(args), "last_checkpoint": save_path, }, logger.path("info"), logger, ) with torch.no_grad(): logger.log("{:}".format(search_model.show_alphas())) if api is not None: logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "90"))) # measure elapsed time epoch_time.update(time.time() - start_time) start_time = time.time() # the final post procedure : count the time start_time = time.time() genotype = network.genotype search_time.update(time.time() - start_time) valid_a_loss, valid_a_top1, valid_a_top5 = valid_func( valid_loader, network, criterion, logger ) logger.log( "Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.".format( genotype, valid_a_top1 ) ) logger.log("\n" + "-" * 100) # check the performance from the architecture dataset logger.log( "[{:}] run {:} epochs, cost {:.1f} s, last-geno is {:}.".format( xargs.algo, total_epoch, search_time.sum, genotype ) ) if api is not None: logger.log("{:}".format(api.query_by_arch(genotype, "90"))) logger.close()
def evaluate_for_seed(arch_config, opt_config, train_loader, valid_loaders, seed: int, logger): """A modular function to train and evaluate a single network, using the given random seed and optimization config with the provided loaders.""" prepare_seed(seed) # random seed net = get_cell_based_tiny_net(arch_config) # net = TinyNetwork(arch_config['channel'], arch_config['num_cells'], arch, config.class_num) flop, param = get_model_infos(net, opt_config.xshape) logger.log("Network : {:}".format(net.get_message()), False) logger.log( "{:} Seed-------------------------- {:} --------------------------". format(time_string(), seed)) logger.log("FLOP = {:} MB, Param = {:} MB".format(flop, param)) # train and valid optimizer, scheduler, criterion = get_optim_scheduler( net.parameters(), opt_config) default_device = torch.cuda.current_device() network = torch.nn.DataParallel(net, device_ids=[default_device ]).cuda(device=default_device) criterion = criterion.cuda(device=default_device) # start training start_time, epoch_time, total_epoch = ( time.time(), AverageMeter(), opt_config.epochs + opt_config.warmup, ) ( train_losses, train_acc1es, train_acc5es, valid_losses, valid_acc1es, valid_acc5es, ) = ({}, {}, {}, {}, {}, {}) train_times, valid_times, lrs = {}, {}, {} for epoch in range(total_epoch): scheduler.update(epoch, 0.0) lr = min(scheduler.get_lr()) train_loss, train_acc1, train_acc5, train_tm = procedure( train_loader, network, criterion, scheduler, optimizer, "train") train_losses[epoch] = train_loss train_acc1es[epoch] = train_acc1 train_acc5es[epoch] = train_acc5 train_times[epoch] = train_tm lrs[epoch] = lr with torch.no_grad(): for key, xloder in valid_loaders.items(): valid_loss, valid_acc1, valid_acc5, valid_tm = procedure( xloder, network, criterion, None, None, "valid") valid_losses["{:}@{:}".format(key, epoch)] = valid_loss valid_acc1es["{:}@{:}".format(key, epoch)] = valid_acc1 valid_acc5es["{:}@{:}".format(key, epoch)] = valid_acc5 valid_times["{:}@{:}".format(key, epoch)] = valid_tm # measure elapsed time epoch_time.update(time.time() - start_time) start_time = time.time() need_time = "Time Left: {:}".format( convert_secs2time(epoch_time.avg * (total_epoch - epoch - 1), True)) logger.log( "{:} {:} epoch={:03d}/{:03d} :: Train [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%] Valid [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%], lr={:}" .format( time_string(), need_time, epoch, total_epoch, train_loss, train_acc1, train_acc5, valid_loss, valid_acc1, valid_acc5, lr, )) info_seed = { "flop": flop, "param": param, "arch_config": arch_config._asdict(), "opt_config": opt_config._asdict(), "total_epoch": total_epoch, "train_losses": train_losses, "train_acc1es": train_acc1es, "train_acc5es": train_acc5es, "train_times": train_times, "valid_losses": valid_losses, "valid_acc1es": valid_acc1es, "valid_acc5es": valid_acc5es, "valid_times": valid_times, "learning_rates": lrs, "net_state_dict": net.state_dict(), "net_string": "{:}".format(net), "finish-train": True, } return info_seed
def main(xargs): assert torch.cuda.is_available(), "CUDA is not available." torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.set_num_threads(xargs.workers) prepare_seed(xargs.rand_seed) logger = prepare_logger(args) train_data, valid_data, xshape, class_num = get_datasets( xargs.dataset, xargs.data_path, -1 ) config = load_config( xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger ) search_loader, _, valid_loader = get_nas_search_loaders( train_data, valid_data, xargs.dataset, "configs/nas-benchmark/", (config.batch_size, config.test_batch_size), xargs.workers, ) logger.log( "||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format( xargs.dataset, len(search_loader), len(valid_loader), config.batch_size ) ) logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config)) search_space = get_search_spaces("cell", xargs.search_space_name) model_config = dict2config( { "name": "RANDOM", "C": xargs.channel, "N": xargs.num_cells, "max_nodes": xargs.max_nodes, "num_classes": class_num, "space": search_space, "affine": False, "track_running_stats": bool(xargs.track_running_stats), }, None, ) search_model = get_cell_based_tiny_net(model_config) w_optimizer, w_scheduler, criterion = get_optim_scheduler( search_model.parameters(), config ) logger.log("w-optimizer : {:}".format(w_optimizer)) logger.log("w-scheduler : {:}".format(w_scheduler)) logger.log("criterion : {:}".format(criterion)) if xargs.arch_nas_dataset is None: api = None else: api = API(xargs.arch_nas_dataset) logger.log("{:} create API = {:} done".format(time_string(), api)) last_info, model_base_path, model_best_path = ( logger.path("info"), logger.path("model"), logger.path("best"), ) network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda() if last_info.exists(): # automatically resume from previous checkpoint logger.log( "=> loading checkpoint of the last-info '{:}' start".format(last_info) ) last_info = torch.load(last_info) start_epoch = last_info["epoch"] checkpoint = torch.load(last_info["last_checkpoint"]) genotypes = checkpoint["genotypes"] valid_accuracies = checkpoint["valid_accuracies"] search_model.load_state_dict(checkpoint["search_model"]) w_scheduler.load_state_dict(checkpoint["w_scheduler"]) w_optimizer.load_state_dict(checkpoint["w_optimizer"]) logger.log( "=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format( last_info, start_epoch ) ) else: logger.log("=> do not find the last-info file : {:}".format(last_info)) start_epoch, valid_accuracies, genotypes = 0, {"best": -1}, {} # start training start_time, search_time, epoch_time, total_epoch = ( time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup, ) for epoch in range(start_epoch, total_epoch): w_scheduler.update(epoch, 0.0) need_time = "Time Left: {:}".format( convert_secs2time(epoch_time.val * (total_epoch - epoch), True) ) epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch) logger.log( "\n[Search the {:}-th epoch] {:}, LR={:}".format( epoch_str, need_time, min(w_scheduler.get_lr()) ) ) # selected_arch = search_find_best(valid_loader, network, criterion, xargs.select_num) search_w_loss, search_w_top1, search_w_top5 = search_func( search_loader, network, criterion, w_scheduler, w_optimizer, epoch_str, xargs.print_freq, logger, ) search_time.update(time.time() - start_time) logger.log( "[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s".format( epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum ) ) valid_a_loss, valid_a_top1, valid_a_top5 = valid_func( valid_loader, network, criterion ) logger.log( "[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format( epoch_str, valid_a_loss, valid_a_top1, valid_a_top5 ) ) cur_arch, cur_valid_acc = search_find_best( valid_loader, network, xargs.select_num ) logger.log( "[{:}] find-the-best : {:}, accuracy@1={:.2f}%".format( epoch_str, cur_arch, cur_valid_acc ) ) genotypes[epoch] = cur_arch # check the best accuracy valid_accuracies[epoch] = valid_a_top1 if valid_a_top1 > valid_accuracies["best"]: valid_accuracies["best"] = valid_a_top1 find_best = True else: find_best = False # save checkpoint save_path = save_checkpoint( { "epoch": epoch + 1, "args": deepcopy(xargs), "search_model": search_model.state_dict(), "w_optimizer": w_optimizer.state_dict(), "w_scheduler": w_scheduler.state_dict(), "genotypes": genotypes, "valid_accuracies": valid_accuracies, }, model_base_path, logger, ) last_info = save_checkpoint( { "epoch": epoch + 1, "args": deepcopy(args), "last_checkpoint": save_path, }, logger.path("info"), logger, ) if find_best: logger.log( "<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.".format( epoch_str, valid_a_top1 ) ) copy_checkpoint(model_base_path, model_best_path, logger) if api is not None: logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200"))) # measure elapsed time epoch_time.update(time.time() - start_time) start_time = time.time() logger.log("\n" + "-" * 200) logger.log("Pre-searching costs {:.1f} s".format(search_time.sum)) start_time = time.time() best_arch, best_acc = search_find_best(valid_loader, network, xargs.select_num) search_time.update(time.time() - start_time) logger.log( "RANDOM-NAS finds the best one : {:} with accuracy={:.2f}%, with {:.1f} s.".format( best_arch, best_acc, search_time.sum ) ) if api is not None: logger.log("{:}".format(api.query_by_arch(best_arch, "200"))) logger.close()
def create_result_count(used_seed, dataset, arch_config, results, dataloader_dict): xresult = ResultsCount( dataset, results["net_state_dict"], results["train_acc1es"], results["train_losses"], results["param"], results["flop"], arch_config, used_seed, results["total_epoch"], None, ) net_config = dict2config( { "name": "infer.tiny", "C": arch_config["channel"], "N": arch_config["num_cells"], "genotype": CellStructure.str2structure(arch_config["arch_str"]), "num_classes": arch_config["class_num"], }, None, ) network = get_cell_based_tiny_net(net_config) network.load_state_dict(xresult.get_net_param()) if "train_times" in results: # new version xresult.update_train_info( results["train_acc1es"], results["train_acc5es"], results["train_losses"], results["train_times"], ) xresult.update_eval(results["valid_acc1es"], results["valid_losses"], results["valid_times"]) else: if dataset == "cifar10-valid": xresult.update_OLD_eval("x-valid", results["valid_acc1es"], results["valid_losses"]) loss, top1, top5, latencies = pure_evaluate( dataloader_dict["{:}@{:}".format("cifar10", "test")], network.cuda()) xresult.update_OLD_eval( "ori-test", {results["total_epoch"] - 1: top1}, {results["total_epoch"] - 1: loss}, ) xresult.update_latency(latencies) elif dataset == "cifar10": xresult.update_OLD_eval("ori-test", results["valid_acc1es"], results["valid_losses"]) loss, top1, top5, latencies = pure_evaluate( dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()) xresult.update_latency(latencies) elif dataset == "cifar100" or dataset == "ImageNet16-120": xresult.update_OLD_eval("ori-test", results["valid_acc1es"], results["valid_losses"]) loss, top1, top5, latencies = pure_evaluate( dataloader_dict["{:}@{:}".format(dataset, "valid")], network.cuda()) xresult.update_OLD_eval( "x-valid", {results["total_epoch"] - 1: top1}, {results["total_epoch"] - 1: loss}, ) loss, top1, top5, latencies = pure_evaluate( dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()) xresult.update_OLD_eval( "x-test", {results["total_epoch"] - 1: top1}, {results["total_epoch"] - 1: loss}, ) xresult.update_latency(latencies) else: raise ValueError("invalid dataset name : {:}".format(dataset)) return xresult
def main(xargs): assert torch.cuda.is_available(), "CUDA is not available." torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.set_num_threads(xargs.workers) prepare_seed(xargs.rand_seed) logger = prepare_logger(args) train_data, valid_data, xshape, class_num = get_datasets( xargs.dataset, xargs.data_path, -1 ) # config_path = 'configs/nas-benchmark/algos/GDAS.config' config = load_config( xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger ) search_loader, _, valid_loader = get_nas_search_loaders( train_data, valid_data, xargs.dataset, "../../configs/nas-benchmark/", config.batch_size, xargs.workers, ) logger.log( "||||||| {:10s} ||||||| Search-Loader-Num={:}, batch size={:}".format( xargs.dataset, len(search_loader), config.batch_size ) ) logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config)) search_space = get_search_spaces("cell", xargs.search_space_name) if xargs.model_config is None: model_config = dict2config( { "name": "GDAS", "C": xargs.channel, "N": xargs.num_cells, "max_nodes": xargs.max_nodes, "num_classes": class_num, "space": search_space, "affine": False, "track_running_stats": bool(xargs.track_running_stats), }, None, ) else: model_config = load_config( xargs.model_config, { "num_classes": class_num, "space": search_space, "affine": False, "track_running_stats": bool(xargs.track_running_stats), }, None, ) search_model = {} w_optimizer = {} a_optimizer = {} w_scheduler = {} a_scheduler = {} valid_accuracies, genotypes = {}, {} search_globle_model = get_cell_based_tiny_net(model_config).cuda() for one in search_loader: search_model[one] = get_cell_based_tiny_net(model_config).cuda() search_model[one].load_state_dict(search_globle_model.state_dict()) w_optimizer[one], w_scheduler[one], criterion = get_optim_scheduler(search_model[one].parameters(), config) if args.baseline == "dl": w_optimizer[one] = dlOptimizer(search_model[one].get_weights(), xargs.arch_learning_rate, 0.1) a_optimizer[one] = torch.optim.Adam(search_model[one].get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay,) valid_accuracies[one], genotypes[one] = ( {"best": -1}, {-1: search_model[one].genotype()}, ) criterion = criterion.cuda() logger.log("search-model :\n{:}".format(search_globle_model)) logger.log("model-config : {:}".format(model_config)) # logger.log("w-optimizer : {:}".format(w_optimizer)) # logger.log("a-optimizer : {:}".format(a_optimizer)) # logger.log("w-scheduler : {:}".format(w_scheduler)) # logger.log("criterion : {:}".format(criterion)) flop, param = get_model_infos(search_globle_model, xshape) logger.log("FLOP = {:.2f} M, Params = {:.2f} MB".format(flop, param)) logger.log("search-space [{:} ops] : {:}".format(len(search_space), search_space)) if xargs.arch_nas_dataset is None: api = None else: api = API(xargs.arch_nas_dataset) logger.log("{:} create API = {:} done".format(time_string(), api)) last_info, model_base_path, model_best_path = ( logger.path("info"), logger.path("model"), logger.path("best"), ) # if last_info.exists(): # automatically resume from previous checkpoint # logger.log( # "=> loading checkpoint of the last-info '{:}' start".format(last_info) # ) # last_info = torch.load(last_info) # start_epoch = last_info["epoch"] # checkpoint = torch.load(last_info["last_checkpoint"]) # genotypes = checkpoint["genotypes"] # valid_accuracies = checkpoint["valid_accuracies"] # search_model.load_state_dict(checkpoint["search_model"]) # w_scheduler.load_state_dict(checkpoint["w_scheduler"]) # w_optimizer.load_state_dict(checkpoint["w_optimizer"]) # a_optimizer.load_state_dict(checkpoint["a_optimizer"]) # logger.log( # "=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format( # last_info, start_epoch # ) # ) # else: logger.log("=> do not find the last-info file : {:}".format(last_info)) start_epoch = 0 # start training start_time, search_time, epoch_time, total_epoch = ( time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup, ) local_epoch = args.local_epoch for epoch in range(start_epoch, total_epoch): for user in w_scheduler: w_scheduler[user].update(epoch, 0.0) search_model[user].set_tau( xargs.tau_max - (xargs.tau_max - xargs.tau_min) * epoch / (total_epoch - 1) ) # need_time = "Time Left: {:}".format( # convert_secs2time(epoch_time.val * (total_epoch - epoch), True) # ) epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch) # logger.log( # "\n[Search the {:}-th epoch] {:}, tau={:}, LR={:}".format( # epoch_str, need_time, search_model.get_tau(), min(w_scheduler.get_lr()) # ) # ) weight_list = [] acc_list = [] test_acc_list = [] for user in search_loader: ( search_w_loss, search_w_top1, search_w_top5, valid_a_loss, valid_a_top1, valid_a_top5, weight ) = search_func( search_loader[user], search_model[user], search_globle_model, criterion, w_scheduler[user], w_optimizer[user], a_optimizer[user], epoch_str, xargs.print_freq, logger, local_epoch ) logger.log( "User {} : [{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s".format( user, epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum ) ) logger.log( "User {} : [{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format( user, epoch_str, valid_a_loss, valid_a_top1, valid_a_top5 ) ) weight_list.append(weight) acc_list.append(valid_a_top1) valid_accuracies[user][epoch] = valid_a_top1 genotypes[user][epoch] = search_model[user].genotype() # loss, top1acc, top5acc = test_func(valid_loader[user], search_model[user], criterion) # test_acc_list.append(top1acc) # logger.log( # "||||---|||| The {epoch:}-th epoch, user {user}, valid loss={loss:.3f}, valid_top1={top1:.2f}%, valid_top5={top5:.2f}%".format( # epoch=epoch_str, user=user, loss=loss, top1=top1acc, top5=top5acc, ) # ) info_dict = { "{}user_w_loss".format(user): search_w_loss, "{}user_w_top1".format(user): search_w_top1, "{}user_w_top5".format(user): search_w_top5, "{}user_a_loss".format(user): valid_a_loss, "{}user_a_top1".format(user): valid_a_top1, "{}user_a_top5".format(user): valid_a_top5, # "{}user_test_loss".format(user): search_w_loss, # "{}user_test_top1".format(user): search_w_loss, # "{}user_test_top5".format(user): search_w_loss, } wandb.log(info_dict) info_dict = { "epoch": epoch, "average_valid_acc": np.average(acc_list), "average_test_acc": np.average(test_acc_list) } wandb.log(info_dict) arch_personalize = args.personalize_arch weight_average, arch_normal_list, arch_reduce_list = average_weights(weight_list, arch_personalize) for user in search_model: if arch_personalize: tep = copy.deepcopy(weight_average) tep['arch_normal_parameters'] = arch_normal_list[user] tep['arch_reduce_parameters'] = arch_reduce_list[user] search_model[user].load_state_dict(tep) else: search_model[user].load_state_dict(weight_average) logger.log( "<<<--->>> The {:}-th epoch : {:}".format(epoch_str, search_model[user].genotype()) ) search_globle_model.load_state_dict(weight_average) search_time.update(time.time() - start_time) # check the best accuracy # if valid_a_top1 > valid_accuracies["best"]: # valid_accuracies["best"] = valid_a_top1 # genotypes["best"] = search_model.genotype() # find_best = True # else: # find_best = False # save checkpoint # save_path = save_checkpoint( # { # "epoch": epoch + 1, # "args": deepcopy(xargs), # "search_model": search_model.state_dict(), # "w_optimizer": w_optimizer.state_dict(), # "a_optimizer": a_optimizer.state_dict(), # "w_scheduler": w_scheduler.state_dict(), # "genotypes": genotypes, # "valid_accuracies": valid_accuracies, # }, # model_base_path, # logger, # ) # last_info = save_checkpoint( # { # "epoch": epoch + 1, # "args": deepcopy(args), # "last_checkpoint": save_path, # }, # logger.path("info"), # logger, # ) # if find_best: # logger.log( # "<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.".format( # epoch_str, valid_a_top1 # ) # ) # copy_checkpoint(model_base_path, model_best_path, logger) # with torch.no_grad(): # logger.log("{:}".format(search_globle_model.show_alphas())) # if api is not None: # logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200"))) # measure elapsed time epoch_time.update(time.time() - start_time) start_time = time.time() # save checkpoint for user in search_model: model_base_path = logger.model_dir / "User{:}-acc-{}-basic-seed-{:}.pth".format(user, valid_accuracies[user][epoch],args.rand_seed) save_path = save_checkpoint( { "epoch": epoch + 1, "args": deepcopy(xargs), "search_model": search_model[user].state_dict(), "w_optimizer": w_optimizer[user].state_dict(), "a_optimizer": a_optimizer[user].state_dict(), "w_scheduler": w_scheduler[user].state_dict(), "genotypes": search_model[user].genotype(), "valid_accuracies": valid_accuracies[user], }, model_base_path, logger, ) # logger.log("\n" + "-" * 100) # # check the performance from the architecture dataset # logger.log( # "GDAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format( # total_epoch, search_time.sum, genotypes[total_epoch - 1] # ) # ) # if api is not None: # logger.log("{:}".format(api.query_by_arch(genotypes[total_epoch - 1], "200"))) logger.close()