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66
BASELINE.md
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@ -40,39 +40,39 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k GDAS_
## Performance on ImageNet
| Model | FLOPs (GB) | Params (M) | Top-1 Error | Top-5 Error | Optimizer |
|:--------------:|:----------:|:----------:|:-----------:|:-----------:|:----------:|
| ResNet-18 | 1.814 | 11.69 | 30.24 | 10.92 | Official |
| ResNet-18 | 1.814 | 11.69 | 29.97 | 10.43 | Step-120 |
| ResNet-18 | 1.814 | 11.69 | 29.35 | 10.13 | Cosine-120 |
| ResNet-18 | 1.814 | 11.69 | 29.45 | 10.25 | Cosine-120 B1024 |
| ResNet-18 | 1.814 | 11.69 | 29.44 | 10.12 |Cosine-S-120|
| ResNet-18 (DS) | 2.053 | 11.71 | 28.53 | 9.69 |Cosine-S-120|
| ResNet-34 | 3.663 | 21.80 | 25.65 | 8.06 |Cosine-120 |
| ResNet-34 (DS) | 3.903 | 21.82 | 25.05 | 7.67 |Cosine-S-120|
| ResNet-50 | 4.089 | 25.56 | 23.85 | 7.13 | Official |
| ResNet-50 | 4.089 | 25.56 | 22.54 | 6.45 |Cosine-120 |
| ResNet-50 | 4.089 | 25.56 | 22.71 | 6.38 |Cosine-120 B1024 |
| ResNet-50 | 4.089 | 25.56 | 22.34 | 6.22 |Cosine-S-120|
| ResNet-50 (DS) | 4.328 | 25.58 | 22.67 | 6.39 | Step-120 |
| ResNet-50 (DS) | 4.328 | 25.58 | 21.94 | 6.23 | Cosine-120 |
| ResNet-50 (DS) | 4.328 | 25.58 | 21.71 | 5.99 |Cosine-S-120|
| ResNet-101 | 7.801 | 44.55 | 20.93 | 5.57 |Cosine-120 |
| ResNet-101 | 7.801 | 44.55 | 20.92 | 5.58 |Cosine-120 B1024 |
| ResNet-101 (DS)| 8.041 | 44.57 | 20.36 | 5.22 |Cosine-S-120|
| ResNet-152 | 11.514 | 60.19 | 20.10 | 5.17 |Cosine-120 B1024 |
| ResNet-152 (DS)| 11.753 | 60.21 | 19.83 | 5.02 |Cosine-S-120|
| ResNet-200 | 15.007 | 64.67 | 20.06 | 4.98 |Cosine-S-120|
| Next50-32x4d (DS)| 4.2 | 25.0 | 22.2 | - | Official |
| Next50-32x4d (DS)| 4.470 | 25.05 | 21.16 | 5.65 |Cosine-S-120|
| MobileNet-V2 | 0.300 | 3.40 | 28.0 | - | Official |
| MobileNet-V2 | 0.300 | 3.50 | 27.92 | 9.50 | MobileFast |
| MobileNet-V2 | 0.300 | 3.50 | 27.56 | 9.26 | MobileFast-Smooth |
| ShuffleNet-V2 1.0| 0.146 | 2.28 | 30.6 | 11.1 | Official |
| ShuffleNet-V2 1.0| 0.145 | 2.28 | | |Cosine-S-120|
| ShuffleNet-V2 1.5| 0.299 | | 27.4 | - | Official |
| ShuffleNet-V2 1.5| | | | |Cosine-S-120|
| ShuffleNet-V2 2.0| | | | |Cosine-S-120|
| Model | FLOPs (GB) | Params (M) | Top-1 Error | Top-5 Error | Optimizer |
|:-----------------:|:----------:|:----------:|:-----------:|:-----------:|:----------:|
| ResNet-18 | 1.814 | 11.69 | 30.24 | 10.92 | Official |
| ResNet-18 | 1.814 | 11.69 | 29.97 | 10.43 | Step-120 |
| ResNet-18 | 1.814 | 11.69 | 29.35 | 10.13 | Cosine-120 |
| ResNet-18 | 1.814 | 11.69 | 29.45 | 10.25 | Cosine-120 B1024 |
| ResNet-18 | 1.814 | 11.69 | 29.44 | 10.12 | Cosine-S-120 |
| ResNet-18 (DS) | 2.053 | 11.71 | 28.53 | 9.69 | Cosine-S-120 |
| ResNet-34 | 3.663 | 21.80 | 25.65 | 8.06 | Cosine-120 |
| ResNet-34 (DS) | 3.903 | 21.82 | 25.05 | 7.67 | Cosine-S-120 |
| ResNet-50 | 4.089 | 25.56 | 23.85 | 7.13 | Official |
| ResNet-50 | 4.089 | 25.56 | 22.54 | 6.45 | Cosine-120 |
| ResNet-50 | 4.089 | 25.56 | 22.71 | 6.38 | Cosine-120 B1024 |
| ResNet-50 | 4.089 | 25.56 | 22.34 | 6.22 | Cosine-S-120 |
| ResNet-50 (DS) | 4.328 | 25.58 | 22.67 | 6.39 | Step-120 |
| ResNet-50 (DS) | 4.328 | 25.58 | 21.94 | 6.23 | Cosine-120 |
| ResNet-50 (DS) | 4.328 | 25.58 | 21.71 | 5.99 | Cosine-S-120 |
| ResNet-101 | 7.801 | 44.55 | 20.93 | 5.57 | Cosine-120 |
| ResNet-101 | 7.801 | 44.55 | 20.92 | 5.58 | Cosine-120 B1024 |
| ResNet-101 (DS) | 8.041 | 44.57 | 20.36 | 5.22 | Cosine-S-120 |
| ResNet-152 | 11.514 | 60.19 | 20.10 | 5.17 | Cosine-120 B1024 |
| ResNet-152 (DS) | 11.753 | 60.21 | 19.83 | 5.02 | Cosine-S-120 |
| ResNet-200 | 15.007 | 64.67 | 20.06 | 4.98 | Cosine-S-120 |
| Next50-32x4d (DS) | 4.2 | 25.0 | 22.2 | - | Official |
| Next50-32x4d (DS) | 4.470 | 25.05 | 21.16 | 5.65 | Cosine-S-120 |
| MobileNet-V2 | 0.300 | 3.40 | 28.0 | - | Official |
| MobileNet-V2 | 0.300 | 3.50 | 27.92 | 9.50 | MobileFast |
| MobileNet-V2 | 0.300 | 3.50 | 27.56 | 9.26 | MobileFast-Smooth |
| ShuffleNet-V2 1.0 | 0.146 | 2.28 | 30.6 | 11.1 | Official |
| ShuffleNet-V2 1.0 | 0.145 | 2.28 | | | Cosine-S-120 |
| ShuffleNet-V2 1.5 | 0.299 | | 27.4 | - | Official |
| ShuffleNet-V2 1.5 | | | | | Cosine-S-120 |
| ShuffleNet-V2 2.0 | | | | | Cosine-S-120 |
`DS` indicates deep-stem for the first convolutional layer.
```

2
CONTRIBUTING.md
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@ -4,7 +4,7 @@
The following is a set of guidelines for contributing to NAS-Projects.
#### Table Of Contents
## Table Of Contents
[How Can I Contribute?](#how-can-i-contribute)
* [Reporting Bugs](#reporting-bugs)

10
NAS-Bench-102.md
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@ -6,9 +6,9 @@ Each edge here is associated with an operation selected from a predefined operat
For it to be applicable for all NAS algorithms, the search space defined in NAS-Bench-102 includes 4 nodes and 5 associated operation options, which generates 15,625 neural cell candidates in total.
In this Markdown file, we provide:
- [How to Use NAS-Bench-102](#how-to-use-nas-bench-102)
- [Instruction to re-generate NAS-Bench-102](#instruction-to-re-generate-nas-bench-102)
- [10 NAS algorithms evaluated in our paper](#to-reproduce-10-baseline-nas-algorithms-in-nas-bench-102)
- [How to Use NAS-Bench-102](#how-to-use-nas-bench-102)
- [Instruction to re-generate NAS-Bench-102](#instruction-to-re-generate-nas-bench-102)
- [10 NAS algorithms evaluated in our paper](#to-reproduce-10-baseline-nas-algorithms-in-nas-bench-102)
Note: please use `PyTorch >= 1.2.0` and `Python >= 3.6.0`.
@ -140,6 +140,8 @@ This command will train 390 architectures (id from 0 to 389) using the following
| CIFAR-100 | train | valid / test |
| ImageNet-16-120 | train | valid / test |
Note that the above `train`, `valid`, and `test` indicate the proposed splits in our NAS-Bench-102, and they might be different with the original splits.
3. calculate the latency, merge the results of all architectures, and simplify the results.
(see commands in `output/NAS-BENCH-102-4/meta-node-4.cal-script.txt` which is automatically generated by step-1).
```
@ -167,7 +169,7 @@ If researchers can provide better results with different hyper-parameters, we ar
**Note that** you need to prepare the training and test data as described in [Preparation and Download](#preparation-and-download)
- [1] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V1.sh cifar10 -1`
- [1] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V1.sh cifar10 -1`, where `cifar10` can be replaced with `cifar100` or `ImageNet16-120`.
- [2] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 -1`
- [3] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh cifar10 -1`
- [4] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/SETN.sh cifar10 -1`

7
exps/NAS-Bench-102/visualize.py
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@ -8,7 +8,6 @@ from tqdm import tqdm
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
from pathlib import Path
from collections import defaultdict
import matplotlib
@ -498,6 +497,8 @@ def show_nas_sharing_w(api, dataset, subset, vis_save_dir, file_name, y_lims, x_
def get_accs(xdata):
epochs, xresults = xdata['epoch'], []
metrics = api.arch2infos_full[ api.random() ].get_metrics(dataset, subset, None, False)
xresults.append( metrics['accuracy'] )
for iepoch in range(epochs):
genotype = xdata['genotypes'][iepoch]
index = api.query_index_by_arch(genotype)
@ -547,7 +548,6 @@ if __name__ == '__main__':
#visualize_relative_ranking(vis_save_dir)
api = API(args.api_path)
"""
for x_maxs in [50, 250]:
show_nas_sharing_w(api, 'cifar10-valid' , 'x-valid' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
show_nas_sharing_w(api, 'cifar10' , 'ori-test', vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
@ -555,11 +555,12 @@ if __name__ == '__main__':
show_nas_sharing_w(api, 'cifar100' , 'x-test' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
show_nas_sharing_w(api, 'ImageNet16-120', 'x-valid' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
show_nas_sharing_w(api, 'ImageNet16-120', 'x-test' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
just_show(api)
"""
just_show(api)
plot_results_nas(api, 'cifar10-valid' , 'x-valid' , vis_save_dir, 'nas-com.pdf', (85,95, 1))
plot_results_nas(api, 'cifar10' , 'ori-test', vis_save_dir, 'nas-com.pdf', (85,95, 1))
plot_results_nas(api, 'cifar100' , 'x-valid' , vis_save_dir, 'nas-com.pdf', (55,75, 3))
plot_results_nas(api, 'cifar100' , 'x-test' , vis_save_dir, 'nas-com.pdf', (55,75, 3))
plot_results_nas(api, 'ImageNet16-120', 'x-valid' , vis_save_dir, 'nas-com.pdf', (35,50, 3))
plot_results_nas(api, 'ImageNet16-120', 'x-test' , vis_save_dir, 'nas-com.pdf', (35,50, 3))
"""

1
exps/algos/BOHB.py
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@ -10,7 +10,6 @@ from copy import deepcopy
from pathlib import Path
import torch
import torch.nn as nn
from torch.distributions import Categorical
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import load_config, dict2config, configure2str

20
exps/algos/DARTS-V1.py
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@ -121,9 +121,19 @@ def main(xargs):
search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data , batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
elif xargs.dataset == 'cifar100':
raise ValueError('not support yet : {:}'.format(xargs.dataset))
elif xargs.dataset.startswith('ImageNet16'):
raise ValueError('not support yet : {:}'.format(xargs.dataset))
cifar100_test_split = load_config('configs/nas-benchmark/cifar100-test-split.txt', None, None)
search_train_data = train_data
search_valid_data = deepcopy(valid_data) ; search_valid_data.transform = train_data.transform
search_data = SearchDataset(xargs.dataset, [search_train_data,search_valid_data], list(range(len(search_train_data))), cifar100_test_split.xvalid)
search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data , batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_test_split.xvalid), num_workers=xargs.workers, pin_memory=True)
elif xargs.dataset == 'ImageNet16-120':
imagenet_test_split = load_config('configs/nas-benchmark/imagenet-16-120-test-split.txt', None, None)
search_train_data = train_data
search_valid_data = deepcopy(valid_data) ; search_valid_data.transform = train_data.transform
search_data = SearchDataset(xargs.dataset, [search_train_data,search_valid_data], list(range(len(search_train_data))), imagenet_test_split.xvalid)
search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data , batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_test_split.xvalid), num_workers=xargs.workers, pin_memory=True)
else:
raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
@ -168,7 +178,7 @@ def main(xargs):
logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {}
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {-1: search_model.genotype()}
# start training
start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup
@ -230,7 +240,7 @@ if __name__ == '__main__':
parser.add_argument('--data_path', type=str, help='Path to dataset')
parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
# channels and number-of-cells
parser.add_argument('--config_path', type=str, help='The config paths.')
parser.add_argument('--config_path', type=str, help='The config path.')
parser.add_argument('--search_space_name', type=str, help='The search space name.')
parser.add_argument('--max_nodes', type=int, help='The maximum number of nodes.')
parser.add_argument('--channel', type=int, help='The number of channels.')

7
exps/algos/DARTS-V2.py
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@ -181,8 +181,8 @@ def main(xargs):
logger.log('Load split file from {:}'.format(split_Fpath))
else:
raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
config_path = 'configs/nas-benchmark/algos/DARTS.config'
config = load_config(config_path, {'class_num': class_num, 'xshape': xshape}, logger)
#config_path = 'configs/nas-benchmark/algos/DARTS.config'
config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
# To split data
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
@ -233,7 +233,7 @@ def main(xargs):
logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {}
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {-1: search_model.genotype()}
# start training
start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup
@ -297,6 +297,7 @@ if __name__ == '__main__':
parser.add_argument('--data_path', type=str, help='Path to dataset')
parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
# channels and number-of-cells
parser.add_argument('--config_path', type=str, help='The config path.')
parser.add_argument('--search_space_name', type=str, help='The search space name.')
parser.add_argument('--max_nodes', type=int, help='The maximum number of nodes.')
parser.add_argument('--channel', type=int, help='The number of channels.')

4
exps/algos/GDAS.py
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@ -3,7 +3,7 @@
###########################################################################
# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
###########################################################################
import os, sys, time, glob, random, argparse
import os, sys, time, random, argparse
import numpy as np
from copy import deepcopy
import torch
@ -11,7 +11,7 @@ import torch.nn as nn
from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import load_config, dict2config, configure2str
from config_utils import load_config, dict2config
from datasets import get_datasets, SearchDataset
from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
from utils import get_model_infos, obtain_accuracy

37
exps/vis/test.py
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@ -1,12 +1,14 @@
# python ./exps/vis/test.py
import os, sys, random
from pathlib import Path
from copy import deepcopy
import torch
import numpy as np
from collections import OrderedDict
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from nas_102_api import NASBench102API as API
def test_nas_api():
from nas_102_api import ArchResults
@ -72,7 +74,40 @@ def test_auto_grad():
s_grads = torch.autograd.grad(grads, net.parameters())
second_order_grads.append( s_grads )
def test_one_shot_model(ckpath, use_train):
from models import get_cell_based_tiny_net, get_search_spaces
from datasets import get_datasets, SearchDataset
from config_utils import load_config, dict2config
from utils.nas_utils import evaluate_one_shot
use_train = int(use_train) > 0
#ckpath = 'output/search-cell-nas-bench-102/DARTS-V1-cifar10/checkpoint/seed-11416-basic.pth'
#ckpath = 'output/search-cell-nas-bench-102/DARTS-V1-cifar10/checkpoint/seed-28640-basic.pth'
print ('ckpath : {:}'.format(ckpath))
ckp = torch.load(ckpath)
xargs = ckp['args']
train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, None)
if xargs.dataset == 'cifar10':
cifar_split = load_config('configs/nas-benchmark/cifar-split.txt', None, None)
xvalid_data = deepcopy(train_data)
xvalid_data.transform = valid_data.transform
valid_loader= torch.utils.data.DataLoader(xvalid_data, batch_size=2048, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar_split.valid), num_workers=12, pin_memory=True)
else: raise ValueError('invalid dataset : {:}'.format(xargs.dataseet))
search_space = get_search_spaces('cell', xargs.search_space_name)
model_config = dict2config({'name': 'SETN', 'C': xargs.channel, 'N': xargs.num_cells,
'max_nodes': xargs.max_nodes, 'num_classes': class_num,
'space' : search_space,
'affine' : False, 'track_running_stats': True}, None)
search_model = get_cell_based_tiny_net(model_config)
search_model.load_state_dict( ckp['search_model'] )
search_model = search_model.cuda()
api = API('/home/dxy/.torch/NAS-Bench-102-v1_0-e61699.pth')
archs, probs, accuracies = evaluate_one_shot(search_model, valid_loader, api, use_train)
if __name__ == '__main__':
#test_nas_api()
#for i in range(200): plot('{:04d}'.format(i))
test_auto_grad()
#test_auto_grad()
test_one_shot_model(sys.argv[1], sys.argv[2])

32
lib/datasets/SearchDatasetWrap.py
View File

@ -9,16 +9,25 @@ class SearchDataset(data.Dataset):
def __init__(self, name, data, train_split, valid_split, check=True):
self.datasetname = name
self.data = data
self.train_split = train_split.copy()
self.valid_split = valid_split.copy()
if check:
intersection = set(train_split).intersection(set(valid_split))
assert len(intersection) == 0, 'the splitted train and validation sets should have no intersection'
if isinstance(data, (list, tuple)): # new type of SearchDataset
assert len(data) == 2, 'invalid length: {:}'.format( len(data) )
self.train_data = data[0]
self.valid_data = data[1]
self.train_split = train_split.copy()
self.valid_split = valid_split.copy()
self.mode_str = 'V2' # new mode
else:
self.mode_str = 'V1' # old mode
self.data = data
self.train_split = train_split.copy()
self.valid_split = valid_split.copy()
if check:
intersection = set(train_split).intersection(set(valid_split))
assert len(intersection) == 0, 'the splitted train and validation sets should have no intersection'
self.length = len(self.train_split)
def __repr__(self):
return ('{name}(name={datasetname}, train={tr_L}, valid={val_L})'.format(name=self.__class__.__name__, datasetname=self.datasetname, tr_L=len(self.train_split), val_L=len(self.valid_split)))
return ('{name}(name={datasetname}, train={tr_L}, valid={val_L}, version={ver})'.format(name=self.__class__.__name__, datasetname=self.datasetname, tr_L=len(self.train_split), val_L=len(self.valid_split), ver=self.mode_str))
def __len__(self):
return self.length
@ -27,6 +36,11 @@ class SearchDataset(data.Dataset):
assert index >= 0 and index < self.length, 'invalid index = {:}'.format(index)
train_index = self.train_split[index]
valid_index = random.choice( self.valid_split )
train_image, train_label = self.data[train_index]
valid_image, valid_label = self.data[valid_index]
if self.mode_str == 'V1':
train_image, train_label = self.data[train_index]
valid_image, valid_label = self.data[valid_index]
elif self.mode_str == 'V2':
train_image, train_label = self.train_data[train_index]
valid_image, valid_label = self.valid_data[valid_index]
else: raise ValueError('invalid mode : {:}'.format(self.mode_str))
return train_image, train_label, valid_image, valid_label

2
lib/datasets/landmark_utils/point_meta.py
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@ -34,7 +34,7 @@ class PointMeta():
def get_box(self, return_diagonal=False):
if self.box is None: return None
if return_diagonal == False:
if not return_diagonal:
return self.box.clone()
else:
W = (self.box[2]-self.box[0]).item()

1
lib/models/cell_infers/cells.py
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@ -1,4 +1,3 @@
import torch
import torch.nn as nn
from copy import deepcopy
from ..cell_operations import OPS

2
lib/models/cell_searchs/genotypes.py
View File

@ -68,7 +68,7 @@ class Structure:
for i, node_info in enumerate(self.nodes):
sums = []
for op, xin in node_info:
if op == 'none' or nodes[xin] == False: x = False
if op == 'none' or nodes[xin] is False: x = False
else: x = True
sums.append( x )
nodes[i+1] = sum(sums) > 0

2
lib/models/cell_searchs/search_cells.py
View File

@ -85,7 +85,7 @@ class SearchCell(nn.Module):
candidates = self.edges[node_str]
select_op = random.choice(candidates)
sops.append( select_op )
if not hasattr(select_op, 'is_zero') or select_op.is_zero == False: has_non_zero=True
if not hasattr(select_op, 'is_zero') or select_op.is_zero is False: has_non_zero=True
if has_non_zero: break
inter_nodes = []
for j, select_op in enumerate(sops):

2
lib/models/shape_infers/InferCifarResNet_width.py
View File

@ -1,4 +1,4 @@
import math, torch
import math
import torch.nn as nn
import torch.nn.functional as F
from ..initialization import initialize_resnet

3
lib/nas_102_api/api.py
View File

@ -70,6 +70,9 @@ class NASBench102API(object):
def __repr__(self):
return ('{name}({num}/{total} architectures)'.format(name=self.__class__.__name__, num=len(self.evaluated_indexes), total=len(self.meta_archs)))
def random(self):
return random.randint(0, len(self.meta_archs)-1)
def query_index_by_arch(self, arch):
if isinstance(arch, str):
if arch in self.archstr2index: arch_index = self.archstr2index[ arch ]

2
lib/procedures/starts.py
View File

@ -1,7 +1,7 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, time, torch, random, PIL, copy, numpy as np
import os, sys, torch, random, PIL, copy, numpy as np
from os import path as osp
from shutil import copyfile

2
lib/utils/__init__.py
View File

@ -1,3 +1,5 @@
from .evaluation_utils import obtain_accuracy
from .gpu_manager import GPUManager
from .flop_benchmark import get_model_infos
from .affine_utils import normalize_points, denormalize_points
from .affine_utils import identity2affine, solve2theta, affine2image

7
lib/xvision/affine_utils.py → lib/utils/affine_utils.py
View File

@ -1,10 +1,3 @@
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
#
# functions for affine transformation
import math, torch
import numpy as np

2
lib/utils/flop_benchmark.py
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@ -1,4 +1,4 @@
import copy, torch
import torch
import torch.nn as nn
import numpy as np

2
lib/utils/gpu_manager.py
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@ -27,7 +27,7 @@ class GPUManager():
find = False
for gpu in all_gpus:
if gpu['index'] == CUDA_VISIBLE_DEVICE:
assert find==False, 'Duplicate cuda device index : {}'.format(CUDA_VISIBLE_DEVICE)
assert not find, 'Duplicate cuda device index : {}'.format(CUDA_VISIBLE_DEVICE)
find = True
selected_gpus.append( gpu.copy() )
selected_gpus[-1]['index'] = '{}'.format(idx)

52
lib/utils/nas_utils.py Normal file
View File

@ -0,0 +1,52 @@
# This file is for experimental usage
import os, sys, torch, random
import numpy as np
from copy import deepcopy
from tqdm import tqdm
import torch.nn as nn
from utils import obtain_accuracy
from models import CellStructure
from log_utils import time_string
def evaluate_one_shot(model, xloader, api, cal_mode, seed=111):
weights = deepcopy(model.state_dict())
model.train(cal_mode)
with torch.no_grad():
logits = nn.functional.log_softmax(model.arch_parameters, dim=-1)
archs = CellStructure.gen_all(model.op_names, model.max_nodes, False)
probs, accuracies, gt_accs = [], [], []
loader_iter = iter(xloader)
random.seed(seed)
random.shuffle(archs)
for idx, arch in enumerate(archs):
arch_index = api.query_index_by_arch( arch )
metrics = api.get_more_info(arch_index, 'cifar10-valid', None, False, False)
gt_accs.append( metrics['valid-accuracy'] )
select_logits = []
for i, node_info in enumerate(arch.nodes):
for op, xin in node_info:
node_str = '{:}<-{:}'.format(i+1, xin)
op_index = model.op_names.index(op)
select_logits.append( logits[model.edge2index[node_str], op_index] )
cur_prob = sum(select_logits).item()
probs.append( cur_prob )
cor_prob = np.corrcoef(probs, gt_accs)[0,1]
print ('correlation for probabilities : {:}'.format(cor_prob))
for idx, arch in enumerate(archs):
model.set_cal_mode('dynamic', arch)
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
_, logits = model(inputs.cuda())
_, preds = torch.max(logits, dim=-1)
correct = (preds == targets.cuda() ).float()
accuracies.append( correct.mean().item() )
if idx != 0 and (idx % 300 == 0 or idx + 1 == len(archs) or idx == 10):
cor_accs = np.corrcoef(accuracies, gt_accs[:idx+1])[0,1]
print ('{:} {:03d}/{:03d} mode={:5s}, correlation : accs={:.4f}, arch={:}'.format(time_string(), idx, len(archs), 'Train' if cal_mode else 'Eval', cor_accs, arch))
model.load_state_dict(weights)
return archs, probs, accuracies

1
lib/xvision/__init__.py
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@ -1 +0,0 @@
from .affine_utils import normalize_points, denormalize_points

1
scripts-search/algos/DARTS-V2.sh
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@ -33,6 +33,7 @@ OMP_NUM_THREADS=4 python ./exps/algos/DARTS-V2.py \
--save_dir ${save_dir} --max_nodes ${max_nodes} --channel ${channel} --num_cells ${num_cells} \
--dataset ${dataset} --data_path ${data_path} \
--search_space_name ${space} \
--config_path configs/nas-benchmark/algos/DARTS.config \
--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
--arch_learning_rate 0.0003 --arch_weight_decay 0.001 \
--workers 4 --print_freq 200 --rand_seed ${seed}

2
scripts/prepare.sh
View File

@ -1,6 +1,6 @@
#!/bin/bash
# bash ./scripts/prepare.sh
datasets='cifar10 cifar100 imagenet-1k'
#datasets='cifar10 cifar100 imagenet-1k'
#ratios='0.5 0.8 0.9'
ratios='0.5'
save_dir=./.latent-data/splits

4
scripts/tas-infer-train.sh
View File

@ -33,7 +33,7 @@ OMP_NUM_THREADS=4 python ./exps/basic-main.py --dataset ${dataset} \
--procedure basic \
--save_dir ${xsave_dir} \
--cutout_length -1 \
--batch_size 256 --rand_seed ${rseed} --workers 6 \
--batch_size ${batch} --rand_seed ${rseed} --workers 6 \
--eval_frequency 1 --print_freq 100 --print_freq_eval 200
# KD training
@ -47,5 +47,5 @@ OMP_NUM_THREADS=4 python ./exps/KD-main.py --dataset ${dataset} \
--save_dir ${xsave_dir} \
--KD_alpha 0.9 --KD_temperature 4 \
--cutout_length -1 \
--batch_size 256 --rand_seed ${rseed} --workers 6 \
--batch_size ${batch} --rand_seed ${rseed} --workers 6 \
--eval_frequency 1 --print_freq 100 --print_freq_eval 200