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This commit is contained in:
D-X-Y
2020-10-08 10:19:34 +11:00
parent ad5d6e28b9
commit ab801cbf14
7 changed files with 90 additions and 43 deletions
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8
exps/NATS-algos/search-size.py
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@@ -2,7 +2,13 @@
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
######################################################################################
# In this file, we aims to evaluate three kinds of channel searching strategies:
# -
# - channel-wise interpaltion from "Network Pruning via Transformable Architecture Search, NeurIPS 2019"
# - masking + Gumbel-Softmax from "FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions, CVPR 2020"
# - masking + sampling from "Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS, CVPR 2020"
# For simplicity, we use tas, fbv2, and tunas to refer these three strategies. Their official implementations are at the following links:
# - TAS: https://github.com/D-X-Y/AutoDL-Projects/blob/master/docs/NeurIPS-2019-TAS.md
# - FBV2: https://github.com/facebookresearch/mobile-vision
# - TuNAS: https://github.com/google-research/google-research/tree/master/tunas
####
# python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --warmup_ratio 0.25
####

21
exps/experimental/vis-nats-bench-ws.py
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@@ -26,7 +26,8 @@ from nats_bench import create
from log_utils import time_string
def fetch_data(root_dir='./output/search', search_space='tss', dataset=None, suffix='-AWD0.0-WARMNone'):
# def fetch_data(root_dir='./output/search', search_space='tss', dataset=None, suffix='-WARMNone'):
def fetch_data(root_dir='./output/search', search_space='tss', dataset=None, suffix='-WARM0.3'):
ss_dir = '{:}-{:}'.format(root_dir, search_space)
alg2name, alg2path = OrderedDict(), OrderedDict()
seeds = [777, 888, 999]
@@ -39,9 +40,12 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None, suf
alg2name['ENAS'] = 'enas-affine0_BN0-None'
alg2name['SETN'] = 'setn-affine0_BN0-None'
else:
alg2name['TAS'] = 'tas-affine0_BN0{:}'.format(suffix)
alg2name['FBNetV2'] = 'fbv2-affine0_BN0{:}'.format(suffix)
alg2name['TuNAS'] = 'tunas-affine0_BN0{:}'.format(suffix)
# alg2name['TAS'] = 'tas-affine0_BN0{:}'.format(suffix)
# alg2name['FBNetV2'] = 'fbv2-affine0_BN0{:}'.format(suffix)
# alg2name['TuNAS'] = 'tunas-affine0_BN0{:}'.format(suffix)
alg2name['channel-wise interpaltion'] = 'tas-affine0_BN0-AWD0.001{:}'.format(suffix)
alg2name['masking + Gumbel-Softmax'] = 'fbv2-affine0_BN0-AWD0.001{:}'.format(suffix)
alg2name['masking + sampling'] = 'tunas-affine0_BN0-AWD0.0{:}'.format(suffix)
for alg, name in alg2name.items():
alg2path[alg] = os.path.join(ss_dir, dataset, name, 'seed-{:}-last-info.pth')
alg2data = OrderedDict()
@@ -98,8 +102,11 @@ def visualize_curve(api, vis_save_dir, search_space):
for idx, (alg, data) in enumerate(alg2data.items()):
print('plot alg : {:}'.format(alg))
xs, accuracies = [], []
for iepoch in range(epochs+1):
structures, accs = [_[iepoch-1] for _ in data], []
for iepoch in range(epochs + 1):
try:
structures, accs = [_[iepoch-1] for _ in data], []
except:
raise ValueError('This alg {:} on {:} has invalid checkpoints.'.format(alg, dataset))
for structure in structures:
info = api.get_more_info(structure, dataset=dataset, hp=90 if api.search_space_name == 'size' else 200, is_random=False)
accs.append(info['test-accuracy'])
@@ -131,5 +138,5 @@ if __name__ == '__main__':
save_dir = Path(args.save_dir)
api = create(None, args.search_space, verbose=False)
api = create(None, args.search_space, fast_mode=True, verbose=False)
visualize_curve(api, save_dir, args.search_space)

12
lib/models/shape_searchs/generic_size_tiny_cell_model.py
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@@ -2,8 +2,8 @@
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
#####################################################
# Here, we utilized three techniques to search for the number of channels:
# - feature interpaltion from "Network Pruning via Transformable Architecture Search, NeurIPS 2019"
# - masking + GumbelSoftmax from "FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions, CVPR 2020"
# - channel-wise interpaltion from "Network Pruning via Transformable Architecture Search, NeurIPS 2019"
# - masking + Gumbel-Softmax from "FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions, CVPR 2020"
# - masking + sampling from "Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS, CVPR 2020"
from typing import List, Text, Any
import random, torch
@@ -55,10 +55,10 @@ class GenericNAS301Model(nn.Module):
assert algo in ['fbv2', 'tunas', 'tas'], 'invalid algo : {:}'.format(algo)
self._algo = algo
self._arch_parameters = nn.Parameter(1e-3*torch.randn(self._max_num_Cs, len(self._candidate_Cs)))
if algo == 'fbv2' or algo == 'tunas':
self.register_buffer('_masks', torch.zeros(len(self._candidate_Cs), max(self._candidate_Cs)))
for i in range(len(self._candidate_Cs)):
self._masks.data[i, :self._candidate_Cs[i]] = 1
# if algo == 'fbv2' or algo == 'tunas':
self.register_buffer('_masks', torch.zeros(len(self._candidate_Cs), max(self._candidate_Cs)))
for i in range(len(self._candidate_Cs)):
self._masks.data[i, :self._candidate_Cs[i]] = 1
@property
def tau(self):

13
lib/nats_bench/api_size.py
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@@ -7,7 +7,6 @@
# [2020.08.31] NATS-sss-v1_0-50262.pickle.pbz2 #
#####################################################################################
import os, copy, random, numpy as np
from pathlib import Path
from typing import List, Text, Union, Dict, Optional
from collections import OrderedDict, defaultdict
from .api_utils import time_string
@@ -15,6 +14,8 @@ from .api_utils import pickle_load
from .api_utils import ArchResults
from .api_utils import NASBenchMetaAPI
from .api_utils import remap_dataset_set_names
from .api_utils import nats_is_dir
from .api_utils import nats_is_file
from .api_utils import PICKLE_EXT
@@ -70,20 +71,20 @@ class NATSsize(NASBenchMetaAPI):
else:
file_path_or_dict = os.path.join(os.environ['TORCH_HOME'], '{:}.{:}'.format(ALL_BASE_NAMES[-1], PICKLE_EXT))
print ('{:} Try to use the default NATS-Bench (size) path from fast_mode={:} and path={:}.'.format(time_string(), self._fast_mode, file_path_or_dict))
if isinstance(file_path_or_dict, str) or isinstance(file_path_or_dict, Path):
if isinstance(file_path_or_dict, str):
file_path_or_dict = str(file_path_or_dict)
if verbose:
print('{:} Try to create the NATS-Bench (size) api from {:} with fast_mode={:}'.format(time_string(), file_path_or_dict, fast_mode))
if not os.path.isfile(file_path_or_dict) and not os.path.isdir(file_path_or_dict):
if not nats_is_file(file_path_or_dict) and not nats_is_dir(file_path_or_dict):
raise ValueError('{:} is neither a file or a dir.'.format(file_path_or_dict))
self.filename = Path(file_path_or_dict).name
self.filename = os.path.basename(file_path_or_dict)
if fast_mode:
if os.path.isfile(file_path_or_dict):
if nats_is_file(file_path_or_dict):
raise ValueError('fast_mode={:} must feed the path for directory : {:}'.format(fast_mode, file_path_or_dict))
else:
self._archive_dir = file_path_or_dict
else:
if os.path.isdir(file_path_or_dict):
if nats_is_dir(file_path_or_dict):
raise ValueError('fast_mode={:} must feed the path for file : {:}'.format(fast_mode, file_path_or_dict))
else:
file_path_or_dict = pickle_load(file_path_or_dict)

13
lib/nats_bench/api_topology.py
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@@ -7,7 +7,6 @@
# [2020.08.31] NATS-tss-v1_0-3ffb9.pickle.pbz2 #
#####################################################################################
import os, copy, random, numpy as np
from pathlib import Path
from typing import List, Text, Union, Dict, Optional
from collections import OrderedDict, defaultdict
import warnings
@@ -16,6 +15,8 @@ from .api_utils import pickle_load
from .api_utils import ArchResults
from .api_utils import NASBenchMetaAPI
from .api_utils import remap_dataset_set_names
from .api_utils import nats_is_dir
from .api_utils import nats_is_file
from .api_utils import PICKLE_EXT
@@ -67,20 +68,20 @@ class NATStopology(NASBenchMetaAPI):
else:
file_path_or_dict = os.path.join(os.environ['TORCH_HOME'], '{:}.{:}'.format(ALL_BASE_NAMES[-1], PICKLE_EXT))
print ('{:} Try to use the default NATS-Bench (topology) path from {:}.'.format(time_string(), file_path_or_dict))
if isinstance(file_path_or_dict, str) or isinstance(file_path_or_dict, Path):
if isinstance(file_path_or_dict, str):
file_path_or_dict = str(file_path_or_dict)
if verbose:
print('{:} Try to create the NATS-Bench (topology) api from {:} with fast_mode={:}'.format(time_string(), file_path_or_dict, fast_mode))
if not os.path.isfile(file_path_or_dict) and not os.path.isdir(file_path_or_dict):
if not nats_is_file(file_path_or_dict) and not nats_is_dir(file_path_or_dict):
raise ValueError('{:} is neither a file or a dir.'.format(file_path_or_dict))
self.filename = Path(file_path_or_dict).name
self.filename = os.path.basename(file_path_or_dict)
if fast_mode:
if os.path.isfile(file_path_or_dict):
if nats_is_file(file_path_or_dict):
raise ValueError('fast_mode={:} must feed the path for directory : {:}'.format(fast_mode, file_path_or_dict))
else:
self._archive_dir = file_path_or_dict
else:
if os.path.isdir(file_path_or_dict):
if nats_is_dir(file_path_or_dict):
raise ValueError('fast_mode={:} must feed the path for file : {:}'.format(fast_mode, file_path_or_dict))
else:
file_path_or_dict = pickle_load(file_path_or_dict)

37
lib/nats_bench/api_utils.py
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@@ -17,6 +17,7 @@ from typing import List, Text, Union, Dict, Optional
from collections import OrderedDict, defaultdict
_FILE_SYSTEM = 'default'
PICKLE_EXT = 'pickle.pbz2'
@@ -45,6 +46,34 @@ def time_string():
return string
def reset_file_system(lib: Text='default'):
_FILE_SYSTEM = lib
def get_file_system(lib: Text='default'):
return _FILE_SYSTEM
def nats_is_dir(file_path):
if _FILE_SYSTEM == 'default':
return os.path.isdir(file_path)
elif _FILE_SYSTEM == 'google':
import tensorflow as tf
return tf.gfile.isdir(file_path)
else:
raise ValueError('Unknown file system lib: {:}'.format(_FILE_SYSTEM))
def nats_is_file(file_path):
if _FILE_SYSTEM == 'default':
return os.path.isfile(file_path)
elif _FILE_SYSTEM == 'google':
import tensorflow as tf
return tf.gfile.exists(file_path) and not tf.gfile.isdir(file_path)
else:
raise ValueError('Unknown file system lib: {:}'.format(_FILE_SYSTEM))
def remap_dataset_set_names(dataset, metric_on_set, verbose=False):
"""re-map the metric_on_set to internal keys"""
if verbose:
@@ -146,10 +175,10 @@ class NASBenchMetaAPI(metaclass=abc.ABCMeta):
time_string(), archive_root, index))
if archive_root is None:
archive_root = os.path.join(os.environ['TORCH_HOME'], '{:}-full'.format(self.ALL_BASE_NAMES[-1]))
if not os.path.isdir(archive_root):
if not nats_is_dir(archive_root):
warnings.warn('The input archive_root is None and the default archive_root path ({:}) does not exist, try to use self.archive_dir.'.format(archive_root))
archive_root = self.archive_dir
if archive_root is None or not os.path.isdir(archive_root):
if archive_root is None or not nats_is_dir(archive_root):
raise ValueError('Invalid archive_root : {:}'.format(archive_root))
if index is None:
indexes = list(range(len(self)))
@@ -158,9 +187,9 @@ class NASBenchMetaAPI(metaclass=abc.ABCMeta):
for idx in indexes:
assert 0 <= idx < len(self.meta_archs), 'invalid index of {:}'.format(idx)
xfile_path = os.path.join(archive_root, '{:06d}.{:}'.format(idx, PICKLE_EXT))
if not os.path.isfile(xfile_path):
if not nats_is_file(xfile_path):
xfile_path = os.path.join(archive_root, '{:d}.{:}'.format(idx, PICKLE_EXT))
assert os.path.isfile(xfile_path), 'invalid data path : {:}'.format(xfile_path)
assert nats_is_file(xfile_path), 'invalid data path : {:}'.format(xfile_path)
xdata = pickle_load(xfile_path)
assert isinstance(xdata, dict), 'invalid format of data in {:}'.format(xfile_path)
self.evaluated_indexes.add(idx)

29
scripts-search/NATS/search-size.sh
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@@ -1,10 +1,10 @@
#!/bin/bash
# bash ./NATS/search-size.sh 0 777
# bash scripts-search/NATS/search-size.sh 0 0.3 777
echo script name: $0
echo $# arguments
if [ "$#" -ne 2 ] ;then
if [ "$#" -ne 3 ] ;then
echo "Input illegal number of parameters " $#
echo "Need 2 parameters for GPU-device and seed"
echo "Need 3 parameters for GPU-device, warmup-ratio, and seed"
exit 1
fi
if [ "$TORCH_HOME" = "" ]; then
@@ -15,16 +15,19 @@ else
fi
device=$1
seed=$2
ratio=$2
seed=$3
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tas --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo tas --warmup_ratio ${ratio} --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tas --warmup_ratio ${ratio} --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tas --warmup_ratio ${ratio} --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo fbv2 --rand_seed ${seed}
#
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo fbv2 --warmup_ratio ${ratio} --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo fbv2 --warmup_ratio ${ratio} --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo fbv2 --warmup_ratio ${ratio} --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tunas --arch_weight_decay 0 --rand_seed ${seed}
#
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --warmup_ratio ${ratio} --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --warmup_ratio ${ratio} --rand_seed ${seed}
CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tunas --arch_weight_decay 0 --warmup_ratio ${ratio} --rand_seed ${seed}