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rm PD ; update NAS-Bench-102 baselines

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D-X-Y 2019-12-23 11:19:09 +11:00
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2
.gitignore vendored
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@ -118,3 +118,5 @@ cx.sh
NAS-Bench-102-v1_0.pth NAS-Bench-102-v1_0.pth
lib/NAS-Bench-102-v1_0.pth lib/NAS-Bench-102-v1_0.pth
others/TF
scripts-search/l2s-algos

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NAS-Bench-102.md
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@ -14,11 +14,11 @@ Note: please use `PyTorch >= 1.2.0` and `Python >= 3.6.0`.
### Preparation and Download ### Preparation and Download
The benchmark file of NAS-Bench-102 can be downloaded from [Google Drive](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) or [Baidu-Wangpan]. The benchmark file of NAS-Bench-102 can be downloaded from [Google Drive](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) or [Baidu-Wangpan (code:6u5d)](https://pan.baidu.com/s/1CiaNH6C12zuZf7q-Ilm09w).
You can move it to anywhere you want and send its path to our API for initialization. You can move it to anywhere you want and send its path to our API for initialization.
- v1.0: `NAS-Bench-102-v1_0-e61699.pth`, where `e61699` is the last six digits for this file. - v1.0: `NAS-Bench-102-v1_0-e61699.pth`, where `e61699` is the last six digits for this file.
The training and evaluation data used in NAS-Bench-102 can be downloaded from [Google Drive](https://drive.google.com/open?id=1L0Lzq8rWpZLPfiQGd6QR8q5xLV88emU7) or [Baidu-Wangpan]. The training and evaluation data used in NAS-Bench-102 can be downloaded from [Google Drive](https://drive.google.com/open?id=1L0Lzq8rWpZLPfiQGd6QR8q5xLV88emU7) or [Baidu-Wangpan (code:4fg7)](https://pan.baidu.com/s/1XAzavPKq3zcat1yBA1L2tQ).
It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default). If you want to generate NAS-Bench-102 or similar NAS datasets or training models by yourself, you need these data. It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default). If you want to generate NAS-Bench-102 or similar NAS datasets or training models by yourself, you need these data.
## How to Use NAS-Bench-102 ## How to Use NAS-Bench-102

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README.md
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@ -13,7 +13,7 @@ More NAS resources can be found in [Awesome-NAS](https://github.com/D-X-Y/Awesom
## Requirements and Preparation ## Requirements and Preparation
Please install `PyTorch>=1.1.0`, `Python>=3.6`, and `opencv`. Please install `PyTorch>=1.2.0`, `Python>=3.6`, and `opencv`.
The CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`. The CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.
Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from [Google Driver](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (or train by yourself) and save into `.latent-data`. Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from [Google Driver](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (or train by yourself) and save into `.latent-data`.

2
exps/NAS-Bench-102/main.py
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@ -213,7 +213,7 @@ def train_single_model(save_dir, workers, datasets, xpaths, splits, use_less, se
def generate_meta_info(save_dir, max_node, divide=40): def generate_meta_info(save_dir, max_node, divide=40):
aa_nas_bench_ss = get_search_spaces('cell', 'aa-nas') aa_nas_bench_ss = get_search_spaces('cell', 'nas-bench-102')
archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False) archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
print ('There are {:} archs vs {:}.'.format(len(archs), len(aa_nas_bench_ss) ** ((max_node-1)*max_node/2))) print ('There are {:} archs vs {:}.'.format(len(archs), len(aa_nas_bench_ss) ** ((max_node-1)*max_node/2)))

31
exps/algos/DARTS-V1.py
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@ -17,6 +17,7 @@ from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_che
from utils import get_model_infos, obtain_accuracy from utils import get_model_infos, obtain_accuracy
from log_utils import AverageMeter, time_string, convert_secs2time from log_utils import AverageMeter, time_string, convert_secs2time
from models import get_cell_based_tiny_net, get_search_spaces from models import get_cell_based_tiny_net, get_search_spaces
from nas_102_api import NASBench102API as API
def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger): def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger):
@ -144,6 +145,11 @@ def main(xargs):
flop, param = get_model_infos(search_model, xshape) flop, param = get_model_infos(search_model, xshape)
#logger.log('{:}'.format(search_model)) #logger.log('{:}'.format(search_model))
logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param)) logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
if xargs.arch_nas_dataset is None:
api = None
else:
api = API(xargs.arch_nas_dataset)
logger.log('{:} create API = {:} done'.format(time_string(), api))
last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best') last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda() network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
@ -165,7 +171,7 @@ def main(xargs):
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {} start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {}
# start training # start training
start_time, epoch_time, total_epoch = time.time(), AverageMeter(), config.epochs + config.warmup start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup
for epoch in range(start_epoch, total_epoch): for epoch in range(start_epoch, total_epoch):
w_scheduler.update(epoch, 0.0) w_scheduler.update(epoch, 0.0)
need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.val * (total_epoch-epoch), True) ) need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.val * (total_epoch-epoch), True) )
@ -173,7 +179,8 @@ def main(xargs):
logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr()))) logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))
search_w_loss, search_w_top1, search_w_top5 = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger) search_w_loss, search_w_top1, search_w_top5 = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger)
logger.log('[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5)) search_time.update(time.time() - start_time)
logger.log('[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum))
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5)) logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
# check the best accuracy # check the best accuracy
@ -204,6 +211,8 @@ def main(xargs):
if find_best: if find_best:
logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, valid_a_top1)) logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, valid_a_top1))
copy_checkpoint(model_base_path, model_best_path, logger) copy_checkpoint(model_base_path, model_best_path, logger)
if api is not None:
logger.log('{:}'.format(api.query_by_arch( genotypes[epoch] )))
with torch.no_grad(): with torch.no_grad():
logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() )) logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
# measure elapsed time # measure elapsed time
@ -211,22 +220,8 @@ def main(xargs):
start_time = time.time() start_time = time.time()
logger.log('\n' + '-'*100) logger.log('\n' + '-'*100)
# check the performance from the architecture dataset logger.log('DARTS-V1 : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(total_epoch, search_time.sum, genotypes[total_epoch-1]))
#if xargs.arch_nas_dataset is None or not os.path.isfile(xargs.arch_nas_dataset): if api is not None: logger.log('{:}'.format( api.query_by_arch(genotypes[total_epoch-1]) ))
# logger.log('Can not find the architecture dataset : {:}.'.format(xargs.arch_nas_dataset))
#else:
# nas_bench = NASBenchmarkAPI(xargs.arch_nas_dataset)
# geno = genotypes[total_epoch-1]
# logger.log('The last model is {:}'.format(geno))
# info = nas_bench.query_by_arch( geno )
# if info is None: logger.log('Did not find this architecture : {:}.'.format(geno))
# else : logger.log('{:}'.format(info))
# logger.log('-'*100)
# geno = genotypes['best']
# logger.log('The best model is {:}'.format(geno))
# info = nas_bench.query_by_arch( geno )
# if info is None: logger.log('Did not find this architecture : {:}.'.format(geno))
# else : logger.log('{:}'.format(info))
logger.close() logger.close()

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exps/algos/R_EA.py
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@ -59,9 +59,9 @@ def train_and_eval(arch, nas_bench, extra_info):
if nas_bench is not None: if nas_bench is not None:
arch_index = nas_bench.query_index_by_arch( arch ) arch_index = nas_bench.query_index_by_arch( arch )
assert arch_index >= 0, 'can not find this arch : {:}'.format(arch) assert arch_index >= 0, 'can not find this arch : {:}'.format(arch)
info = nas_bench.arch2infos[ arch_index ] info = nas_bench.get_more_info(arch_index, 'cifar10-valid', True)
_, valid_acc = info.get_metrics('cifar10-valid', 'x-valid' , 25, True) # use the validation accuracy after 25 training epochs import pdb; pdb.set_trace()
#import pdb; pdb.set_trace() #_, valid_acc = info.get_metrics('cifar10-valid', 'x-valid' , 25, True) # use the validation accuracy after 25 training epochs
else: else:
# train a model from scratch. # train a model from scratch.
raise ValueError('NOT IMPLEMENT YET') raise ValueError('NOT IMPLEMENT YET')

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lib/models/__init__.py
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@ -36,6 +36,7 @@ def get_cell_based_tiny_net(config):
def get_search_spaces(xtype, name): def get_search_spaces(xtype, name):
if xtype == 'cell': if xtype == 'cell':
from .cell_operations import SearchSpaceNames from .cell_operations import SearchSpaceNames
assert name in SearchSpaceNames, 'invalid name [{:}] in {:}'.format(name, SearchSpaceNames.keys())
return SearchSpaceNames[name] return SearchSpaceNames[name]
else: else:
raise ValueError('invalid search-space type is {:}'.format(xtype)) raise ValueError('invalid search-space type is {:}'.format(xtype))

5
lib/models/cell_operations.py
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@ -17,10 +17,11 @@ OPS = {
} }
CONNECT_NAS_BENCHMARK = ['none', 'skip_connect', 'nor_conv_3x3'] CONNECT_NAS_BENCHMARK = ['none', 'skip_connect', 'nor_conv_3x3']
AA_NAS_BENCHMARK = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3'] NAS_BENCH_102 = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']
SearchSpaceNames = {'connect-nas' : CONNECT_NAS_BENCHMARK, SearchSpaceNames = {'connect-nas' : CONNECT_NAS_BENCHMARK,
'aa-nas' : AA_NAS_BENCHMARK, 'aa-nas' : NAS_BENCH_102,
'nas-bench-102': NAS_BENCH_102,
'full' : sorted(list(OPS.keys()))} 'full' : sorted(list(OPS.keys()))}

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lib/nas_102_api/api.py
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@ -129,6 +129,27 @@ class NASBench102API(object):
assert 0 <= index < len(self.meta_archs), 'invalid index : {:} vs. {:}.'.format(index, len(self.meta_archs)) assert 0 <= index < len(self.meta_archs), 'invalid index : {:} vs. {:}.'.format(index, len(self.meta_archs))
return copy.deepcopy(self.meta_archs[index]) return copy.deepcopy(self.meta_archs[index])
def get_more_info(self, index, dataset, use_12epochs_result=False):
if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
else : basestr, arch2infos = '200epochs', self.arch2infos_full
archresult = arch2infos[index]
if dataset == 'cifar10-valid':
train_info = archresult.get_metrics(dataset, 'train', is_random=True)
valid_info = archresult.get_metrics(dataset, 'x-valid', is_random=True)
test__info = archresult.get_metrics(dataset, 'ori-test', is_random=True)
total = train_info['iepoch'] + 1
return {'train-loss' : train_info['loss'],
'train-accuracy': train_info['accuracy'],
'train-all-time': train_info['all_time'],
'valid-loss' : valid_info['loss'],
'valid-accuracy': valid_info['accuracy'],
'valid-all-time': valid_info['all_time'],
'valid-per-time': valid_info['all_time'] / total,
'test-loss' : test__info['loss'],
'test-accuracy' : test__info['accuracy']}
else:
raise ValueError('coming soon...')
def show(self, index=-1): def show(self, index=-1):
if index < 0: # show all architectures if index < 0: # show all architectures
print(self) print(self)
@ -367,23 +388,28 @@ class ResultsCount(object):
def get_train(self, iepoch=None): def get_train(self, iepoch=None):
if iepoch is None: iepoch = self.epochs-1 if iepoch is None: iepoch = self.epochs-1
assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs) assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)
if self.train_times is not None: xtime = self.train_times[iepoch] if self.train_times is not None:
else : xtime = None xtime = self.train_times[iepoch]
atime = sum([self.train_times[i] for i in range(iepoch+1)])
else: xtime, atime = None, None
return {'iepoch' : iepoch, return {'iepoch' : iepoch,
'loss' : self.train_losses[iepoch], 'loss' : self.train_losses[iepoch],
'accuracy': self.train_acc1es[iepoch], 'accuracy': self.train_acc1es[iepoch],
'time' : xtime} 'cur_time': xtime,
'all_time': atime}
def get_eval(self, name, iepoch=None): def get_eval(self, name, iepoch=None):
if iepoch is None: iepoch = self.epochs-1 if iepoch is None: iepoch = self.epochs-1
assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs) assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)
if isinstance(self.eval_times,dict) and len(self.eval_times) > 0: if isinstance(self.eval_times,dict) and len(self.eval_times) > 0:
xtime = self.eval_times['{:}@{:}'.format(name,iepoch)] xtime = self.eval_times['{:}@{:}'.format(name,iepoch)]
else: xtime = None atime = sum([self.eval_times['{:}@{:}'.format(name,i)] for i in range(iepoch+1)])
else: xtime, atime = None, None
return {'iepoch' : iepoch, return {'iepoch' : iepoch,
'loss' : self.eval_losses['{:}@{:}'.format(name,iepoch)], 'loss' : self.eval_losses['{:}@{:}'.format(name,iepoch)],
'accuracy': self.eval_acc1es['{:}@{:}'.format(name,iepoch)], 'accuracy': self.eval_acc1es['{:}@{:}'.format(name,iepoch)],
'time' : xtime} 'cur_time': xtime,
'all_time': atime}
def get_net_param(self): def get_net_param(self):
return self.net_state_dict return self.net_state_dict

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others/paddlepaddle/.gitignore vendored
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@ -1,3 +0,0 @@
.DS_Store
*.whl
snapshots

118
others/paddlepaddle/README.md
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@ -1,118 +0,0 @@
# Image Classification based on NAS-Searched Models
This directory contains 10 image classification models.
Nine of them are automatically searched models from different Neural Architecture Search (NAS) algorithms. The other is the residual network.
We provide codes and scripts to train these models on both CIFAR-10 and CIFAR-100.
We use the standard data augmentation, i.e., random crop, random flip, and normalization.
---
## Table of Contents
- [Installation](#installation)
- [Data Preparation](#data-preparation)
- [Training Models](#training-models)
- [Project Structure](#project-structure)
- [Citation](#citation)
### Installation
This project has the following requirements:
- Python = 3.6
- PadddlePaddle Fluid >= v0.15.0
### Data Preparation
Please download [CIFAR-10](https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz) and [CIFAR-100](https://dataset.bj.bcebos.com/cifar/cifar-100-python.tar.gz) before running the codes.
Note that the MD5 of CIFAR-10-Python compressed file is `c58f30108f718f92721af3b95e74349a` and the MD5 of CIFAR-100-Python compressed file is `eb9058c3a382ffc7106e4002c42a8d85`.
Please save the file into `${TORCH_HOME}/cifar.python`.
After data preparation, there should be two files `${TORCH_HOME}/cifar.python/cifar-10-python.tar.gz` and `${TORCH_HOME}/cifar.python/cifar-100-python.tar.gz`.
### Training Models
After setting up the environment and preparing the data, one can train the model. The main function entrance is `train_cifar.py`. We also provide some scripts for easy usage.
```
bash ./scripts/base-train.sh 0 cifar-10 ResNet110
bash ./scripts/train-nas.sh 0 cifar-10 GDAS_V1
bash ./scripts/train-nas.sh 0 cifar-10 GDAS_V2
bash ./scripts/train-nas.sh 0 cifar-10 SETN
bash ./scripts/train-nas.sh 0 cifar-10 NASNet
bash ./scripts/train-nas.sh 0 cifar-10 ENASNet
bash ./scripts/train-nas.sh 0 cifar-10 AmoebaNet
bash ./scripts/train-nas.sh 0 cifar-10 PNASNet
bash ./scripts/train-nas.sh 0 cifar-100 SETN
```
The first argument is the GPU-ID to train your program, the second argument is the dataset name, and the last one is the model name.
Please use `./scripts/base-train.sh` for ResNet and use `./scripts/train-nas.sh` for NAS-searched models.
### Project Structure
```
.
├──train_cifar.py [Training CNN models]
├──lib [Library for dataset, models, and others]
│ └──models
│ ├──__init__.py [Import useful Classes and Functions in models]
│ ├──resnet.py [Define the ResNet models]
│ ├──operations.py [Define the atomic operation in NAS search space]
│ ├──genotypes.py [Define the topological structure of different NAS-searched models]
│ └──nas_net.py [Define the macro structure of NAS models]
│ └──utils
│ ├──__init__.py [Import useful Classes and Functions in utils]
│ ├──meter.py [Define the AverageMeter class to count the accuracy and loss]
│ ├──time_utils.py [Define some functions to print date or convert seconds into hours]
│ └──data_utils.py [Define data augmentation functions and dataset reader for CIFAR]
└──scripts [Scripts for running]
```
### Citation
If you find that this project helps your research, please consider citing these papers:
```
@inproceedings{dong2019one,
title = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
year = {2019}
}
@inproceedings{dong2019search,
title = {Searching for A Robust Neural Architecture in Four GPU Hours},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {1761--1770},
year = {2019}
}
@inproceedings{liu2018darts,
title = {Darts: Differentiable architecture search},
author = {Liu, Hanxiao and Simonyan, Karen and Yang, Yiming},
booktitle = {ICLR},
year = {2018}
}
@inproceedings{pham2018efficient,
title = {Efficient Neural Architecture Search via Parameter Sharing},
author = {Pham, Hieu and Guan, Melody and Zoph, Barret and Le, Quoc and Dean, Jeff},
booktitle = {International Conference on Machine Learning (ICML)},
pages = {4092--4101},
year = {2018}
}
@inproceedings{liu2018progressive,
title = {Progressive neural architecture search},
author = {Liu, Chenxi and Zoph, Barret and Neumann, Maxim and Shlens, Jonathon and Hua, Wei and Li, Li-Jia and Fei-Fei, Li and Yuille, Alan and Huang, Jonathan and Murphy, Kevin},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
pages = {19--34},
year = {2018}
}
@inproceedings{zoph2018learning,
title = {Learning transferable architectures for scalable image recognition},
author = {Zoph, Barret and Vasudevan, Vijay and Shlens, Jonathon and Le, Quoc V},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {8697--8710},
year = {2018}
}
@inproceedings{real2019regularized,
title = {Regularized evolution for image classifier architecture search},
author = {Real, Esteban and Aggarwal, Alok and Huang, Yanping and Le, Quoc V},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
pages = {4780--4789},
year = {2019}
}
```

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others/paddlepaddle/lib/models/__init__.py
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@ -1,3 +0,0 @@
from .genotypes import Networks
from .nas_net import NASCifarNet
from .resnet import resnet_cifar

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others/paddlepaddle/lib/models/genotypes.py
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@ -1,175 +0,0 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from collections import namedtuple
Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat')
# Learning Transferable Architectures for Scalable Image Recognition, CVPR 2018
NASNet = Genotype(
normal = [
(('sep_conv_5x5', 1), ('sep_conv_3x3', 0)),
(('sep_conv_5x5', 0), ('sep_conv_3x3', 0)),
(('avg_pool_3x3', 1), ('skip_connect', 0)),
(('avg_pool_3x3', 0), ('avg_pool_3x3', 0)),
(('sep_conv_3x3', 1), ('skip_connect', 1)),
],
normal_concat = [2, 3, 4, 5, 6],
reduce = [
(('sep_conv_5x5', 1), ('sep_conv_7x7', 0)),
(('max_pool_3x3', 1), ('sep_conv_7x7', 0)),
(('avg_pool_3x3', 1), ('sep_conv_5x5', 0)),
(('skip_connect', 3), ('avg_pool_3x3', 2)),
(('sep_conv_3x3', 2), ('max_pool_3x3', 1)),
],
reduce_concat = [4, 5, 6],
)
# Progressive Neural Architecture Search, ECCV 2018
PNASNet = Genotype(
normal = [
(('sep_conv_5x5', 0), ('max_pool_3x3', 0)),
(('sep_conv_7x7', 1), ('max_pool_3x3', 1)),
(('sep_conv_5x5', 1), ('sep_conv_3x3', 1)),
(('sep_conv_3x3', 4), ('max_pool_3x3', 1)),
(('sep_conv_3x3', 0), ('skip_connect', 1)),
],
normal_concat = [2, 3, 4, 5, 6],
reduce = [
(('sep_conv_5x5', 0), ('max_pool_3x3', 0)),
(('sep_conv_7x7', 1), ('max_pool_3x3', 1)),
(('sep_conv_5x5', 1), ('sep_conv_3x3', 1)),
(('sep_conv_3x3', 4), ('max_pool_3x3', 1)),
(('sep_conv_3x3', 0), ('skip_connect', 1)),
],
reduce_concat = [2, 3, 4, 5, 6],
)
# Regularized Evolution for Image Classifier Architecture Search, AAAI 2019
AmoebaNet = Genotype(
normal = [
(('avg_pool_3x3', 0), ('max_pool_3x3', 1)),
(('sep_conv_3x3', 0), ('sep_conv_5x5', 2)),
(('sep_conv_3x3', 0), ('avg_pool_3x3', 3)),
(('sep_conv_3x3', 1), ('skip_connect', 1)),
(('skip_connect', 0), ('avg_pool_3x3', 1)),
],
normal_concat = [4, 5, 6],
reduce = [
(('avg_pool_3x3', 0), ('sep_conv_3x3', 1)),
(('max_pool_3x3', 0), ('sep_conv_7x7', 2)),
(('sep_conv_7x7', 0), ('avg_pool_3x3', 1)),
(('max_pool_3x3', 0), ('max_pool_3x3', 1)),
(('conv_7x1_1x7', 0), ('sep_conv_3x3', 5)),
],
reduce_concat = [3, 4, 6]
)
# Efficient Neural Architecture Search via Parameter Sharing, ICML 2018
ENASNet = Genotype(
normal = [
(('sep_conv_3x3', 1), ('skip_connect', 1)),
(('sep_conv_5x5', 1), ('skip_connect', 0)),
(('avg_pool_3x3', 0), ('sep_conv_3x3', 1)),
(('sep_conv_3x3', 0), ('avg_pool_3x3', 1)),
(('sep_conv_5x5', 1), ('avg_pool_3x3', 0)),
],
normal_concat = [2, 3, 4, 5, 6],
reduce = [
(('sep_conv_5x5', 0), ('sep_conv_3x3', 1)), # 2
(('sep_conv_3x3', 1), ('avg_pool_3x3', 1)), # 3
(('sep_conv_3x3', 1), ('avg_pool_3x3', 1)), # 4
(('avg_pool_3x3', 1), ('sep_conv_5x5', 4)), # 5
(('sep_conv_3x3', 5), ('sep_conv_5x5', 0)),
],
reduce_concat = [2, 3, 4, 5, 6],
)
# DARTS: Differentiable Architecture Search, ICLR 2019
DARTS_V1 = Genotype(
normal=[
(('sep_conv_3x3', 1), ('sep_conv_3x3', 0)), # step 1
(('skip_connect', 0), ('sep_conv_3x3', 1)), # step 2
(('skip_connect', 0), ('sep_conv_3x3', 1)), # step 3
(('sep_conv_3x3', 0), ('skip_connect', 2)) # step 4
],
normal_concat=[2, 3, 4, 5],
reduce=[
(('max_pool_3x3', 0), ('max_pool_3x3', 1)), # step 1
(('skip_connect', 2), ('max_pool_3x3', 0)), # step 2
(('max_pool_3x3', 0), ('skip_connect', 2)), # step 3
(('skip_connect', 2), ('avg_pool_3x3', 0)) # step 4
],
reduce_concat=[2, 3, 4, 5],
)
# DARTS: Differentiable Architecture Search, ICLR 2019
DARTS_V2 = Genotype(
normal=[
(('sep_conv_3x3', 0), ('sep_conv_3x3', 1)), # step 1
(('sep_conv_3x3', 0), ('sep_conv_3x3', 1)), # step 2
(('sep_conv_3x3', 1), ('skip_connect', 0)), # step 3
(('skip_connect', 0), ('dil_conv_3x3', 2)) # step 4
],
normal_concat=[2, 3, 4, 5],
reduce=[
(('max_pool_3x3', 0), ('max_pool_3x3', 1)), # step 1
(('skip_connect', 2), ('max_pool_3x3', 1)), # step 2
(('max_pool_3x3', 0), ('skip_connect', 2)), # step 3
(('skip_connect', 2), ('max_pool_3x3', 1)) # step 4
],
reduce_concat=[2, 3, 4, 5],
)
# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019
SETN = Genotype(
normal=[
(('skip_connect', 0), ('sep_conv_5x5', 1)),
(('sep_conv_5x5', 0), ('sep_conv_3x3', 1)),
(('sep_conv_5x5', 1), ('sep_conv_5x5', 3)),
(('max_pool_3x3', 1), ('conv_3x1_1x3', 4))],
normal_concat=[2, 3, 4, 5],
reduce=[
(('sep_conv_3x3', 0), ('sep_conv_5x5', 1)),
(('avg_pool_3x3', 0), ('sep_conv_5x5', 1)),
(('avg_pool_3x3', 0), ('sep_conv_5x5', 1)),
(('avg_pool_3x3', 0), ('skip_connect', 1))],
reduce_concat=[2, 3, 4, 5],
)
# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019
GDAS_V1 = Genotype(
normal=[
(('skip_connect', 0), ('skip_connect', 1)),
(('skip_connect', 0), ('sep_conv_5x5', 2)),
(('sep_conv_3x3', 3), ('skip_connect', 0)),
(('sep_conv_5x5', 4), ('sep_conv_3x3', 3))],
normal_concat=[2, 3, 4, 5],
reduce=[
(('sep_conv_5x5', 0), ('sep_conv_3x3', 1)),
(('sep_conv_5x5', 2), ('sep_conv_5x5', 1)),
(('dil_conv_5x5', 2), ('sep_conv_3x3', 1)),
(('sep_conv_5x5', 0), ('sep_conv_5x5', 1))],
reduce_concat=[2, 3, 4, 5],
)
Networks = {'DARTS_V1' : DARTS_V1,
'DARTS_V2' : DARTS_V2,
'DARTS' : DARTS_V2,
'NASNet' : NASNet,
'ENASNet' : ENASNet,
'AmoebaNet': AmoebaNet,
'GDAS_V1' : GDAS_V1,
'PNASNet' : PNASNet,
'SETN' : SETN,
}

79
others/paddlepaddle/lib/models/nas_net.py
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@ -1,79 +0,0 @@
import paddle
import paddle.fluid as fluid
from .operations import OPS
def AuxiliaryHeadCIFAR(inputs, C, class_num):
print ('AuxiliaryHeadCIFAR : inputs-shape : {:}'.format(inputs.shape))
temp = fluid.layers.relu(inputs)
temp = fluid.layers.pool2d(temp, pool_size=5, pool_stride=3, pool_padding=0, pool_type='avg')
temp = fluid.layers.conv2d(temp, filter_size=1, num_filters=128, stride=1, padding=0, act=None, bias_attr=False)
temp = fluid.layers.batch_norm(input=temp, act='relu', bias_attr=None)
temp = fluid.layers.conv2d(temp, filter_size=1, num_filters=768, stride=2, padding=0, act=None, bias_attr=False)
temp = fluid.layers.batch_norm(input=temp, act='relu', bias_attr=None)
print ('AuxiliaryHeadCIFAR : last---shape : {:}'.format(temp.shape))
predict = fluid.layers.fc(input=temp, size=class_num, act='softmax')
return predict
def InferCell(name, inputs_prev_prev, inputs_prev, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev):
print ('[{:}] C_prev_prev={:} C_prev={:}, C={:}, reduction_prev={:}, reduction={:}'.format(name, C_prev_prev, C_prev, C, reduction_prev, reduction))
print ('inputs_prev_prev : {:}'.format(inputs_prev_prev.shape))
print ('inputs_prev : {:}'.format(inputs_prev.shape))
inputs_prev_prev = OPS['skip_connect'](inputs_prev_prev, C_prev_prev, C, 2 if reduction_prev else 1)
inputs_prev = OPS['skip_connect'](inputs_prev, C_prev, C, 1)
print ('inputs_prev_prev : {:}'.format(inputs_prev_prev.shape))
print ('inputs_prev : {:}'.format(inputs_prev.shape))
if reduction: step_ops, concat = genotype.reduce, genotype.reduce_concat
else : step_ops, concat = genotype.normal, genotype.normal_concat
states = [inputs_prev_prev, inputs_prev]
for istep, operations in enumerate(step_ops):
op_a, op_b = operations
# the first operation
#print ('-->>[{:}/{:}] [{:}] + [{:}]'.format(istep, len(step_ops), op_a, op_b))
stride = 2 if reduction and op_a[1] < 2 else 1
tensor1 = OPS[ op_a[0] ](states[op_a[1]], C, C, stride)
stride = 2 if reduction and op_b[1] < 2 else 1
tensor2 = OPS[ op_b[0] ](states[op_b[1]], C, C, stride)
state = fluid.layers.elementwise_add(x=tensor1, y=tensor2, act=None)
assert tensor1.shape == tensor2.shape, 'invalid shape {:} vs. {:}'.format(tensor1.shape, tensor2.shape)
print ('-->>[{:}/{:}] tensor={:} from {:} + {:}'.format(istep, len(step_ops), state.shape, tensor1.shape, tensor2.shape))
states.append( state )
states_to_cat = [states[x] for x in concat]
outputs = fluid.layers.concat(states_to_cat, axis=1)
print ('-->> output-shape : {:} from concat={:}'.format(outputs.shape, concat))
return outputs
# NASCifarNet(inputs, 36, 6, 3, 10, 'xxx', True)
def NASCifarNet(ipt, C, N, stem_multiplier, class_num, genotype, auxiliary):
# cifar head module
C_curr = stem_multiplier * C
stem = fluid.layers.conv2d(ipt, filter_size=3, num_filters=C_curr, stride=1, padding=1, act=None, bias_attr=False)
stem = fluid.layers.batch_norm(input=stem, act=None, bias_attr=None)
print ('stem-shape : {:}'.format(stem.shape))
# N + 1 + N + 1 + N cells
layer_channels = [C ] * N + [C*2 ] + [C*2 ] * N + [C*4 ] + [C*4 ] * N
layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
reduction_prev = False
auxiliary_pred = None
cell_results = [stem, stem]
for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
xstr = '{:02d}/{:02d}'.format(index, len(layer_channels))
cell_result = InferCell(xstr, cell_results[-2], cell_results[-1], genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)
reduction_prev = reduction
C_prev_prev, C_prev = C_prev, cell_result.shape[1]
cell_results.append( cell_result )
if auxiliary and reduction and C_curr == C*4:
auxiliary_pred = AuxiliaryHeadCIFAR(cell_result, C_prev, class_num)
global_P = fluid.layers.pool2d(input=cell_results[-1], pool_size=8, pool_type='avg', pool_stride=1)
predicts = fluid.layers.fc(input=global_P, size=class_num, act='softmax')
print ('predict-shape : {:}'.format(predicts.shape))
if auxiliary_pred is None:
return predicts
else:
return [predicts, auxiliary_pred]

91
others/paddlepaddle/lib/models/operations.py
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@ -1,91 +0,0 @@
import paddle
import paddle.fluid as fluid
OPS = {
'none' : lambda inputs, C_in, C_out, stride: ZERO(inputs, stride),
'avg_pool_3x3' : lambda inputs, C_in, C_out, stride: POOL_3x3(inputs, C_in, C_out, stride, 'avg'),
'max_pool_3x3' : lambda inputs, C_in, C_out, stride: POOL_3x3(inputs, C_in, C_out, stride, 'max'),
'skip_connect' : lambda inputs, C_in, C_out, stride: Identity(inputs, C_in, C_out, stride),
'sep_conv_3x3' : lambda inputs, C_in, C_out, stride: SepConv(inputs, C_in, C_out, 3, stride, 1),
'sep_conv_5x5' : lambda inputs, C_in, C_out, stride: SepConv(inputs, C_in, C_out, 5, stride, 2),
'sep_conv_7x7' : lambda inputs, C_in, C_out, stride: SepConv(inputs, C_in, C_out, 7, stride, 3),
'dil_conv_3x3' : lambda inputs, C_in, C_out, stride: DilConv(inputs, C_in, C_out, 3, stride, 2, 2),
'dil_conv_5x5' : lambda inputs, C_in, C_out, stride: DilConv(inputs, C_in, C_out, 5, stride, 4, 2),
'conv_3x1_1x3' : lambda inputs, C_in, C_out, stride: Conv313(inputs, C_in, C_out, stride),
'conv_7x1_1x7' : lambda inputs, C_in, C_out, stride: Conv717(inputs, C_in, C_out, stride),
}
def ReLUConvBN(inputs, C_in, C_out, kernel, stride, padding):
temp = fluid.layers.relu(inputs)
temp = fluid.layers.conv2d(temp, filter_size=kernel, num_filters=C_out, stride=stride, padding=padding, act=None, bias_attr=False)
temp = fluid.layers.batch_norm(input=temp, act=None, bias_attr=None)
return temp
def ZERO(inputs, stride):
if stride == 1:
return inputs * 0
elif stride == 2:
return fluid.layers.pool2d(inputs, filter_size=2, pool_stride=2, pool_padding=0, pool_type='avg') * 0
else:
raise ValueError('invalid stride of {:} not [1, 2]'.format(stride))
def Identity(inputs, C_in, C_out, stride):
if C_in == C_out and stride == 1:
return inputs
elif stride == 1:
return ReLUConvBN(inputs, C_in, C_out, 1, 1, 0)
else:
temp1 = fluid.layers.relu(inputs)
temp2 = fluid.layers.pad2d(input=temp1, paddings=[0, 1, 0, 1], mode='reflect')
temp2 = fluid.layers.slice(temp2, axes=[0, 1, 2, 3], starts=[0, 0, 1, 1], ends=[999, 999, 999, 999])
temp1 = fluid.layers.conv2d(temp1, filter_size=1, num_filters=C_out//2, stride=stride, padding=0, act=None, bias_attr=False)
temp2 = fluid.layers.conv2d(temp2, filter_size=1, num_filters=C_out-C_out//2, stride=stride, padding=0, act=None, bias_attr=False)
temp = fluid.layers.concat([temp1,temp2], axis=1)
return fluid.layers.batch_norm(input=temp, act=None, bias_attr=None)
def POOL_3x3(inputs, C_in, C_out, stride, mode):
if C_in == C_out:
xinputs = inputs
else:
xinputs = ReLUConvBN(inputs, C_in, C_out, 1, 1, 0)
return fluid.layers.pool2d(xinputs, pool_size=3, pool_stride=stride, pool_padding=1, pool_type=mode)
def SepConv(inputs, C_in, C_out, kernel, stride, padding):
temp = fluid.layers.relu(inputs)
temp = fluid.layers.conv2d(temp, filter_size=kernel, num_filters=C_in , stride=stride, padding=padding, act=None, bias_attr=False)
temp = fluid.layers.conv2d(temp, filter_size= 1, num_filters=C_in , stride= 1, padding= 0, act=None, bias_attr=False)
temp = fluid.layers.batch_norm(input=temp, act='relu', bias_attr=None)
temp = fluid.layers.conv2d(temp, filter_size=kernel, num_filters=C_in , stride= 1, padding=padding, act=None, bias_attr=False)
temp = fluid.layers.conv2d(temp, filter_size= 1, num_filters=C_out, stride= 1, padding= 0, act=None, bias_attr=False)
temp = fluid.layers.batch_norm(input=temp, act=None , bias_attr=None)
return temp
def DilConv(inputs, C_in, C_out, kernel, stride, padding, dilation):
temp = fluid.layers.relu(inputs)
temp = fluid.layers.conv2d(temp, filter_size=kernel, num_filters=C_in , stride=stride, padding=padding, dilation=dilation, act=None, bias_attr=False)
temp = fluid.layers.conv2d(temp, filter_size= 1, num_filters=C_out, stride= 1, padding= 0, act=None, bias_attr=False)
temp = fluid.layers.batch_norm(input=temp, act=None, bias_attr=None)
return temp
def Conv313(inputs, C_in, C_out, stride):
temp = fluid.layers.relu(inputs)
temp = fluid.layers.conv2d(temp, filter_size=(1,3), num_filters=C_out, stride=(1,stride), padding=(0,1), act=None, bias_attr=False)
temp = fluid.layers.conv2d(temp, filter_size=(3,1), num_filters=C_out, stride=(stride,1), padding=(1,0), act=None, bias_attr=False)
temp = fluid.layers.batch_norm(input=temp, act=None, bias_attr=None)
return temp
def Conv717(inputs, C_in, C_out, stride):
temp = fluid.layers.relu(inputs)
temp = fluid.layers.conv2d(temp, filter_size=(1,7), num_filters=C_out, stride=(1,stride), padding=(0,3), act=None, bias_attr=False)
temp = fluid.layers.conv2d(temp, filter_size=(7,1), num_filters=C_out, stride=(stride,1), padding=(3,0), act=None, bias_attr=False)
temp = fluid.layers.batch_norm(input=temp, act=None, bias_attr=None)
return temp

65
others/paddlepaddle/lib/models/resnet.py
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@ -1,65 +0,0 @@
import paddle
import paddle.fluid as fluid
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
def shortcut(input, ch_in, ch_out, stride):
if stride == 2:
temp = fluid.layers.pool2d(input, pool_size=2, pool_type='avg', pool_stride=2)
temp = fluid.layers.conv2d(temp , filter_size=1, num_filters=ch_out, stride=1, padding=0, act=None, bias_attr=None)
return temp
elif ch_in != ch_out:
return conv_bn_layer(input, ch_out, 1, stride, 0, None, None)
else:
return input
def basicblock(input, ch_in, ch_out, stride):
tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None, bias_attr=True)
short = shortcut(input, ch_in, ch_out, stride)
return fluid.layers.elementwise_add(x=tmp, y=short, act='relu')
def layer_warp(block_func, input, ch_in, ch_out, count, stride):
tmp = block_func(input, ch_in, ch_out, stride)
for i in range(1, count):
tmp = block_func(tmp, ch_out, ch_out, 1)
return tmp
def resnet_cifar(ipt, depth, class_num):
# depth should be one of 20, 32, 44, 56, 110, 1202
assert (depth - 2) % 6 == 0
n = (depth - 2) // 6
print('[resnet] depth : {:}, class_num : {:}'.format(depth, class_num))
conv1 = conv_bn_layer(ipt, ch_out=16, filter_size=3, stride=1, padding=1)
print('conv-1 : shape = {:}'.format(conv1.shape))
res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
print('res--1 : shape = {:}'.format(res1.shape))
res2 = layer_warp(basicblock, res1 , 16, 32, n, 2)
print('res--2 : shape = {:}'.format(res2.shape))
res3 = layer_warp(basicblock, res2 , 32, 64, n, 2)
print('res--3 : shape = {:}'.format(res3.shape))
pool = fluid.layers.pool2d(input=res3, pool_size=8, pool_type='avg', pool_stride=1)
print('pool : shape = {:}'.format(pool.shape))
predict = fluid.layers.fc(input=pool, size=class_num, act='softmax')
print('predict: shape = {:}'.format(predict.shape))
return predict

6
others/paddlepaddle/lib/utils/__init__.py
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@ -1,6 +0,0 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .meter import AverageMeter
from .time_utils import time_for_file, time_string, time_string_short, time_print, convert_size2str, convert_secs2time
from .data_utils import reader_creator

64
others/paddlepaddle/lib/utils/data_utils.py
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@ -1,64 +0,0 @@
import random, tarfile
import numpy, six
from six.moves import cPickle as pickle
from PIL import Image, ImageOps
def train_cifar_augmentation(image):
# flip
if random.random() < 0.5: image1 = image.transpose(Image.FLIP_LEFT_RIGHT)
else: image1 = image
# random crop
image2 = ImageOps.expand(image1, border=4, fill=0)
i = random.randint(0, 40 - 32)
j = random.randint(0, 40 - 32)
image3 = image2.crop((j,i,j+32,i+32))
# to numpy
image3 = numpy.array(image3) / 255.0
mean = numpy.array([x / 255 for x in [125.3, 123.0, 113.9]]).reshape(1, 1, 3)
std = numpy.array([x / 255 for x in [63.0, 62.1, 66.7]]).reshape(1, 1, 3)
return (image3 - mean) / std
def valid_cifar_augmentation(image):
image3 = numpy.array(image) / 255.0
mean = numpy.array([x / 255 for x in [125.3, 123.0, 113.9]]).reshape(1, 1, 3)
std = numpy.array([x / 255 for x in [63.0, 62.1, 66.7]]).reshape(1, 1, 3)
return (image3 - mean) / std
def reader_creator(filename, sub_name, is_train, cycle=False):
def read_batch(batch):
data = batch[six.b('data')]
labels = batch.get(
six.b('labels'), batch.get(six.b('fine_labels'), None))
assert labels is not None
for sample, label in six.moves.zip(data, labels):
sample = sample.reshape(3, 32, 32)
sample = sample.transpose((1, 2, 0))
image = Image.fromarray(sample)
if is_train:
ximage = train_cifar_augmentation(image)
else:
ximage = valid_cifar_augmentation(image)
ximage = ximage.transpose((2, 0, 1))
yield ximage.astype(numpy.float32), int(label)
def reader():
with tarfile.open(filename, mode='r') as f:
names = (each_item.name for each_item in f
if sub_name in each_item.name)
while True:
for name in names:
if six.PY2:
batch = pickle.load(f.extractfile(name))
else:
batch = pickle.load(
f.extractfile(name), encoding='bytes')
for item in read_batch(batch):
yield item
if not cycle:
break
return reader

26
others/paddlepaddle/lib/utils/meter.py
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@ -1,26 +0,0 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import time, sys
import numpy as np
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0.0
self.avg = 0.0
self.sum = 0.0
self.count = 0.0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __repr__(self):
return ('{name}(val={val}, avg={avg}, count={count})'.format(name=self.__class__.__name__, **self.__dict__))

52
others/paddlepaddle/lib/utils/time_utils.py
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@ -1,52 +0,0 @@
# 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.
#
import time, sys
import numpy as np
def time_for_file():
ISOTIMEFORMAT='%d-%h-at-%H-%M-%S'
return '{}'.format(time.strftime( ISOTIMEFORMAT, time.gmtime(time.time()) ))
def time_string():
ISOTIMEFORMAT='%Y-%m-%d %X'
string = '[{}]'.format(time.strftime( ISOTIMEFORMAT, time.gmtime(time.time()) ))
return string
def time_string_short():
ISOTIMEFORMAT='%Y%m%d'
string = '{}'.format(time.strftime( ISOTIMEFORMAT, time.gmtime(time.time()) ))
return string
def time_print(string, is_print=True):
if (is_print):
print('{} : {}'.format(time_string(), string))
def convert_size2str(torch_size):
dims = len(torch_size)
string = '['
for idim in range(dims):
string = string + ' {}'.format(torch_size[idim])
return string + ']'
def convert_secs2time(epoch_time, return_str=False):
need_hour = int(epoch_time / 3600)
need_mins = int((epoch_time - 3600*need_hour) / 60)
need_secs = int(epoch_time - 3600*need_hour - 60*need_mins)
if return_str:
str = '[{:02d}:{:02d}:{:02d}]'.format(need_hour, need_mins, need_secs)
return str
else:
return need_hour, need_mins, need_secs
def print_log(print_string, log):
#if isinstance(log, Logger): log.log('{:}'.format(print_string))
if hasattr(log, 'log'): log.log('{:}'.format(print_string))
else:
print("{:}".format(print_string))
if log is not None:
log.write('{:}\n'.format(print_string))
log.flush()

31
others/paddlepaddle/scripts/base-train.sh
View File

@ -1,31 +0,0 @@
#!/bin/bash
# bash ./scripts/base-train.sh 0 cifar-10 ResNet110
echo script name: $0
echo $# arguments
if [ "$#" -ne 3 ] ;then
echo "Input illegal number of parameters " $#
echo "Need 3 parameters for GPU and dataset and the-model-name"
exit 1
fi
if [ "$TORCH_HOME" = "" ]; then
echo "Must set TORCH_HOME envoriment variable for data dir saving"
exit 1
else
echo "TORCH_HOME : $TORCH_HOME"
fi
GPU=$1
dataset=$2
model=$3
save_dir=snapshots/${dataset}-${model}
export FLAGS_fraction_of_gpu_memory_to_use="0.005"
export FLAGS_free_idle_memory=True
CUDA_VISIBLE_DEVICES=${GPU} python train_cifar.py \
--data_path $TORCH_HOME/cifar.python/${dataset}-python.tar.gz \
--log_dir ${save_dir} \
--dataset ${dataset} \
--model_name ${model} \
--lr 0.1 --epochs 300 --batch_size 256 --step_each_epoch 196

31
others/paddlepaddle/scripts/train-nas.sh
View File

@ -1,31 +0,0 @@
#!/bin/bash
# bash ./scripts/base-train.sh 0 cifar-10 ResNet110
echo script name: $0
echo $# arguments
if [ "$#" -ne 3 ] ;then
echo "Input illegal number of parameters " $#
echo "Need 3 parameters for GPU and dataset and the-model-name"
exit 1
fi
if [ "$TORCH_HOME" = "" ]; then
echo "Must set TORCH_HOME envoriment variable for data dir saving"
exit 1
else
echo "TORCH_HOME : $TORCH_HOME"
fi
GPU=$1
dataset=$2
model=$3
save_dir=snapshots/${dataset}-${model}
export FLAGS_fraction_of_gpu_memory_to_use="0.005"
export FLAGS_free_idle_memory=True
CUDA_VISIBLE_DEVICES=${GPU} python train_cifar.py \
--data_path $TORCH_HOME/cifar.python/${dataset}-python.tar.gz \
--log_dir ${save_dir} \
--dataset ${dataset} \
--model_name ${model} \
--lr 0.025 --epochs 600 --batch_size 96 --step_each_epoch 521

189
others/paddlepaddle/train_cifar.py
View File

@ -1,189 +0,0 @@
import os, sys, numpy as np, argparse
from pathlib import Path
import paddle.fluid as fluid
import math, time, paddle
import paddle.fluid.layers.ops as ops
#from tb_paddle import SummaryWriter
lib_dir = (Path(__file__).parent / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from models import resnet_cifar, NASCifarNet, Networks
from utils import AverageMeter, time_for_file, time_string, convert_secs2time
from utils import reader_creator
def inference_program(model_name, num_class):
# The image is 32 * 32 with RGB representation.
data_shape = [3, 32, 32]
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
if model_name == 'ResNet20':
predict = resnet_cifar(images, 20, num_class)
elif model_name == 'ResNet32':
predict = resnet_cifar(images, 32, num_class)
elif model_name == 'ResNet110':
predict = resnet_cifar(images, 110, num_class)
else:
predict = NASCifarNet(images, 36, 6, 3, num_class, Networks[model_name], True)
return predict
def train_program(predict):
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
if isinstance(predict, (list, tuple)):
predict, aux_predict = predict
x_losses = fluid.layers.cross_entropy(input=predict, label=label)
aux_losses = fluid.layers.cross_entropy(input=aux_predict, label=label)
x_loss = fluid.layers.mean(x_losses)
aux_loss = fluid.layers.mean(aux_losses)
loss = x_loss + aux_loss * 0.4
accuracy = fluid.layers.accuracy(input=predict, label=label)
else:
losses = fluid.layers.cross_entropy(input=predict, label=label)
loss = fluid.layers.mean(losses)
accuracy = fluid.layers.accuracy(input=predict, label=label)
return [loss, accuracy]
# For training test cost
def evaluation(program, reader, fetch_list, place):
feed_var_list = [program.global_block().var('pixel'), program.global_block().var('label')]
feeder_test = fluid.DataFeeder(feed_list=feed_var_list, place=place)
test_exe = fluid.Executor(place)
losses, accuracies = AverageMeter(), AverageMeter()
for tid, test_data in enumerate(reader()):
loss, acc = test_exe.run(program=program, feed=feeder_test.feed(test_data), fetch_list=fetch_list)
losses.update(float(loss), len(test_data))
accuracies.update(float(acc)*100, len(test_data))
return losses.avg, accuracies.avg
def cosine_decay_with_warmup(learning_rate, step_each_epoch, epochs=120):
"""Applies cosine decay to the learning rate.
lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
decrease lr for every mini-batch and start with warmup.
"""
from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
from paddle.fluid.initializer import init_on_cpu
global_step = _decay_step_counter()
lr = fluid.layers.tensor.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
warmup_epoch = fluid.layers.fill_constant(
shape=[1], dtype='float32', value=float(5), force_cpu=True)
with init_on_cpu():
epoch = ops.floor(global_step / step_each_epoch)
with fluid.layers.control_flow.Switch() as switch:
with switch.case(epoch < warmup_epoch):
decayed_lr = learning_rate * (global_step / (step_each_epoch * warmup_epoch))
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
with switch.default():
decayed_lr = learning_rate * \
(ops.cos((global_step - warmup_epoch * step_each_epoch) * (math.pi / (epochs * step_each_epoch))) + 1)/2
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
return lr
def main(xargs):
save_dir = Path(xargs.log_dir) / time_for_file()
save_dir.mkdir(parents=True, exist_ok=True)
print ('save dir : {:}'.format(save_dir))
print ('xargs : {:}'.format(xargs))
if xargs.dataset == 'cifar-10':
train_data = reader_creator(xargs.data_path, 'data_batch', True , False)
test__data = reader_creator(xargs.data_path, 'test_batch', False, False)
class_num = 10
print ('create cifar-10 dataset')
elif xargs.dataset == 'cifar-100':
train_data = reader_creator(xargs.data_path, 'train', True , False)
test__data = reader_creator(xargs.data_path, 'test' , False, False)
class_num = 100
print ('create cifar-100 dataset')
else:
raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
train_reader = paddle.batch(
paddle.reader.shuffle(train_data, buf_size=5000),
batch_size=xargs.batch_size)
# Reader for testing. A separated data set for testing.
test_reader = paddle.batch(test__data, batch_size=xargs.batch_size)
place = fluid.CUDAPlace(0)
main_program = fluid.default_main_program()
star_program = fluid.default_startup_program()
# programs
predict = inference_program(xargs.model_name, class_num)
[loss, accuracy] = train_program(predict)
print ('training program setup done')
test_program = main_program.clone(for_test=True)
print ('testing program setup done')
#infer_writer = SummaryWriter( str(save_dir / 'infer') )
#infer_writer.add_paddle_graph(fluid_program=fluid.default_main_program(), verbose=True)
#infer_writer.close()
#print(test_program.to_string(True))
#learning_rate = fluid.layers.cosine_decay(learning_rate=xargs.lr, step_each_epoch=xargs.step_each_epoch, epochs=xargs.epochs)
#learning_rate = fluid.layers.cosine_decay(learning_rate=0.1, step_each_epoch=196, epochs=300)
learning_rate = cosine_decay_with_warmup(xargs.lr, xargs.step_each_epoch, xargs.epochs)
optimizer = fluid.optimizer.Momentum(
learning_rate=learning_rate,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(0.0005),
use_nesterov=True)
optimizer.minimize( loss )
exe = fluid.Executor(place)
feed_var_list_loop = [main_program.global_block().var('pixel'), main_program.global_block().var('label')]
feeder = fluid.DataFeeder(feed_list=feed_var_list_loop, place=place)
exe.run(star_program)
start_time, epoch_time = time.time(), AverageMeter()
for iepoch in range(xargs.epochs):
losses, accuracies, steps = AverageMeter(), AverageMeter(), 0
for step_id, train_data in enumerate(train_reader()):
tloss, tacc, xlr = exe.run(main_program, feed=feeder.feed(train_data), fetch_list=[loss, accuracy, learning_rate])
tloss, tacc, xlr = float(tloss), float(tacc) * 100, float(xlr)
steps += 1
losses.update(tloss, len(train_data))
accuracies.update(tacc, len(train_data))
if step_id % 100 == 0:
print('{:} [{:03d}/{:03d}] [{:03d}] lr = {:.7f}, loss = {:.4f} ({:.4f}), accuracy = {:.2f} ({:.2f}), error={:.2f}'.format(time_string(), iepoch, xargs.epochs, step_id, xlr, tloss, losses.avg, tacc, accuracies.avg, 100-accuracies.avg))
test_loss, test_acc = evaluation(test_program, test_reader, [loss, accuracy], place)
need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.avg * (xargs.epochs-iepoch), True) )
print('{:}x[{:03d}/{:03d}] {:} train-loss = {:.4f}, train-accuracy = {:.2f}, test-loss = {:.4f}, test-accuracy = {:.2f} test-error = {:.2f} [{:} steps per epoch]\n'.format(time_string(), iepoch, xargs.epochs, need_time, losses.avg, accuracies.avg, test_loss, test_acc, 100-test_acc, steps))
if isinstance(predict, list):
fluid.io.save_inference_model(str(save_dir / 'inference_model'), ["pixel"], predict, exe)
else:
fluid.io.save_inference_model(str(save_dir / 'inference_model'), ["pixel"], [predict], exe)
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
print('finish training and evaluation with {:} epochs in {:}'.format(xargs.epochs, convert_secs2time(epoch_time.sum, True)))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Train.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--log_dir' , type=str, help='Save dir.')
parser.add_argument('--dataset', type=str, help='The dataset name.')
parser.add_argument('--data_path', type=str, help='The dataset path.')
parser.add_argument('--model_name', type=str, help='The model name.')
parser.add_argument('--lr', type=float, help='The learning rate.')
parser.add_argument('--batch_size', type=int, help='The batch size.')
parser.add_argument('--step_each_epoch',type=int, help='The batch size.')
parser.add_argument('--epochs' , type=int, help='The total training epochs.')
args = parser.parse_args()
main(args)

6
scripts-search/algos/DARTS-V1.sh
View File

@ -19,6 +19,7 @@ seed=$2
channel=16 channel=16
num_cells=5 num_cells=5
max_nodes=4 max_nodes=4
space=nas-bench-102
if [ "$dataset" == "cifar10" ] || [ "$dataset" == "cifar100" ]; then if [ "$dataset" == "cifar10" ] || [ "$dataset" == "cifar100" ]; then
data_path="$TORCH_HOME/cifar.python" data_path="$TORCH_HOME/cifar.python"
@ -26,11 +27,12 @@ else
data_path="$TORCH_HOME/cifar.python/ImageNet16" data_path="$TORCH_HOME/cifar.python/ImageNet16"
fi fi
save_dir=./output/cell-search-tiny/DARTS-V1-${dataset} save_dir=./output/search-cell-${space}/DARTS-V1-${dataset}
OMP_NUM_THREADS=4 python ./exps/algos/DARTS-V1.py \ OMP_NUM_THREADS=4 python ./exps/algos/DARTS-V1.py \
--save_dir ${save_dir} --max_nodes ${max_nodes} --channel ${channel} --num_cells ${num_cells} \ --save_dir ${save_dir} --max_nodes ${max_nodes} --channel ${channel} --num_cells ${num_cells} \
--dataset ${dataset} --data_path ${data_path} \ --dataset ${dataset} --data_path ${data_path} \
--search_space_name aa-nas \ --search_space_name ${space} \
--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
--arch_learning_rate 0.0003 --arch_weight_decay 0.001 \ --arch_learning_rate 0.0003 --arch_weight_decay 0.001 \
--workers 4 --print_freq 200 --rand_seed ${seed} --workers 4 --print_freq 200 --rand_seed ${seed}

4
scripts-search/algos/R-EA.sh
View File

@ -32,7 +32,7 @@ save_dir=./output/cell-search-tiny/R-EA-${dataset}
OMP_NUM_THREADS=4 python ./exps/algos/R_EA.py \ OMP_NUM_THREADS=4 python ./exps/algos/R_EA.py \
--save_dir ${save_dir} --max_nodes ${max_nodes} --channel ${channel} --num_cells ${num_cells} \ --save_dir ${save_dir} --max_nodes ${max_nodes} --channel ${channel} --num_cells ${num_cells} \
--dataset ${dataset} --data_path ${data_path} \ --dataset ${dataset} --data_path ${data_path} \
--search_space_name aa-nas \ --search_space_name nas-bench-102 \
--arch_nas_dataset ./output/AA-NAS-BENCH-4/simplifies/C16-N5-final-infos.pth \ --arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
--ea_cycles 30 --ea_population 10 --ea_sample_size 3 --ea_fast_by_api 1 \ --ea_cycles 30 --ea_population 10 --ea_sample_size 3 --ea_fast_by_api 1 \
--workers 4 --print_freq 200 --rand_seed ${seed} --workers 4 --print_freq 200 --rand_seed ${seed}