Update NATS-Bench (sss version 1.0)
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D-X-Y
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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#####################################################
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# python exps/NAS-Bench-201/xshape-collect.py
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#####################################################
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import os, re, sys, time, argparse, collections
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import numpy as np
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import torch
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from tqdm import tqdm
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from pathlib import Path
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from collections import defaultdict, OrderedDict
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from typing import Dict, Any, Text, List
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from log_utils import AverageMeter, time_string, convert_secs2time
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from config_utils import dict2config
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# NAS-Bench-201 related module or function
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from models import CellStructure, get_cell_based_tiny_net
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from nas_201_api import ArchResults, ResultsCount
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from procedures import bench_pure_evaluate as pure_evaluate, get_nas_bench_loaders
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def account_one_arch(arch_index: int, arch_str: Text, checkpoints: List[Text], datasets: List[Text]) -> ArchResults:
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information = ArchResults(arch_index, arch_str)
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for checkpoint_path in checkpoints:
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try:
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checkpoint = torch.load(checkpoint_path, map_location='cpu')
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except:
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raise ValueError('This checkpoint failed to be loaded : {:}'.format(checkpoint_path))
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used_seed = checkpoint_path.name.split('-')[-1].split('.')[0]
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ok_dataset = 0
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for dataset in datasets:
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if dataset not in checkpoint:
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print('Can not find {:} in arch-{:} from {:}'.format(dataset, arch_index, checkpoint_path))
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continue
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else:
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ok_dataset += 1
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results = checkpoint[dataset]
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assert results['finish-train'], 'This {:} arch seed={:} does not finish train on {:} ::: {:}'.format(arch_index, used_seed, dataset, checkpoint_path)
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arch_config = {'name': 'infer.shape.tiny', 'channels': arch_str, 'arch_str': arch_str,
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'genotype': results['arch_config']['genotype'],
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'class_num': results['arch_config']['num_classes']}
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xresult = ResultsCount(dataset, results['net_state_dict'], results['train_acc1es'], results['train_losses'],
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results['param'], results['flop'], arch_config, used_seed, results['total_epoch'], None)
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xresult.update_train_info(results['train_acc1es'], results['train_acc5es'], results['train_losses'], results['train_times'])
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xresult.update_eval(results['valid_acc1es'], results['valid_losses'], results['valid_times'])
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information.update(dataset, int(used_seed), xresult)
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if ok_dataset < len(datasets): raise ValueError('{:} does find enought data : {:} vs {:}'.format(checkpoint_path, ok_dataset, len(datasets)))
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return information
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def correct_time_related_info(hp2info: Dict[Text, ArchResults]):
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# calibrate the latency based on the number of epochs = 01, since they are trained on the same machine.
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x1 = hp2info['01'].get_metrics('cifar10-valid', 'x-valid')['all_time'] / 98
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x2 = hp2info['01'].get_metrics('cifar10-valid', 'ori-test')['all_time'] / 40
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cifar010_latency = (x1 + x2) / 2
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for hp, arch_info in hp2info.items():
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arch_info.reset_latency('cifar10-valid', None, cifar010_latency)
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arch_info.reset_latency('cifar10', None, cifar010_latency)
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# hp2info['01'].get_latency('cifar10')
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x1 = hp2info['01'].get_metrics('cifar100', 'ori-test')['all_time'] / 40
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x2 = hp2info['01'].get_metrics('cifar100', 'x-test')['all_time'] / 20
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x3 = hp2info['01'].get_metrics('cifar100', 'x-valid')['all_time'] / 20
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cifar100_latency = (x1 + x2 + x3) / 3
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for hp, arch_info in hp2info.items():
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arch_info.reset_latency('cifar100', None, cifar100_latency)
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x1 = hp2info['01'].get_metrics('ImageNet16-120', 'ori-test')['all_time'] / 24
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x2 = hp2info['01'].get_metrics('ImageNet16-120', 'x-test')['all_time'] / 12
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x3 = hp2info['01'].get_metrics('ImageNet16-120', 'x-valid')['all_time'] / 12
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image_latency = (x1 + x2 + x3) / 3
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for hp, arch_info in hp2info.items():
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arch_info.reset_latency('ImageNet16-120', None, image_latency)
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# CIFAR10 VALID
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train_per_epoch_time = list(hp2info['01'].query('cifar10-valid', 777).train_times.values())
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train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
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eval_ori_test_time, eval_x_valid_time = [], []
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for key, value in hp2info['01'].query('cifar10-valid', 777).eval_times.items():
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if key.startswith('ori-test@'):
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eval_ori_test_time.append(value)
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elif key.startswith('x-valid@'):
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eval_x_valid_time.append(value)
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else: raise ValueError('-- {:} --'.format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times('cifar10-valid', None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times('cifar10-valid', None, 'x-valid', eval_x_valid_time)
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arch_info.reset_pseudo_eval_times('cifar10-valid', None, 'ori-test', eval_ori_test_time)
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# CIFAR10
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train_per_epoch_time = list(hp2info['01'].query('cifar10', 777).train_times.values())
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train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
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eval_ori_test_time = []
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for key, value in hp2info['01'].query('cifar10', 777).eval_times.items():
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if key.startswith('ori-test@'):
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eval_ori_test_time.append(value)
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else: raise ValueError('-- {:} --'.format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times('cifar10', None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times('cifar10', None, 'ori-test', eval_ori_test_time)
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# CIFAR100
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train_per_epoch_time = list(hp2info['01'].query('cifar100', 777).train_times.values())
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train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
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eval_ori_test_time, eval_x_valid_time, eval_x_test_time = [], [], []
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for key, value in hp2info['01'].query('cifar100', 777).eval_times.items():
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if key.startswith('ori-test@'):
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eval_ori_test_time.append(value)
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elif key.startswith('x-valid@'):
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eval_x_valid_time.append(value)
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elif key.startswith('x-test@'):
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eval_x_test_time.append(value)
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else: raise ValueError('-- {:} --'.format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
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eval_x_test_time = sum(eval_x_test_time) / len(eval_x_test_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times('cifar100', None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times('cifar100', None, 'x-valid', eval_x_valid_time)
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arch_info.reset_pseudo_eval_times('cifar100', None, 'x-test', eval_x_test_time)
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arch_info.reset_pseudo_eval_times('cifar100', None, 'ori-test', eval_ori_test_time)
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# ImageNet16-120
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train_per_epoch_time = list(hp2info['01'].query('ImageNet16-120', 777).train_times.values())
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train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
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eval_ori_test_time, eval_x_valid_time, eval_x_test_time = [], [], []
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for key, value in hp2info['01'].query('ImageNet16-120', 777).eval_times.items():
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if key.startswith('ori-test@'):
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eval_ori_test_time.append(value)
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elif key.startswith('x-valid@'):
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eval_x_valid_time.append(value)
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elif key.startswith('x-test@'):
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eval_x_test_time.append(value)
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else: raise ValueError('-- {:} --'.format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
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eval_x_test_time = sum(eval_x_test_time) / len(eval_x_test_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times('ImageNet16-120', None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'x-valid', eval_x_valid_time)
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arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'x-test', eval_x_test_time)
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arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'ori-test', eval_ori_test_time)
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return hp2info
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def simplify(save_dir, save_name, nets, total):
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hps, seeds = ['01', '12', '90'], set()
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for hp in hps:
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sub_save_dir = save_dir / 'raw-data-{:}'.format(hp)
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ckps = sorted(list(sub_save_dir.glob('arch-*-seed-*.pth')))
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seed2names = defaultdict(list)
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for ckp in ckps:
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parts = re.split('-|\.', ckp.name)
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seed2names[parts[3]].append(ckp.name)
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print('DIR : {:}'.format(sub_save_dir))
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nums = []
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for seed, xlist in seed2names.items():
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seeds.add(seed)
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nums.append(len(xlist))
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print(' seed={:}, num={:}'.format(seed, len(xlist)))
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# assert len(nets) == total == max(nums), 'there are some missed files : {:} vs {:}'.format(max(nums), total)
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print('{:} start simplify the checkpoint.'.format(time_string()))
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datasets = ('cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120')
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simplify_save_dir, arch2infos, evaluated_indexes = save_dir / save_name, {}, set()
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simplify_save_dir.mkdir(parents=True, exist_ok=True)
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end_time, arch_time = time.time(), AverageMeter()
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# for index, arch_str in enumerate(nets):
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for index in tqdm(range(total)):
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arch_str = nets[index]
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hp2info = OrderedDict()
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for hp in hps:
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sub_save_dir = save_dir / 'raw-data-{:}'.format(hp)
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ckps = [sub_save_dir / 'arch-{:06d}-seed-{:}.pth'.format(index, seed) for seed in seeds]
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ckps = [x for x in ckps if x.exists()]
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if len(ckps) == 0: raise ValueError('Invalid data : index={:}, hp={:}'.format(index, hp))
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arch_info = account_one_arch(index, arch_str, ckps, datasets)
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hp2info[hp] = arch_info
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hp2info = correct_time_related_info(hp2info)
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evaluated_indexes.add(index)
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to_save_data = OrderedDict({'01': hp2info['01'].state_dict(),
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'12': hp2info['12'].state_dict(),
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'90': hp2info['90'].state_dict()})
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torch.save(to_save_data, simplify_save_dir / '{:}-FULL.pth'.format(index))
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for hp in hps: hp2info[hp].clear_params()
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to_save_data = OrderedDict({'01': hp2info['01'].state_dict(),
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'12': hp2info['12'].state_dict(),
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'90': hp2info['90'].state_dict()})
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torch.save(to_save_data, simplify_save_dir / '{:}-SIMPLE.pth'.format(index))
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arch2infos[index] = to_save_data
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# measure elapsed time
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arch_time.update(time.time() - end_time)
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end_time = time.time()
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need_time = '{:}'.format(convert_secs2time(arch_time.avg * (total-index-1), True))
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# print('{:} {:06d}/{:06d} : still need {:}'.format(time_string(), index, total, need_time))
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print('{:} {:} done.'.format(time_string(), save_name))
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final_infos = {'meta_archs' : nets,
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'total_archs': total,
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'arch2infos' : arch2infos,
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'evaluated_indexes': evaluated_indexes}
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save_file_name = save_dir / '{:}.pth'.format(save_name)
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torch.save(final_infos, save_file_name)
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print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), total, save_file_name))
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def traverse_net(candidates: List[int], N: int):
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nets = ['']
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for i in range(N):
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new_nets = []
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for net in nets:
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for C in candidates:
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new_nets.append(str(C) if net == '' else "{:}:{:}".format(net,C))
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nets = new_nets
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return nets
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='NAS-BENCH-201', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--base_save_dir', type=str, default='./output/NAS-BENCH-202', help='The base-name of folder to save checkpoints and log.')
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parser.add_argument('--candidateC' , type=int, nargs='+', default=[8, 16, 24, 32, 40, 48, 56, 64], help='.')
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parser.add_argument('--num_layers' , type=int, default=5, help='The number of layers in a network.')
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parser.add_argument('--check_N' , type=int, default=32768, help='For safety.')
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parser.add_argument('--save_name' , type=str, default='simplify', help='The save directory.')
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args = parser.parse_args()
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nets = traverse_net(args.candidateC, args.num_layers)
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if len(nets) != args.check_N: raise ValueError('Pre-num-check failed : {:} vs {:}'.format(len(nets), args.check_N))
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save_dir = Path(args.base_save_dir)
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simplify(save_dir, args.save_name, nets, args.check_N)
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