##################################################### # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 # ##################################################### import os, sys, time, argparse, collections import numpy as np import torch from pathlib import Path from collections import defaultdict, OrderedDict from typing import Dict, Any, Text, List from xautodl.log_utils import AverageMeter, time_string, convert_secs2time from xautodl.config_utils import dict2config # NAS-Bench-201 related module or function from xautodl.models import CellStructure, get_cell_based_tiny_net from xautodl.procedures import ( bench_pure_evaluate as pure_evaluate, get_nas_bench_loaders, ) from nas_201_api import NASBench201API, ArchResults, ResultsCount api = NASBench201API( "{:}/.torch/NAS-Bench-201-v1_0-e61699.pth".format(os.environ["HOME"]) ) def create_result_count( used_seed: int, dataset: Text, arch_config: Dict[Text, Any], results: Dict[Text, Any], dataloader_dict: Dict[Text, Any], ) -> ResultsCount: 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 account_one_arch( arch_index: int, arch_str: Text, checkpoints: List[Text], datasets: List[Text], dataloader_dict: Dict[Text, Any], ) -> ArchResults: information = ArchResults(arch_index, arch_str) for checkpoint_path in checkpoints: checkpoint = torch.load(checkpoint_path, map_location="cpu") used_seed = checkpoint_path.name.split("-")[-1].split(".")[0] ok_dataset = 0 for dataset in datasets: if dataset not in checkpoint: print( "Can not find {:} in arch-{:} from {:}".format( dataset, arch_index, checkpoint_path ) ) continue else: ok_dataset += 1 results = checkpoint[dataset] assert results[ "finish-train" ], "This {:} arch seed={:} does not finish train on {:} ::: {:}".format( arch_index, used_seed, dataset, checkpoint_path ) arch_config = { "channel": results["channel"], "num_cells": results["num_cells"], "arch_str": arch_str, "class_num": results["config"]["class_num"], } xresult = create_result_count( used_seed, dataset, arch_config, results, dataloader_dict ) information.update(dataset, int(used_seed), xresult) if ok_dataset == 0: raise ValueError("{:} does not find any data".format(checkpoint_path)) return information def correct_time_related_info( arch_index: int, arch_info_full: ArchResults, arch_info_less: ArchResults ): # calibrate the latency based on NAS-Bench-201-v1_0-e61699.pth cifar010_latency = ( api.get_latency(arch_index, "cifar10-valid", hp="200") + api.get_latency(arch_index, "cifar10", hp="200") ) / 2 arch_info_full.reset_latency("cifar10-valid", None, cifar010_latency) arch_info_full.reset_latency("cifar10", None, cifar010_latency) arch_info_less.reset_latency("cifar10-valid", None, cifar010_latency) arch_info_less.reset_latency("cifar10", None, cifar010_latency) cifar100_latency = api.get_latency(arch_index, "cifar100", hp="200") arch_info_full.reset_latency("cifar100", None, cifar100_latency) arch_info_less.reset_latency("cifar100", None, cifar100_latency) image_latency = api.get_latency(arch_index, "ImageNet16-120", hp="200") arch_info_full.reset_latency("ImageNet16-120", None, image_latency) arch_info_less.reset_latency("ImageNet16-120", None, image_latency) train_per_epoch_time = list( arch_info_less.query("cifar10-valid", 777).train_times.values() ) train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time) eval_ori_test_time, eval_x_valid_time = [], [] for key, value in arch_info_less.query("cifar10-valid", 777).eval_times.items(): if key.startswith("ori-test@"): eval_ori_test_time.append(value) elif key.startswith("x-valid@"): eval_x_valid_time.append(value) else: raise ValueError("-- {:} --".format(key)) eval_ori_test_time, eval_x_valid_time = float(np.mean(eval_ori_test_time)), float( np.mean(eval_x_valid_time) ) nums = { "ImageNet16-120-train": 151700, "ImageNet16-120-valid": 3000, "ImageNet16-120-test": 6000, "cifar10-valid-train": 25000, "cifar10-valid-valid": 25000, "cifar10-train": 50000, "cifar10-test": 10000, "cifar100-train": 50000, "cifar100-test": 10000, "cifar100-valid": 5000, } eval_per_sample = (eval_ori_test_time + eval_x_valid_time) / ( nums["cifar10-valid-valid"] + nums["cifar10-test"] ) for arch_info in [arch_info_less, arch_info_full]: arch_info.reset_pseudo_train_times( "cifar10-valid", None, train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar10-valid-train"], ) arch_info.reset_pseudo_train_times( "cifar10", None, train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar10-train"], ) arch_info.reset_pseudo_train_times( "cifar100", None, train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar100-train"], ) arch_info.reset_pseudo_train_times( "ImageNet16-120", None, train_per_epoch_time / nums["cifar10-valid-train"] * nums["ImageNet16-120-train"], ) arch_info.reset_pseudo_eval_times( "cifar10-valid", None, "x-valid", eval_per_sample * nums["cifar10-valid-valid"], ) arch_info.reset_pseudo_eval_times( "cifar10-valid", None, "ori-test", eval_per_sample * nums["cifar10-test"] ) arch_info.reset_pseudo_eval_times( "cifar10", None, "ori-test", eval_per_sample * nums["cifar10-test"] ) arch_info.reset_pseudo_eval_times( "cifar100", None, "x-valid", eval_per_sample * nums["cifar100-valid"] ) arch_info.reset_pseudo_eval_times( "cifar100", None, "x-test", eval_per_sample * nums["cifar100-valid"] ) arch_info.reset_pseudo_eval_times( "cifar100", None, "ori-test", eval_per_sample * nums["cifar100-test"] ) arch_info.reset_pseudo_eval_times( "ImageNet16-120", None, "x-valid", eval_per_sample * nums["ImageNet16-120-valid"], ) arch_info.reset_pseudo_eval_times( "ImageNet16-120", None, "x-test", eval_per_sample * nums["ImageNet16-120-valid"], ) arch_info.reset_pseudo_eval_times( "ImageNet16-120", None, "ori-test", eval_per_sample * nums["ImageNet16-120-test"], ) # arch_info_full.debug_test() # arch_info_less.debug_test() return arch_info_full, arch_info_less def simplify(save_dir, meta_file, basestr, target_dir): meta_infos = torch.load(meta_file, map_location="cpu") meta_archs = meta_infos["archs"] # a list of architecture strings meta_num_archs = meta_infos["total"] assert meta_num_archs == len( meta_archs ), "invalid number of archs : {:} vs {:}".format(meta_num_archs, len(meta_archs)) sub_model_dirs = sorted(list(save_dir.glob("*-*-{:}".format(basestr)))) print( "{:} find {:} directories used to save checkpoints".format( time_string(), len(sub_model_dirs) ) ) subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0 num_seeds = defaultdict(lambda: 0) for index, sub_dir in enumerate(sub_model_dirs): xcheckpoints = list(sub_dir.glob("arch-*-seed-*.pth")) arch_indexes = set() for checkpoint in xcheckpoints: temp_names = checkpoint.name.split("-") assert ( len(temp_names) == 4 and temp_names[0] == "arch" and temp_names[2] == "seed" ), "invalid checkpoint name : {:}".format(checkpoint.name) arch_indexes.add(temp_names[1]) subdir2archs[sub_dir] = sorted(list(arch_indexes)) num_evaluated_arch += len(arch_indexes) # count number of seeds for each architecture for arch_index in arch_indexes: num_seeds[ len(list(sub_dir.glob("arch-{:}-seed-*.pth".format(arch_index)))) ] += 1 print( "{:} There are {:5d} architectures that have been evaluated ({:} in total).".format( time_string(), num_evaluated_arch, meta_num_archs ) ) for key in sorted(list(num_seeds.keys())): print( "{:} There are {:5d} architectures that are evaluated {:} times.".format( time_string(), num_seeds[key], key ) ) dataloader_dict = get_nas_bench_loaders(6) to_save_simply = save_dir / "simplifies" to_save_allarc = save_dir / "simplifies" / "architectures" if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True) if not to_save_allarc.exists(): to_save_allarc.mkdir(parents=True, exist_ok=True) assert (save_dir / target_dir) in subdir2archs, "can not find {:}".format( target_dir ) arch2infos, datasets = {}, ( "cifar10-valid", "cifar10", "cifar100", "ImageNet16-120", ) evaluated_indexes = set() target_full_dir = save_dir / target_dir target_less_dir = save_dir / "{:}-LESS".format(target_dir) arch_indexes = subdir2archs[target_full_dir] num_seeds = defaultdict(lambda: 0) end_time = time.time() arch_time = AverageMeter() for idx, arch_index in enumerate(arch_indexes): checkpoints = list( target_full_dir.glob("arch-{:}-seed-*.pth".format(arch_index)) ) ckps_less = list(target_less_dir.glob("arch-{:}-seed-*.pth".format(arch_index))) # create the arch info for each architecture try: arch_info_full = account_one_arch( arch_index, meta_archs[int(arch_index)], checkpoints, datasets, dataloader_dict, ) arch_info_less = account_one_arch( arch_index, meta_archs[int(arch_index)], ckps_less, datasets, dataloader_dict, ) num_seeds[len(checkpoints)] += 1 except: print("Loading {:} failed, : {:}".format(arch_index, checkpoints)) continue assert ( int(arch_index) not in evaluated_indexes ), "conflict arch-index : {:}".format(arch_index) assert ( 0 <= int(arch_index) < len(meta_archs) ), "invalid arch-index {:} (not found in meta_archs)".format(arch_index) arch_info = {"full": arch_info_full, "less": arch_info_less} evaluated_indexes.add(int(arch_index)) arch2infos[int(arch_index)] = arch_info # to correct the latency and training_time info. arch_info_full, arch_info_less = correct_time_related_info( int(arch_index), arch_info_full, arch_info_less ) to_save_data = OrderedDict( full=arch_info_full.state_dict(), less=arch_info_less.state_dict() ) torch.save(to_save_data, to_save_allarc / "{:}-FULL.pth".format(arch_index)) arch_info["full"].clear_params() arch_info["less"].clear_params() torch.save(to_save_data, to_save_allarc / "{:}-SIMPLE.pth".format(arch_index)) # measure elapsed time arch_time.update(time.time() - end_time) end_time = time.time() need_time = "{:}".format( convert_secs2time(arch_time.avg * (len(arch_indexes) - idx - 1), True) ) print( "{:} {:} [{:03d}/{:03d}] : {:} still need {:}".format( time_string(), target_dir, idx, len(arch_indexes), arch_index, need_time ) ) # measure time xstrs = [ "{:}:{:03d}".format(key, num_seeds[key]) for key in sorted(list(num_seeds.keys())) ] print("{:} {:} done : {:}".format(time_string(), target_dir, xstrs)) final_infos = { "meta_archs": meta_archs, "total_archs": meta_num_archs, "basestr": basestr, "arch2infos": arch2infos, "evaluated_indexes": evaluated_indexes, } save_file_name = to_save_simply / "{:}.pth".format(target_dir) torch.save(final_infos, save_file_name) print( "Save {:} / {:} architecture results into {:}.".format( len(evaluated_indexes), meta_num_archs, save_file_name ) ) def merge_all(save_dir, meta_file, basestr): meta_infos = torch.load(meta_file, map_location="cpu") meta_archs = meta_infos["archs"] meta_num_archs = meta_infos["total"] assert meta_num_archs == len( meta_archs ), "invalid number of archs : {:} vs {:}".format(meta_num_archs, len(meta_archs)) sub_model_dirs = sorted(list(save_dir.glob("*-*-{:}".format(basestr)))) print( "{:} find {:} directories used to save checkpoints".format( time_string(), len(sub_model_dirs) ) ) for index, sub_dir in enumerate(sub_model_dirs): arch_info_files = sorted(list(sub_dir.glob("arch-*-seed-*.pth"))) print( "The {:02d}/{:02d}-th directory : {:} : {:} runs.".format( index, len(sub_model_dirs), sub_dir, len(arch_info_files) ) ) arch2infos, evaluated_indexes = dict(), set() for IDX, sub_dir in enumerate(sub_model_dirs): ckp_path = sub_dir.parent / "simplifies" / "{:}.pth".format(sub_dir.name) if ckp_path.exists(): sub_ckps = torch.load(ckp_path, map_location="cpu") assert ( sub_ckps["total_archs"] == meta_num_archs and sub_ckps["basestr"] == basestr ) xarch2infos = sub_ckps["arch2infos"] xevalindexs = sub_ckps["evaluated_indexes"] for eval_index in xevalindexs: assert ( eval_index not in evaluated_indexes and eval_index not in arch2infos ) # arch2infos[eval_index] = xarch2infos[eval_index].state_dict() arch2infos[eval_index] = { "full": xarch2infos[eval_index]["full"].state_dict(), "less": xarch2infos[eval_index]["less"].state_dict(), } evaluated_indexes.add(eval_index) print( "{:} [{:03d}/{:03d}] merge data from {:} with {:} models.".format( time_string(), IDX, len(sub_model_dirs), ckp_path, len(xevalindexs) ) ) else: raise ValueError("Can not find {:}".format(ckp_path)) # print ('{:} [{:03d}/{:03d}] can not find {:}, skip.'.format(time_string(), IDX, len(subdir2archs), ckp_path)) evaluated_indexes = sorted(list(evaluated_indexes)) print( "Finally, there are {:} architectures that have been trained and evaluated.".format( len(evaluated_indexes) ) ) to_save_simply = save_dir / "simplifies" if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True) final_infos = { "meta_archs": meta_archs, "total_archs": meta_num_archs, "arch2infos": arch2infos, "evaluated_indexes": evaluated_indexes, } save_file_name = to_save_simply / "{:}-final-infos.pth".format(basestr) torch.save(final_infos, save_file_name) print( "Save {:} / {:} architecture results into {:}.".format( len(evaluated_indexes), meta_num_archs, save_file_name ) ) if __name__ == "__main__": parser = argparse.ArgumentParser( description="NAS-BENCH-201", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument( "--mode", type=str, choices=["cal", "merge"], help="The running mode for this script.", ) parser.add_argument( "--base_save_dir", type=str, default="./output/NAS-BENCH-201-4", help="The base-name of folder to save checkpoints and log.", ) parser.add_argument("--target_dir", type=str, help="The target directory.") parser.add_argument( "--max_node", type=int, default=4, help="The maximum node in a cell." ) parser.add_argument( "--channel", type=int, default=16, help="The number of channels." ) parser.add_argument( "--num_cells", type=int, default=5, help="The number of cells in one stage." ) args = parser.parse_args() save_dir = Path(args.base_save_dir) meta_path = save_dir / "meta-node-{:}.pth".format(args.max_node) assert save_dir.exists(), "invalid save dir path : {:}".format(save_dir) assert meta_path.exists(), "invalid saved meta path : {:}".format(meta_path) print( "start the statistics of our nas-benchmark from {:} using {:}.".format( save_dir, args.target_dir ) ) basestr = "C{:}-N{:}".format(args.channel, args.num_cells) if args.mode == "cal": simplify(save_dir, meta_path, basestr, args.target_dir) elif args.mode == "merge": merge_all(save_dir, meta_path, basestr) else: raise ValueError("invalid mode : {:}".format(args.mode))