85 lines
3.7 KiB
Python
85 lines
3.7 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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###############################################################################################
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# Before run these commands, the files must be properly put.
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# python exps/NAS-Bench-201/test-weights.py --base_path $HOME/.torch/NAS-Bench-201-v1_0-e61699
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# python exps/NAS-Bench-201/test-weights.py --base_path $HOME/.torch/NAS-Bench-201-v1_1-096897
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###############################################################################################
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import os, sys, time, glob, random, argparse
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import numpy as np
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import torch
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import torch.nn as nn
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from pathlib import Path
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from tqdm import tqdm
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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 procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
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from nas_201_api import NASBench201API as API
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from log_utils import time_string
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from models import get_cell_based_tiny_net
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from utils import weight_watcher
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def get_cor(A, B):
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return float(np.corrcoef(A, B)[0,1])
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def evaluate(api, weight_dir, data: str, use_12epochs_result: bool, valid_or_test: bool):
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norms, accs = [], []
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for idx in tqdm(range(len(api))):
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info = api.get_more_info(idx, data, use_12epochs_result=use_12epochs_result, is_random=False)
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if valid_or_test:
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accs.append(info['valid-accuracy'])
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else:
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accs.append(info['test-accuracy'])
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config = api.get_net_config(idx, data)
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net = get_cell_based_tiny_net(config)
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api.reload(weight_dir, idx)
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params = api.get_net_param(idx, data, None)
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cur_norms = []
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for seed, param in params.items():
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net.load_state_dict(param)
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_, summary = weight_watcher.analyze(net, alphas=False)
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cur_norms.append( summary['lognorm'] )
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norms.append( float(np.mean(cur_norms)) )
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api.clear_params(idx, use_12epochs_result)
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correlation = get_cor(norms, accs)
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print('For {:} with {:} epochs on {:} : the correlation is {:}'.format(data, 12 if use_12epochs_result else 200, 'valid' if valid_or_test else 'test', correlation))
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def main(meta_file: str, weight_dir, save_dir):
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api = API(meta_file)
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datasets = ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']
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print(time_string() + ' ' + '='*50)
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for data in datasets:
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nums = api.statistics(data, True)
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total = sum([k*v for k, v in nums.items()])
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print('Using 012 epochs, trained on {:20s} : {:} trials in total ({:}).'.format(data, total, nums))
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print(time_string() + ' ' + '='*50)
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for data in datasets:
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nums = api.statistics(data, False)
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total = sum([k*v for k, v in nums.items()])
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print('Using 200 epochs, trained on {:20s} : {:} trials in total ({:}).'.format(data, total, nums))
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print(time_string() + ' ' + '='*50)
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evaluate(api, weight_dir, 'cifar10-valid', False, True)
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print('{:} finish this test.'.format(time_string()))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("Analysis of NAS-Bench-201")
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parser.add_argument('--save_dir', type=str, default='./output/search-cell-nas-bench-201/visuals', help='The base-name of folder to save checkpoints and log.')
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parser.add_argument('--base_path', type=str, default=None, help='The path to the NAS-Bench-201 benchmark file and weight dir.')
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args = parser.parse_args()
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save_dir = Path(args.save_dir)
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save_dir.mkdir(parents=True, exist_ok=True)
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meta_file = Path(args.base_path + '.pth')
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weight_dir = Path(args.base_path + '-archive')
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assert meta_file.exists(), 'invalid path for api : {:}'.format(meta_file)
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assert weight_dir.exists() and weight_dir.is_dir(), 'invalid path for weight dir : {:}'.format(weight_dir)
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main(str(meta_file), weight_dir, save_dir)
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