Update weight watcher codes
This commit is contained in:
D-X-Y
parent
9659f132be
commit
6facc39a42
@ -46,7 +46,8 @@ It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default).
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```
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from nas_201_api import NASBench201API as API
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api = API('$path_to_meta_nas_bench_file')
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api = API('NAS-Bench-201-v1_1-096897.pth')
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# Create an API without the verbose log
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api = API('NAS-Bench-201-v1_1-096897.pth', verbose=False)
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# The default path for benchmark file is '{:}/{:}'.format(os.environ['TORCH_HOME'], 'NAS-Bench-201-v1_1-096897.pth')
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api = API(None)
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```
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@ -90,9 +90,9 @@ def visualize_sss_info(api, dataset, vis_save_dir):
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print ('Do not find cache file : {:}'.format(cache_file_path))
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params, flops, train_accs, valid_accs, test_accs = [], [], [], [], []
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for index in range(len(api)):
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info = api.get_cost_info(index, dataset)
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params.append(info['params'])
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flops.append(info['flops'])
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cost_info = api.get_cost_info(index, dataset, hp='90')
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params.append(cost_info['params'])
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flops.append(cost_info['flops'])
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# accuracy
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info = api.get_more_info(index, dataset, hp='90', is_random=False)
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train_accs.append(info['train-accuracy'])
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@ -178,9 +178,9 @@ def visualize_tss_info(api, dataset, vis_save_dir):
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print ('Do not find cache file : {:}'.format(cache_file_path))
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params, flops, train_accs, valid_accs, test_accs = [], [], [], [], []
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for index in range(len(api)):
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info = api.get_cost_info(index, dataset)
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params.append(info['params'])
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flops.append(info['flops'])
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cost_info = api.get_cost_info(index, dataset, hp='12')
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params.append(cost_info['params'])
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flops.append(cost_info['flops'])
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# accuracy
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info = api.get_more_info(index, dataset, hp='200', is_random=False)
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train_accs.append(info['train-accuracy'])
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@ -190,6 +190,7 @@ def visualize_tss_info(api, dataset, vis_save_dir):
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valid_accs.append(info['valid-accuracy'])
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else:
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valid_accs.append(info['valid-accuracy'])
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print('')
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info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_accs}
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torch.save(info, cache_file_path)
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else:
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@ -1,113 +0,0 @@
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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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# 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 --dataset cifar10-valid --use_12 1 --use_valid 1
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# bash ./scripts-search/NAS-Bench-201/test-weights.sh cifar10-valid 1
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###############################################################################################
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import os, gc, sys, math, argparse, psutil
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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 OrderedDict
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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 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 tostr(accdict, norms):
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xstr = []
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for key, accs in accdict.items():
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cor = get_cor(accs, norms)
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xstr.append('{:}: {:.3f}'.format(key, cor))
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return ' '.join(xstr)
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def evaluate(api, weight_dir, data: str, use_12epochs_result: bool):
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print('\nEvaluate dataset={:}'.format(data))
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norms, process = [], psutil.Process(os.getpid())
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final_val_accs = OrderedDict({'cifar10': [], 'cifar100': [], 'ImageNet16-120': []})
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final_test_accs = OrderedDict({'cifar10': [], 'cifar100': [], 'ImageNet16-120': []})
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for idx in 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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for key in ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']:
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info = api.get_more_info(idx, key, use_12epochs_result=False, is_random=False)
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if key == 'cifar10-valid':
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final_val_accs['cifar10'].append(info['valid-accuracy'])
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elif key == 'cifar10':
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final_test_accs['cifar10'].append(info['test-accuracy'])
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else:
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final_test_accs[key].append(info['test-accuracy'])
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final_val_accs[key].append(info['valid-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, use_12epochs_result=use_12epochs_result)
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cur_norms = []
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for seed, param in params.items():
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with torch.no_grad():
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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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cur_norm = float(np.mean(cur_norms))
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if math.isnan(cur_norm):
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print (' IGNORE {:} due to nan.'.format(idx))
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continue
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norms.append(cur_norm)
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api.clear_params(idx, None)
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if idx % 200 == 199 or idx + 1 == len(api):
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head = '{:05d}/{:05d}'.format(idx, len(api))
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stem_val = tostr(final_val_accs, norms)
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stem_test = tostr(final_test_accs, norms)
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print('{:} {:} {:} with {:} epochs ({:.2f} MB memory)'.format(time_string(), head, data, 12 if use_12epochs_result else 200, process.memory_info().rss / 1e6))
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print(' [Valid] -->> {:}'.format(stem_val))
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print(' [Test.] -->> {:}'.format(stem_test))
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gc.collect()
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def main(meta_file: str, weight_dir, save_dir, xdata, use_12epochs_result):
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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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evaluate(api, weight_dir, xdata, use_12epochs_result)
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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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parser.add_argument('--dataset' , type=str, default=None, help='.')
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parser.add_argument('--use_12' , type=int, default=None, help='.')
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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, args.dataset, bool(args.use_12))
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@ -1,20 +0,0 @@
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#
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# exps/experimental/test-api.py
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#
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import sys, time, random, argparse
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from copy import deepcopy
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import torchvision.models as models
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from pathlib import Path
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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 nas_201_api import NASBench201API as API
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def main():
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api = API(None)
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info = api.get_more_info(100, 'cifar100', 199, False, True)
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if __name__ == '__main__':
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main()
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151
exps/experimental/test-ww-bench.py
Normal file
151
exps/experimental/test-ww-bench.py
Normal file
@ -0,0 +1,151 @@
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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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# Before run these commands, the files must be properly put.
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#
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# python exps/experimental/test-ww-bench.py --base_path $HOME/.torch/NAS-Bench-201-v1_0-e61699
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# python exps/experimental/test-ww-bench.py --base_path $HOME/.torch/NAS-Bench-201-v1_1-096897 --dataset cifar10-valid --use_12 1 --use_valid 1
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# CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=4 python exps/experimental/test-ww-bench.py --base_path $HOME/.torch/NAS-Bench-201-v1_1-096897 --dataset cifar10
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# CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=4 python exps/experimental/test-ww-bench.py --search_space sss --base_path $HOME/.torch/NAS-Bench-301-v1_0 --dataset cifar10
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# CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=4 python exps/experimental/test-ww-bench.py --search_space sss --base_path $HOME/.torch/NAS-Bench-301-v1_0 --dataset cifar100
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# CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=4 python exps/experimental/test-ww-bench.py --search_space sss --base_path $HOME/.torch/NAS-Bench-301-v1_0 --dataset ImageNet16-120
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###########################################################################################################################################################
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import os, gc, sys, math, argparse, psutil
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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 OrderedDict
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import matplotlib
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import seaborn as sns
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matplotlib.use('agg')
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import matplotlib.pyplot as plt
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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 nas_201_api import NASBench201API, NASBench301API
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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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"""
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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 tostr(accdict, norms):
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xstr = []
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for key, accs in accdict.items():
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cor = get_cor(accs, norms)
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xstr.append('{:}: {:.3f}'.format(key, cor))
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return ' '.join(xstr)
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"""
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def evaluate(api, weight_dir, data: str):
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print('\nEvaluate dataset={:}'.format(data))
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process = psutil.Process(os.getpid())
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norms, accuracies = [], []
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ok, total = 0, 5000
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for idx in range(total):
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arch_index = api.random()
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api.reload(weight_dir, arch_index)
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# compute the weight watcher results
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config = api.get_net_config(arch_index, data)
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net = get_cell_based_tiny_net(config)
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meta_info = api.query_meta_info_by_index(arch_index, hp='200' if isinstance(api, NASBench201API) else '90')
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params = meta_info.get_net_param(data, 777)
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with torch.no_grad():
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net.load_state_dict(params)
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_, summary = weight_watcher.analyze(net, alphas=False)
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if 'lognorm' not in summary:
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api.clear_params(arch_index, None)
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del net ; continue
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continue
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cur_norm = -summary['lognorm']
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api.clear_params(arch_index, None)
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if math.isnan(cur_norm):
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del net, meta_info
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continue
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else:
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ok += 1
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norms.append(cur_norm)
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# query the accuracy
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info = meta_info.get_metrics(data, 'ori-test', iepoch=None, is_random=777)
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accuracies.append(info['accuracy'])
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del net, meta_info
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# print the information
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if idx % 20 == 0:
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gc.collect()
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print('{:} {:04d}_{:04d}/{:04d} ({:.2f} MB memory)'.format(time_string(), ok, idx, total, process.memory_info().rss / 1e6))
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return norms, accuracies
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def main(search_space, meta_file: str, weight_dir, save_dir, xdata):
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API = NASBench201API if search_space == 'tss' else NASBench301API
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save_dir.mkdir(parents=True, exist_ok=True)
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api = API(meta_file, verbose=False)
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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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hps = api.avaliable_hps
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for hp in hps:
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nums = api.statistics(data, hp=hp)
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total = sum([k*v for k, v in nums.items()])
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print('Using {:3s} epochs, trained on {:20s} : {:} trials in total ({:}).'.format(hp, data, total, nums))
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print(time_string() + ' ' + '='*50)
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norms, accuracies = evaluate(api, weight_dir, xdata)
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indexes = list(range(len(norms)))
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norm_indexes = sorted(indexes, key=lambda i: norms[i])
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accy_indexes = sorted(indexes, key=lambda i: accuracies[i])
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labels = []
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for index in norm_indexes:
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labels.append(accy_indexes.index(index))
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dpi, width, height = 200, 1400, 800
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 18, 12
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resnet_scale, resnet_alpha = 120, 0.5
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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xlim(min(indexes), max(indexes))
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plt.ylim(min(indexes), max(indexes))
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# plt.ylabel('y').set_rotation(30)
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plt.yticks(np.arange(min(indexes), max(indexes), max(indexes)//3), fontsize=LegendFontsize, rotation='vertical')
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plt.xticks(np.arange(min(indexes), max(indexes), max(indexes)//5), fontsize=LegendFontsize)
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ax.scatter(indexes, labels , marker='*', s=0.5, c='tab:red' , alpha=0.8)
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ax.scatter(indexes, indexes, marker='o', s=0.5, c='tab:blue' , alpha=0.8)
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ax.scatter([-1], [-1], marker='o', s=100, c='tab:blue' , label='Test accuracy')
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ax.scatter([-1], [-1], marker='*', s=100, c='tab:red' , label='Weight watcher')
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plt.grid(zorder=0)
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ax.set_axisbelow(True)
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plt.legend(loc=0, fontsize=LegendFontsize)
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ax.set_xlabel('architecture ranking sorted by the test accuracy ', fontsize=LabelSize)
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ax.set_ylabel('architecture ranking computed by weight watcher', fontsize=LabelSize)
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save_path = (save_dir / '{:}-{:}-test-ww.pdf'.format(search_space, xdata)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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save_path = (save_dir / '{:}-{:}-test-ww.png'.format(search_space, xdata)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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print ('{:} save into {:}'.format(time_string(), save_path))
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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/vis-nas-bench/', help='The base-name of folder to save checkpoints and log.')
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parser.add_argument('--search_space', type=str, default=None, choices=['tss', 'sss'], help='The search space.')
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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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parser.add_argument('--dataset' , type=str, default=None, help='.')
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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(args.search_space, str(meta_file), weight_dir, save_dir, args.dataset)
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@ -77,6 +77,7 @@ class NASBench201API(NASBenchMetaAPI):
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self.meta_archs = copy.deepcopy( file_path_or_dict['meta_archs'] )
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# This is a dict mapping each architecture to a dict, where the key is #epochs and the value is ArchResults
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self.arch2infos_dict = OrderedDict()
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self._avaliable_hps = set(['12', '200'])
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for xkey in sorted(list(file_path_or_dict['arch2infos'].keys())):
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all_info = file_path_or_dict['arch2infos'][xkey]
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hp2archres = OrderedDict()
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@ -75,11 +75,13 @@ class NASBench301API(NASBenchMetaAPI):
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self.meta_archs = copy.deepcopy( file_path_or_dict['meta_archs'] )
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# This is a dict mapping each architecture to a dict, where the key is #epochs and the value is ArchResults
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self.arch2infos_dict = OrderedDict()
|
||||
self._avaliable_hps = set()
|
||||
for xkey in sorted(list(file_path_or_dict['arch2infos'].keys())):
|
||||
all_infos = file_path_or_dict['arch2infos'][xkey]
|
||||
hp2archres = OrderedDict()
|
||||
for hp_key, results in all_infos.items():
|
||||
hp2archres[hp_key] = ArchResults.create_from_state_dict(results)
|
||||
self._avaliable_hps.add(hp_key) # save the avaliable hyper-parameter
|
||||
self.arch2infos_dict[xkey] = hp2archres
|
||||
self.evaluated_indexes = sorted(list(file_path_or_dict['evaluated_indexes']))
|
||||
self.archstr2index = {}
|
||||
|
||||
@ -57,6 +57,10 @@ class NASBenchMetaAPI(metaclass=abc.ABCMeta):
|
||||
def __repr__(self):
|
||||
return ('{name}({num}/{total} architectures, file={filename})'.format(name=self.__class__.__name__, num=len(self.evaluated_indexes), total=len(self.meta_archs), filename=self.filename))
|
||||
|
||||
@property
|
||||
def avaliable_hps(self):
|
||||
return list(copy.deepcopy(self._avaliable_hps))
|
||||
|
||||
def random(self):
|
||||
"""Return a random index of all architectures."""
|
||||
return random.randint(0, len(self.meta_archs)-1)
|
||||
|
||||
@ -1,21 +0,0 @@
|
||||
#!/bin/bash
|
||||
# bash ./scripts-search/NAS-Bench-201/test-weights.sh cifar10-valid 1
|
||||
echo script name: $0
|
||||
echo $# arguments
|
||||
if [ "$#" -ne 2 ] ;then
|
||||
echo "Input illegal number of parameters " $#
|
||||
echo "Need 2 parameters for dataset and use_12_epoch"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ "$TORCH_HOME" = "" ]; then
|
||||
echo "Must set TORCH_HOME envoriment variable for data dir saving"
|
||||
exit 1
|
||||
else
|
||||
echo "TORCH_HOME : $TORCH_HOME"
|
||||
fi
|
||||
|
||||
CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=4 python exps/NAS-Bench-201/test-weights.py \
|
||||
--base_path $HOME/.torch/NAS-Bench-201-v1_1-096897 \
|
||||
--dataset $1 \
|
||||
--use_12 $2
|
||||
Loading…
Reference in New Issue
Block a user