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102->201 / NAS->autoDL / more configs of TAS / reorganize docs / fix bugs in NAS baselines

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This commit is contained in:
D-X-Y
2020-01-15 00:52:06 +11:00
parent 33384a78af
commit bb2f405961
62 changed files with 789 additions and 412 deletions
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2
lib/config_utils/basic_args.py
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@@ -1,5 +1,5 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
##################################################
import os, sys, time, random, argparse
from .share_args import add_shared_args

2
lib/models/__init__.py
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@@ -19,7 +19,7 @@ def get_cell_based_tiny_net(config):
super_type = getattr(config, 'super_type', 'basic')
group_names = ['DARTS-V1', 'DARTS-V2', 'GDAS', 'SETN', 'ENAS', 'RANDOM']
if super_type == 'basic' and config.name in group_names:
from .cell_searchs import nas102_super_nets as nas_super_nets
from .cell_searchs import nas201_super_nets as nas_super_nets
try:
return nas_super_nets[config.name](config.C, config.N, config.max_nodes, config.num_classes, config.space, config.affine, config.track_running_stats)
except:

5
lib/models/cell_infers/cells.py
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@@ -1,8 +1,13 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
##################################################
import torch.nn as nn
from copy import deepcopy
from ..cell_operations import OPS
# Cell for NAS-Bench-201
class InferCell(nn.Module):
def __init__(self, genotype, C_in, C_out, stride):

4
lib/models/cell_infers/tiny_network.py
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@@ -1,9 +1,13 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
##################################################
import torch
import torch.nn as nn
from ..cell_operations import ResNetBasicblock
from .cells import InferCell
# The macro structure for architectures in NAS-Bench-201
class TinyNetwork(nn.Module):
def __init__(self, C, N, genotype, num_classes):

5
lib/models/cell_operations.py
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@@ -21,12 +21,11 @@ OPS = {
}
CONNECT_NAS_BENCHMARK = ['none', 'skip_connect', 'nor_conv_3x3']
NAS_BENCH_102 = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']
NAS_BENCH_201 = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']
DARTS_SPACE = ['none', 'skip_connect', 'dua_sepc_3x3', 'dua_sepc_5x5', 'dil_sepc_3x3', 'dil_sepc_5x5', 'avg_pool_3x3', 'max_pool_3x3']
SearchSpaceNames = {'connect-nas' : CONNECT_NAS_BENCHMARK,
'aa-nas' : NAS_BENCH_102,
'nas-bench-102': NAS_BENCH_102,
'nas-bench-201': NAS_BENCH_201,
'darts' : DARTS_SPACE}

4
lib/models/cell_searchs/__init__.py
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@@ -1,7 +1,7 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
# The macro structure is defined in NAS-Bench-102
# The macro structure is defined in NAS-Bench-201
from .search_model_darts import TinyNetworkDarts
from .search_model_gdas import TinyNetworkGDAS
from .search_model_setn import TinyNetworkSETN
@@ -12,7 +12,7 @@ from .genotypes import Structure as CellStructure, architectures as
from .search_model_gdas_nasnet import NASNetworkGDAS
nas102_super_nets = {'DARTS-V1': TinyNetworkDarts,
nas201_super_nets = {'DARTS-V1': TinyNetworkDarts,
'DARTS-V2': TinyNetworkDarts,
'GDAS' : TinyNetworkGDAS,
'SETN' : TinyNetworkSETN,

6
lib/models/cell_searchs/search_cells.py
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@@ -9,11 +9,11 @@ from copy import deepcopy
from ..cell_operations import OPS
# This module is used for NAS-Bench-102, represents a small search space with a complete DAG
class NAS102SearchCell(nn.Module):
# This module is used for NAS-Bench-201, represents a small search space with a complete DAG
class NAS201SearchCell(nn.Module):
def __init__(self, C_in, C_out, stride, max_nodes, op_names, affine=False, track_running_stats=True):
super(NAS102SearchCell, self).__init__()
super(NAS201SearchCell, self).__init__()
self.op_names = deepcopy(op_names)
self.edges = nn.ModuleDict()

2
lib/models/cell_searchs/search_model_darts.py
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@@ -7,7 +7,7 @@ import torch
import torch.nn as nn
from copy import deepcopy
from ..cell_operations import ResNetBasicblock
from .search_cells import NAS102SearchCell as SearchCell
from .search_cells import NAS201SearchCell as SearchCell
from .genotypes import Structure

2
lib/models/cell_searchs/search_model_enas.py
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@@ -7,7 +7,7 @@ import torch
import torch.nn as nn
from copy import deepcopy
from ..cell_operations import ResNetBasicblock
from .search_cells import NAS102SearchCell as SearchCell
from .search_cells import NAS201SearchCell as SearchCell
from .genotypes import Structure
from .search_model_enas_utils import Controller

2
lib/models/cell_searchs/search_model_gdas.py
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@@ -5,7 +5,7 @@ import torch
import torch.nn as nn
from copy import deepcopy
from ..cell_operations import ResNetBasicblock
from .search_cells import NAS102SearchCell as SearchCell
from .search_cells import NAS201SearchCell as SearchCell
from .genotypes import Structure

2
lib/models/cell_searchs/search_model_random.py
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@@ -7,7 +7,7 @@ import torch, random
import torch.nn as nn
from copy import deepcopy
from ..cell_operations import ResNetBasicblock
from .search_cells import NAS102SearchCell as SearchCell
from .search_cells import NAS201SearchCell as SearchCell
from .genotypes import Structure

2
lib/models/cell_searchs/search_model_setn.py
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@@ -7,7 +7,7 @@ import torch, random
import torch.nn as nn
from copy import deepcopy
from ..cell_operations import ResNetBasicblock
from .search_cells import NAS102SearchCell as SearchCell
from .search_cells import NAS201SearchCell as SearchCell
from .genotypes import Structure

4
lib/nas_102_api/__init__.py → lib/nas_201_api/__init__.py
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@@ -1,7 +1,7 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .api import NASBench102API
from .api import NASBench201API
from .api import ArchResults, ResultsCount
NAS_BENCH_102_API_VERSION="v1.0"
NAS_BENCH_201_API_VERSION="v1.0"

8
lib/nas_102_api/api.py → lib/nas_201_api/api.py
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@@ -1,9 +1,9 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
############################################################################################
# NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search, ICLR 2020 #
# NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search, ICLR 2020 #
############################################################################################
# NAS-Bench-102-v1_0-e61699.pth : 6219 architectures are trained once, 1621 architectures are trained twice, 7785 architectures are trained three times. `LESS` only supports CIFAR10-VALID.
# NAS-Bench-201-v1_0-e61699.pth : 6219 architectures are trained once, 1621 architectures are trained twice, 7785 architectures are trained three times. `LESS` only supports CIFAR10-VALID.
#
#
#
@@ -38,11 +38,11 @@ def print_information(information, extra_info=None, show=False):
return strings
class NASBench102API(object):
class NASBench201API(object):
def __init__(self, file_path_or_dict, verbose=True):
if isinstance(file_path_or_dict, str):
if verbose: print('try to create the NAS-Bench-102 api from {:}'.format(file_path_or_dict))
if verbose: print('try to create the NAS-Bench-201 api from {:}'.format(file_path_or_dict))
assert os.path.isfile(file_path_or_dict), 'invalid path : {:}'.format(file_path_or_dict)
file_path_or_dict = torch.load(file_path_or_dict)
elif isinstance(file_path_or_dict, dict):

1
lib/nas_infer_model/DXYs/__init__.py
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@@ -2,3 +2,4 @@
from .CifarNet import NetworkCIFAR as CifarNet
from .ImageNet import NetworkImageNet as ImageNet
from .genotypes import Networks
from .genotypes import build_genotype_from_dict

3
lib/nas_infer_model/DXYs/genotypes.py
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@@ -167,3 +167,6 @@ Networks = {'DARTS_V1': DARTS_V1,
'PNASNet' : PNASNet,
'SETN' : SETN,
}
def build_genotype_from_dict(xdict):
import pdb; pdb.set_trace()

7
lib/nas_infer_model/__init__.py
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@@ -1,6 +1,11 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
##################################################
# I write this package to make AutoDL-Projects to be compatible with the old GDAS projects.
# Ideally, this package will be merged into lib/models/cell_infers in future.
# Currently, this package is used to reproduce the results in GDAS (Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019).
##################################################
import torch
def obtain_nas_infer_model(config):

4
lib/tf_models/cell_operations.py
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@@ -14,10 +14,10 @@ OPS = {
'skip_connect': lambda C_in, C_out, stride, affine: Identity(C_in, C_out, stride)
}
NAS_BENCH_102 = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']
NAS_BENCH_201 = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']
SearchSpaceNames = {
'nas-bench-102': NAS_BENCH_102,
'nas-bench-201': NAS_BENCH_201,
}