283 lines
18 KiB
Python
283 lines
18 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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#####################################################
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import os, sys, time, argparse, collections
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import numpy as np
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import torch
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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 NASBench201API, ArchResults, ResultsCount
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from procedures import bench_pure_evaluate as pure_evaluate, get_nas_bench_loaders
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api = NASBench201API('{:}/.torch/NAS-Bench-201-v1_0-e61699.pth'.firmat(os.environ['HOME']))
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def create_result_count(used_seed: int, dataset: Text, arch_config: Dict[Text, Any],
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results: Dict[Text, Any], dataloader_dict: Dict[Text, Any]) -> ResultsCount:
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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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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)
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network = get_cell_based_tiny_net(net_config)
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network.load_state_dict(xresult.get_net_param())
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if 'train_times' in results: # new version
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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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else:
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if dataset == 'cifar10-valid':
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xresult.update_OLD_eval('x-valid' , results['valid_acc1es'], results['valid_losses'])
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loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format('cifar10', 'test')], network.cuda())
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xresult.update_OLD_eval('ori-test', {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
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xresult.update_latency(latencies)
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elif dataset == 'cifar10':
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xresult.update_OLD_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
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loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'test')], network.cuda())
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xresult.update_latency(latencies)
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elif dataset == 'cifar100' or dataset == 'ImageNet16-120':
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xresult.update_OLD_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
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loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'valid')], network.cuda())
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xresult.update_OLD_eval('x-valid', {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
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loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'test')], network.cuda())
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xresult.update_OLD_eval('x-test' , {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
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xresult.update_latency(latencies)
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else:
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raise ValueError('invalid dataset name : {:}'.format(dataset))
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return xresult
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def account_one_arch(arch_index: int, arch_str: Text, checkpoints: List[Text],
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datasets: List[Text], dataloader_dict: Dict[Text, Any]) -> ArchResults:
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information = ArchResults(arch_index, arch_str)
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for checkpoint_path in checkpoints:
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checkpoint = torch.load(checkpoint_path, map_location='cpu')
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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 = {'channel': results['channel'], 'num_cells': results['num_cells'], 'arch_str': arch_str, 'class_num': results['config']['class_num']}
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xresult = create_result_count(used_seed, dataset, arch_config, results, dataloader_dict)
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information.update(dataset, int(used_seed), xresult)
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if ok_dataset == 0: raise ValueError('{:} does not find any data'.format(checkpoint_path))
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return information
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def correct_time_related_info(arch_index: int, arch_info_full: ArchResults, arch_info_less: ArchResults):
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# calibrate the latency based on NAS-Bench-201-v1_0-e61699.pth
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cifar010_latency = (api.get_latency(arch_index, 'cifar10-valid', False) + api.get_latency(arch_index, 'cifar10', False)) / 2
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arch_info_full.reset_latency('cifar10-valid', None, cifar010_latency)
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arch_info_full.reset_latency('cifar10', None, cifar010_latency)
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arch_info_less.reset_latency('cifar10-valid', None, cifar010_latency)
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arch_info_less.reset_latency('cifar10', None, cifar010_latency)
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cifar100_latency = api.get_latency(arch_index, 'cifar100', False)
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arch_info_full.reset_latency('cifar100', None, cifar100_latency)
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arch_info_less.reset_latency('cifar100', None, cifar100_latency)
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image_latency = api.get_latency(arch_index, 'ImageNet16-120', False)
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arch_info_full.reset_latency('ImageNet16-120', None, image_latency)
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arch_info_less.reset_latency('ImageNet16-120', None, image_latency)
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train_per_epoch_time = list(arch_info_less.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 arch_info_less.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, eval_x_valid_time = float(np.mean(eval_ori_test_time)), float(np.mean(eval_x_valid_time))
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nums = {'ImageNet16-120-train': 151700, 'ImageNet16-120-valid': 3000, 'ImageNet16-120-test': 6000,
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'cifar10-valid-train': 25000, 'cifar10-valid-valid': 25000,
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'cifar10-train': 50000, 'cifar10-test': 10000,
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'cifar100-train': 50000, 'cifar100-test': 10000, 'cifar100-valid': 5000}
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eval_per_sample = (eval_ori_test_time + eval_x_valid_time) / (nums['cifar10-valid-valid'] + nums['cifar10-test'])
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for arch_info in [arch_info_less, arch_info_full]:
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arch_info.reset_pseudo_train_times('cifar10-valid', None,
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train_per_epoch_time / nums['cifar10-valid-train'] * nums['cifar10-valid-train'])
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arch_info.reset_pseudo_train_times('cifar10', None,
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train_per_epoch_time / nums['cifar10-valid-train'] * nums['cifar10-train'])
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arch_info.reset_pseudo_train_times('cifar100', None,
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train_per_epoch_time / nums['cifar10-valid-train'] * nums['cifar100-train'])
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arch_info.reset_pseudo_train_times('ImageNet16-120', None,
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train_per_epoch_time / nums['cifar10-valid-train'] * nums['ImageNet16-120-train'])
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arch_info.reset_pseudo_eval_times('cifar10-valid', None, 'x-valid', eval_per_sample*nums['cifar10-valid-valid'])
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arch_info.reset_pseudo_eval_times('cifar10-valid', None, 'ori-test', eval_per_sample * nums['cifar10-test'])
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arch_info.reset_pseudo_eval_times('cifar10', None, 'ori-test', eval_per_sample * nums['cifar10-test'])
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arch_info.reset_pseudo_eval_times('cifar100', None, 'x-valid', eval_per_sample * nums['cifar100-valid'])
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arch_info.reset_pseudo_eval_times('cifar100', None, 'x-test', eval_per_sample * nums['cifar100-valid'])
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arch_info.reset_pseudo_eval_times('cifar100', None, 'ori-test', eval_per_sample * nums['cifar100-test'])
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arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'x-valid', eval_per_sample * nums['ImageNet16-120-valid'])
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arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'x-test', eval_per_sample * nums['ImageNet16-120-valid'])
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arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'ori-test', eval_per_sample * nums['ImageNet16-120-test'])
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# arch_info_full.debug_test()
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# arch_info_less.debug_test()
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# import pdb; pdb.set_trace()
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return arch_info_full, arch_info_less
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def simplify(save_dir, meta_file, basestr, target_dir):
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meta_infos = torch.load(meta_file, map_location='cpu')
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meta_archs = meta_infos['archs'] # a list of architecture strings
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meta_num_archs = meta_infos['total']
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assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))
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sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
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print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
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subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
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num_seeds = defaultdict(lambda: 0)
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for index, sub_dir in enumerate(sub_model_dirs):
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xcheckpoints = list(sub_dir.glob('arch-*-seed-*.pth'))
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arch_indexes = set()
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for checkpoint in xcheckpoints:
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temp_names = checkpoint.name.split('-')
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assert len(temp_names) == 4 and temp_names[0] == 'arch' and temp_names[2] == 'seed', 'invalid checkpoint name : {:}'.format(checkpoint.name)
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arch_indexes.add( temp_names[1] )
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subdir2archs[sub_dir] = sorted(list(arch_indexes))
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num_evaluated_arch += len(arch_indexes)
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# count number of seeds for each architecture
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for arch_index in arch_indexes:
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num_seeds[ len(list(sub_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))) ] += 1
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print('{:} There are {:5d} architectures that have been evaluated ({:} in total).'.format(time_string(), num_evaluated_arch, meta_num_archs))
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for key in sorted( list( num_seeds.keys() ) ): print ('{:} There are {:5d} architectures that are evaluated {:} times.'.format(time_string(), num_seeds[key], key))
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dataloader_dict = get_nas_bench_loaders( 6 )
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to_save_simply = save_dir / 'simplifies'
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to_save_allarc = save_dir / 'simplifies' / 'architectures'
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if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
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if not to_save_allarc.exists(): to_save_allarc.mkdir(parents=True, exist_ok=True)
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assert (save_dir / target_dir) in subdir2archs, 'can not find {:}'.format(target_dir)
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arch2infos, datasets = {}, ('cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120')
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evaluated_indexes = set()
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target_full_dir = save_dir / target_dir
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target_less_dir = save_dir / '{:}-LESS'.format(target_dir)
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arch_indexes = subdir2archs[ target_full_dir ]
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num_seeds = defaultdict(lambda: 0)
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end_time = time.time()
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arch_time = AverageMeter()
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for idx, arch_index in enumerate(arch_indexes):
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checkpoints = list(target_full_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))
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ckps_less = list(target_less_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))
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# create the arch info for each architecture
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try:
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arch_info_full = account_one_arch(arch_index, meta_archs[int(arch_index)], checkpoints, datasets, dataloader_dict)
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arch_info_less = account_one_arch(arch_index, meta_archs[int(arch_index)], ckps_less, datasets, dataloader_dict)
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num_seeds[ len(checkpoints) ] += 1
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except:
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print('Loading {:} failed, : {:}'.format(arch_index, checkpoints))
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continue
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assert int(arch_index) not in evaluated_indexes, 'conflict arch-index : {:}'.format(arch_index)
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assert 0 <= int(arch_index) < len(meta_archs), 'invalid arch-index {:} (not found in meta_archs)'.format(arch_index)
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arch_info = {'full': arch_info_full, 'less': arch_info_less}
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evaluated_indexes.add(int(arch_index))
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arch2infos[int(arch_index)] = arch_info
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# to correct the latency and training_time info.
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arch_info_full, arch_info_less = correct_time_related_info(int(arch_index), arch_info_full, arch_info_less)
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to_save_data = OrderedDict(full=arch_info_full.state_dict(), less=arch_info_less.state_dict())
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torch.save(to_save_data, to_save_allarc / '{:}-FULL.pth'.format(arch_index))
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arch_info['full'].clear_params()
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arch_info['less'].clear_params()
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torch.save(to_save_data, to_save_allarc / '{:}-SIMPLE.pth'.format(arch_index))
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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 * (len(arch_indexes)-idx-1), True) )
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print('{:} {:} [{:03d}/{:03d}] : {:} still need {:}'.format(time_string(), target_dir, idx, len(arch_indexes), arch_index, need_time))
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# measure time
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xstrs = ['{:}:{:03d}'.format(key, num_seeds[key]) for key in sorted( list( num_seeds.keys() ) ) ]
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print('{:} {:} done : {:}'.format(time_string(), target_dir, xstrs))
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final_infos = {'meta_archs' : meta_archs,
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'total_archs': meta_num_archs,
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'basestr' : basestr,
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'arch2infos' : arch2infos,
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'evaluated_indexes': evaluated_indexes}
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save_file_name = to_save_simply / '{:}.pth'.format(target_dir)
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torch.save(final_infos, save_file_name)
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print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
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def merge_all(save_dir, meta_file, basestr):
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meta_infos = torch.load(meta_file, map_location='cpu')
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meta_archs = meta_infos['archs']
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meta_num_archs = meta_infos['total']
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assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))
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sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
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print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
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for index, sub_dir in enumerate(sub_model_dirs):
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arch_info_files = sorted( list(sub_dir.glob('arch-*-seed-*.pth') ) )
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print ('The {:02d}/{:02d}-th directory : {:} : {:} runs.'.format(index, len(sub_model_dirs), sub_dir, len(arch_info_files)))
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arch2infos, evaluated_indexes = dict(), set()
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for IDX, sub_dir in enumerate(sub_model_dirs):
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ckp_path = sub_dir.parent / 'simplifies' / '{:}.pth'.format(sub_dir.name)
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if ckp_path.exists():
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sub_ckps = torch.load(ckp_path, map_location='cpu')
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assert sub_ckps['total_archs'] == meta_num_archs and sub_ckps['basestr'] == basestr
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xarch2infos = sub_ckps['arch2infos']
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xevalindexs = sub_ckps['evaluated_indexes']
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for eval_index in xevalindexs:
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assert eval_index not in evaluated_indexes and eval_index not in arch2infos
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#arch2infos[eval_index] = xarch2infos[eval_index].state_dict()
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arch2infos[eval_index] = {'full': xarch2infos[eval_index]['full'].state_dict(),
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'less': xarch2infos[eval_index]['less'].state_dict()}
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evaluated_indexes.add( eval_index )
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print ('{:} [{:03d}/{:03d}] merge data from {:} with {:} models.'.format(time_string(), IDX, len(sub_model_dirs), ckp_path, len(xevalindexs)))
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else:
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raise ValueError('Can not find {:}'.format(ckp_path))
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#print ('{:} [{:03d}/{:03d}] can not find {:}, skip.'.format(time_string(), IDX, len(subdir2archs), ckp_path))
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evaluated_indexes = sorted( list( evaluated_indexes ) )
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print ('Finally, there are {:} architectures that have been trained and evaluated.'.format(len(evaluated_indexes)))
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to_save_simply = save_dir / 'simplifies'
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if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
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final_infos = {'meta_archs' : meta_archs,
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'total_archs': meta_num_archs,
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'arch2infos' : arch2infos,
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'evaluated_indexes': evaluated_indexes}
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save_file_name = to_save_simply / '{:}-final-infos.pth'.format(basestr)
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torch.save(final_infos, save_file_name)
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print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
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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('--mode' , type=str, choices=['cal', 'merge'], help='The running mode for this script.')
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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.')
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parser.add_argument('--target_dir' , type=str, help='The target directory.')
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parser.add_argument('--max_node' , type=int, default=4, help='The maximum node in a cell.')
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parser.add_argument('--channel' , type=int, default=16, help='The number of channels.')
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parser.add_argument('--num_cells' , type=int, default=5, help='The number of cells in one stage.')
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args = parser.parse_args()
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save_dir = Path(args.base_save_dir)
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meta_path = save_dir / 'meta-node-{:}.pth'.format(args.max_node)
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assert save_dir.exists(), 'invalid save dir path : {:}'.format(save_dir)
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assert meta_path.exists(), 'invalid saved meta path : {:}'.format(meta_path)
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print ('start the statistics of our nas-benchmark from {:} using {:}.'.format(save_dir, args.target_dir))
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basestr = 'C{:}-N{:}'.format(args.channel, args.num_cells)
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if args.mode == 'cal':
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simplify(save_dir, meta_path, basestr, args.target_dir)
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elif args.mode == 'merge':
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merge_all(save_dir, meta_path, basestr)
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else:
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raise ValueError('invalid mode : {:}'.format(args.mode)) |