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simplify DARTS codes and update affine/track

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This commit is contained in:
D-X-Y 2020-01-11 18:46:31 +11:00
parent c66afa4df8
commit 654015bf9d
15 changed files with 30 additions and 110 deletions
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1
NAS-Bench-102.md
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@ -194,6 +194,7 @@ If you find that NAS-Bench-102 helps your research, please consider citing it:
title = {NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {International Conference on Learning Representations (ICLR)},
url = {https://openreview.net/forum?id=HJxyZkBKDr},
year = {2020}
}
```

3
README.md
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@ -15,7 +15,7 @@ More NAS resources can be found in [Awesome-NAS](https://github.com/D-X-Y/Awesom
Please install `PyTorch>=1.2.0`, `Python>=3.6`, and `opencv`.
The CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.
CIFAR and ImageNet should be downloaded and extracted into `$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`.
### Usefull tools
@ -150,6 +150,7 @@ If you find that this project helps your research, please consider citing some o
title = {NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {International Conference on Learning Representations (ICLR)},
url = {https://openreview.net/forum?id=HJxyZkBKDr},
year = {2020}
}
@inproceedings{dong2019tas,

4
exps/algos/DARTS-V1.py
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@ -114,7 +114,8 @@ def main(xargs):
search_space = get_search_spaces('cell', xargs.search_space_name)
model_config = dict2config({'name': 'DARTS-V1', 'C': xargs.channel, 'N': xargs.num_cells,
'max_nodes': xargs.max_nodes, 'num_classes': class_num,
'space' : search_space}, None)
'space' : search_space,
'affine' : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
search_model = get_cell_based_tiny_net(model_config)
logger.log('search-model :\n{:}'.format(search_model))
@ -217,6 +218,7 @@ if __name__ == '__main__':
parser.add_argument('--max_nodes', type=int, help='The maximum number of nodes.')
parser.add_argument('--channel', type=int, help='The number of channels.')
parser.add_argument('--num_cells', type=int, help='The number of cells in one stage.')
parser.add_argument('--track_running_stats',type=int, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
# architecture leraning rate
parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')

4
exps/algos/DARTS-V2.py
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@ -177,7 +177,8 @@ def main(xargs):
search_space = get_search_spaces('cell', xargs.search_space_name)
model_config = dict2config({'name': 'DARTS-V2', 'C': xargs.channel, 'N': xargs.num_cells,
'max_nodes': xargs.max_nodes, 'num_classes': class_num,
'space' : search_space}, None)
'space' : search_space,
'affine' : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
search_model = get_cell_based_tiny_net(model_config)
logger.log('search-model :\n{:}'.format(search_model))
@ -282,6 +283,7 @@ if __name__ == '__main__':
parser.add_argument('--max_nodes', type=int, help='The maximum number of nodes.')
parser.add_argument('--channel', type=int, help='The number of channels.')
parser.add_argument('--num_cells', type=int, help='The number of cells in one stage.')
parser.add_argument('--track_running_stats',type=int, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
# architecture leraning rate
parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')

4
exps/algos/ENAS.py
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@ -198,7 +198,8 @@ def main(xargs):
search_space = get_search_spaces('cell', xargs.search_space_name)
model_config = dict2config({'name': 'ENAS', 'C': xargs.channel, 'N': xargs.num_cells,
'max_nodes': xargs.max_nodes, 'num_classes': class_num,
'space' : search_space}, None)
'space' : search_space,
'affine' : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
shared_cnn = get_cell_based_tiny_net(model_config)
controller = shared_cnn.create_controller()
@ -319,6 +320,7 @@ 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('--track_running_stats',type=int, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
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/RANDOM-NAS.py
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@ -126,7 +126,8 @@ def main(xargs):
search_space = get_search_spaces('cell', xargs.search_space_name)
model_config = dict2config({'name': 'RANDOM', 'C': xargs.channel, 'N': xargs.num_cells,
'max_nodes': xargs.max_nodes, 'num_classes': class_num,
'space' : search_space}, None)
'space' : search_space,
'affine' : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
search_model = get_cell_based_tiny_net(model_config)
w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.parameters(), config)
@ -222,6 +223,7 @@ if __name__ == '__main__':
parser.add_argument('--channel', type=int, help='The number of channels.')
parser.add_argument('--num_cells', type=int, help='The number of cells in one stage.')
parser.add_argument('--select_num', type=int, help='The number of selected architectures to evaluate.')
parser.add_argument('--track_running_stats',type=int, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
# log
parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.')

7
lib/models/cell_searchs/__init__.py
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@ -1,16 +1,15 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .search_model_darts_v1 import TinyNetworkDartsV1
from .search_model_darts_v2 import TinyNetworkDartsV2
from .search_model_darts import TinyNetworkDarts
from .search_model_gdas import TinyNetworkGDAS
from .search_model_setn import TinyNetworkSETN
from .search_model_enas import TinyNetworkENAS
from .search_model_random import TinyNetworkRANDOM
from .genotypes import Structure as CellStructure, architectures as CellArchitectures
nas_super_nets = {'DARTS-V1': TinyNetworkDartsV1,
'DARTS-V2': TinyNetworkDartsV2,
nas_super_nets = {'DARTS-V1': TinyNetworkDarts,
'DARTS-V2': TinyNetworkDarts,
'GDAS' : TinyNetworkGDAS,
'SETN' : TinyNetworkSETN,
'ENAS' : TinyNetworkENAS,

8
lib/models/cell_searchs/search_model_darts_v1.py → lib/models/cell_searchs/search_model_darts.py
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@ -11,10 +11,10 @@ from .search_cells import SearchCell
from .genotypes import Structure
class TinyNetworkDartsV1(nn.Module):
class TinyNetworkDarts(nn.Module):
def __init__(self, C, N, max_nodes, num_classes, search_space):
super(TinyNetworkDartsV1, self).__init__()
def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
super(TinyNetworkDarts, self).__init__()
self._C = C
self._layerN = N
self.max_nodes = max_nodes
@ -31,7 +31,7 @@ class TinyNetworkDartsV1(nn.Module):
if reduction:
cell = ResNetBasicblock(C_prev, C_curr, 2)
else:
cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space, affine, track_running_stats)
if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
self.cells.append( cell )

93
lib/models/cell_searchs/search_model_darts_v2.py
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@ -1,93 +0,0 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
########################################################
# DARTS: Differentiable Architecture Search, ICLR 2019 #
########################################################
import torch
import torch.nn as nn
from copy import deepcopy
from ..cell_operations import ResNetBasicblock
from .search_cells import SearchCell
from .genotypes import Structure
class TinyNetworkDartsV2(nn.Module):
def __init__(self, C, N, max_nodes, num_classes, search_space):
super(TinyNetworkDartsV2, self).__init__()
self._C = C
self._layerN = N
self.max_nodes = max_nodes
self.stem = nn.Sequential(
nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(C))
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, num_edge, edge2index = C, None, None
self.cells = nn.ModuleList()
for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
if reduction:
cell = ResNetBasicblock(C_prev, C_curr, 2)
else:
cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
self.cells.append( cell )
C_prev = cell.out_dim
self.op_names = deepcopy( search_space )
self._Layer = len(self.cells)
self.edge2index = edge2index
self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
self.global_pooling = nn.AdaptiveAvgPool2d(1)
self.classifier = nn.Linear(C_prev, num_classes)
self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
def get_weights(self):
xlist = list( self.stem.parameters() ) + list( self.cells.parameters() )
xlist+= list( self.lastact.parameters() ) + list( self.global_pooling.parameters() )
xlist+= list( self.classifier.parameters() )
return xlist
def get_alphas(self):
return [self.arch_parameters]
def get_message(self):
string = self.extra_repr()
for i, cell in enumerate(self.cells):
string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self.cells), cell.extra_repr())
return string
def extra_repr(self):
return ('{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
def genotype(self):
genotypes = []
for i in range(1, self.max_nodes):
xlist = []
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
with torch.no_grad():
weights = self.arch_parameters[ self.edge2index[node_str] ]
op_name = self.op_names[ weights.argmax().item() ]
xlist.append((op_name, j))
genotypes.append( tuple(xlist) )
return Structure( genotypes )
def forward(self, inputs):
alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
feature = self.stem(inputs)
for i, cell in enumerate(self.cells):
if isinstance(cell, SearchCell):
feature = cell(feature, alphas)
else:
feature = cell(feature)
out = self.lastact(feature)
out = self.global_pooling( out )
out = out.view(out.size(0), -1)
logits = self.classifier(out)
return out, logits

4
lib/models/cell_searchs/search_model_enas.py
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@ -14,7 +14,7 @@ from .search_model_enas_utils import Controller
class TinyNetworkENAS(nn.Module):
def __init__(self, C, N, max_nodes, num_classes, search_space):
def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
super(TinyNetworkENAS, self).__init__()
self._C = C
self._layerN = N
@ -32,7 +32,7 @@ class TinyNetworkENAS(nn.Module):
if reduction:
cell = ResNetBasicblock(C_prev, C_curr, 2)
else:
cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space, affine, track_running_stats)
if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
self.cells.append( cell )

4
lib/models/cell_searchs/search_model_random.py
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@ -13,7 +13,7 @@ from .genotypes import Structure
class TinyNetworkRANDOM(nn.Module):
def __init__(self, C, N, max_nodes, num_classes, search_space):
def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
super(TinyNetworkRANDOM, self).__init__()
self._C = C
self._layerN = N
@ -31,7 +31,7 @@ class TinyNetworkRANDOM(nn.Module):
if reduction:
cell = ResNetBasicblock(C_prev, C_curr, 2)
else:
cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space, affine, track_running_stats)
if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
self.cells.append( cell )

1
scripts-search/algos/DARTS-V1.sh
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@ -35,5 +35,6 @@ OMP_NUM_THREADS=4 python ./exps/algos/DARTS-V1.py \
--search_space_name ${space} \
--config_path configs/nas-benchmark/algos/DARTS.config \
--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
--track_running_stats 1 \
--arch_learning_rate 0.0003 --arch_weight_decay 0.001 \
--workers 4 --print_freq 200 --rand_seed ${seed}

1
scripts-search/algos/DARTS-V2.sh
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@ -35,5 +35,6 @@ OMP_NUM_THREADS=4 python ./exps/algos/DARTS-V2.py \
--search_space_name ${space} \
--config_path configs/nas-benchmark/algos/DARTS.config \
--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
--track_running_stats 1 \
--arch_learning_rate 0.0003 --arch_weight_decay 0.001 \
--workers 4 --print_freq 200 --rand_seed ${seed}

1
scripts-search/algos/ENAS.sh
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@ -35,6 +35,7 @@ OMP_NUM_THREADS=4 python ./exps/algos/ENAS.py \
--dataset ${dataset} --data_path ${data_path} \
--search_space_name ${space} \
--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
--track_running_stats 1 \
--config_path ./configs/nas-benchmark/algos/ENAS.config \
--controller_entropy_weight 0.0001 \
--controller_bl_dec 0.99 \

1
scripts-search/algos/RANDOM-NAS.sh
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@ -34,6 +34,7 @@ OMP_NUM_THREADS=4 python ./exps/algos/RANDOM-NAS.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} \
--track_running_stats 1 \
--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
--config_path ./configs/nas-benchmark/algos/RANDOM.config \
--select_num 100 \