Support GDAS (FRC), see details in docs/CVPR-2019-GDAS.md
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9
configs/search-archs/GDASFRC-NASNet-CIFAR.config
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9
configs/search-archs/GDASFRC-NASNet-CIFAR.config
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@ -0,0 +1,9 @@
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{
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"super_type" : ["str", "nasnet-super"],
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"name" : ["str", "GDAS_FRC"],
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"C" : ["int", "16" ],
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"N" : ["int", "2" ],
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"steps" : ["int", "4" ],
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"multiplier" : ["int", "4" ],
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"stem_multiplier" : ["int", "3" ]
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}
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@ -37,9 +37,14 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k GDAS_
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If you are interested in the configs of each NAS-searched architecture, they are defined at [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py).
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If you are interested in the configs of each NAS-searched architecture, they are defined at [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py).
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### Searching on the NASNet search space
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### Searching on the NASNet search space
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Please use the following scripts to use GDAS to search as in the original paper:
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Please use the following scripts to use GDAS to search as in the original paper:
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```
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```
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# search for both normal and reduction cells
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/NASNet-space-search-by-GDAS.sh cifar10 1 -1
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/NASNet-space-search-by-GDAS.sh cifar10 1 -1
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# search for the normal cell while use a fixed reduction cell
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/NASNet-space-search-by-GDAS-FRC.sh cifar10 1 -1
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```
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```
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**After searching**, if you want to re-train the searched architecture found by the above script, you can use the following script:
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**After searching**, if you want to re-train the searched architecture found by the above script, you can use the following script:
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@ -52,7 +57,9 @@ Note that `gdas-searched` is a string to indicate the name of the saved dir and
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The above script does not apply heavy augmentation to train the model, so the accuracy will be lower than the original paper.
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The above script does not apply heavy augmentation to train the model, so the accuracy will be lower than the original paper.
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If you want to change the default hyper-parameter for re-training, please have a look at `./scripts/retrain-searched-net.sh` and `configs/archs/NAS-*-none.config`.
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If you want to change the default hyper-parameter for re-training, please have a look at `./scripts/retrain-searched-net.sh` and `configs/archs/NAS-*-none.config`.
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### Searching on a small search space (NAS-Bench-201)
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### Searching on a small search space (NAS-Bench-201)
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The GDAS searching codes on a small search space:
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The GDAS searching codes on a small search space:
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```
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```
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh cifar10 1 -1
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh cifar10 1 -1
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@ -4,7 +4,7 @@
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import torch
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import torch
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import torch.nn as nn
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import torch.nn as nn
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__all__ = ['OPS', 'ResNetBasicblock', 'SearchSpaceNames']
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__all__ = ['OPS', 'RAW_OP_CLASSES', 'ResNetBasicblock', 'SearchSpaceNames']
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OPS = {
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OPS = {
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'none' : lambda C_in, C_out, stride, affine, track_running_stats: Zero(C_in, C_out, stride),
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'none' : lambda C_in, C_out, stride, affine, track_running_stats: Zero(C_in, C_out, stride),
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@ -175,7 +175,7 @@ class FactorizedReduce(nn.Module):
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self.convs.append(nn.Conv2d(C_in, C_outs[i], 1, stride=stride, padding=0, bias=not affine))
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self.convs.append(nn.Conv2d(C_in, C_outs[i], 1, stride=stride, padding=0, bias=not affine))
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self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
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self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
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elif stride == 1:
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elif stride == 1:
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self.conv = nn.Conv2d(C_in, C_out, 1, stride=stride, padding=0, bias=False)
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self.conv = nn.Conv2d(C_in, C_out, 1, stride=stride, padding=0, bias=not affine)
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else:
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else:
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raise ValueError('Invalid stride : {:}'.format(stride))
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raise ValueError('Invalid stride : {:}'.format(stride))
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self.bn = nn.BatchNorm2d(C_out, affine=affine, track_running_stats=track_running_stats)
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self.bn = nn.BatchNorm2d(C_out, affine=affine, track_running_stats=track_running_stats)
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@ -256,41 +256,44 @@ def drop_path(x, drop_prob):
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# Searching for A Robust Neural Architecture in Four GPU Hours
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# Searching for A Robust Neural Architecture in Four GPU Hours
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class GDAS_Reduction_Cell(nn.Module):
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class GDAS_Reduction_Cell(nn.Module):
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def __init__(self, C_prev_prev, C_prev, C, reduction_prev, multiplier, affine, track_running_stats):
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def __init__(self, C_prev_prev, C_prev, C, reduction_prev, affine, track_running_stats):
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super(GDAS_Reduction_Cell, self).__init__()
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super(GDAS_Reduction_Cell, self).__init__()
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if reduction_prev:
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if reduction_prev:
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self.preprocess0 = FactorizedReduce(C_prev_prev, C, 2, affine, track_running_stats)
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self.preprocess0 = FactorizedReduce(C_prev_prev, C, 2, affine, track_running_stats)
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else:
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else:
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self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, 1, affine, track_running_stats)
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self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, 1, affine, track_running_stats)
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self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, 1, affine, track_running_stats)
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self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, 1, affine, track_running_stats)
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self.multiplier = multiplier
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self.reduction = True
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self.reduction = True
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self.ops1 = nn.ModuleList(
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self.ops1 = nn.ModuleList(
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[nn.Sequential(
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[nn.Sequential(
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nn.ReLU(inplace=False),
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False),
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nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=not affine),
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nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False),
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nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=not affine),
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nn.BatchNorm2d(C, affine=True),
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nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats),
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nn.ReLU(inplace=False),
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False),
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nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine),
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nn.BatchNorm2d(C, affine=True)),
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nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats)),
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nn.Sequential(
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nn.Sequential(
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nn.ReLU(inplace=False),
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False),
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nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=not affine),
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nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False),
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nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=not affine),
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nn.BatchNorm2d(C, affine=True),
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nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats),
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nn.ReLU(inplace=False),
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False),
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nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine),
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nn.BatchNorm2d(C, affine=True))])
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nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats))])
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self.ops2 = nn.ModuleList(
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self.ops2 = nn.ModuleList(
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[nn.Sequential(
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[nn.Sequential(
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nn.MaxPool2d(3, stride=1, padding=1),
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nn.MaxPool2d(3, stride=2, padding=1),
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nn.BatchNorm2d(C, affine=True)),
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nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats)),
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nn.Sequential(
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nn.Sequential(
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nn.MaxPool2d(3, stride=2, padding=1),
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nn.MaxPool2d(3, stride=2, padding=1),
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nn.BatchNorm2d(C, affine=True))])
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nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats))])
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@property
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def multiplier(self):
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return 4
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def forward(self, s0, s1, drop_prob = -1):
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def forward(self, s0, s1, drop_prob = -1):
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s0 = self.preprocess0(s0)
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s0 = self.preprocess0(s0)
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@ -307,3 +310,10 @@ class GDAS_Reduction_Cell(nn.Module):
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if self.training and drop_prob > 0.:
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if self.training and drop_prob > 0.:
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X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob)
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X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob)
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return torch.cat([X0, X1, X2, X3], dim=1)
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return torch.cat([X0, X1, X2, X3], dim=1)
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# To manage the useful classes in this file.
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RAW_OP_CLASSES = {
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'gdas_reduction': GDAS_Reduction_Cell
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}
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@ -11,6 +11,7 @@ from .generic_model import GenericNAS201Model
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from .genotypes import Structure as CellStructure, architectures as CellArchitectures
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from .genotypes import Structure as CellStructure, architectures as CellArchitectures
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# NASNet-based macro structure
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# NASNet-based macro structure
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from .search_model_gdas_nasnet import NASNetworkGDAS
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from .search_model_gdas_nasnet import NASNetworkGDAS
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from .search_model_gdas_frc_nasnet import NASNetworkGDAS_FRC
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from .search_model_darts_nasnet import NASNetworkDARTS
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from .search_model_darts_nasnet import NASNetworkDARTS
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@ -23,4 +24,5 @@ nas201_super_nets = {'DARTS-V1': TinyNetworkDarts,
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"generic": GenericNAS201Model}
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"generic": GenericNAS201Model}
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nasnet_super_nets = {"GDAS": NASNetworkGDAS,
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nasnet_super_nets = {"GDAS": NASNetworkGDAS,
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"GDAS_FRC": NASNetworkGDAS_FRC,
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"DARTS": NASNetworkDARTS}
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"DARTS": NASNetworkDARTS}
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@ -163,6 +163,10 @@ class NASNetSearchCell(nn.Module):
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self.edge2index = {key:i for i, key in enumerate(self.edge_keys)}
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self.edge2index = {key:i for i, key in enumerate(self.edge_keys)}
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self.num_edges = len(self.edges)
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self.num_edges = len(self.edges)
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@property
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def multiplier(self):
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return self._multiplier
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def forward_gdas(self, s0, s1, weightss, indexs):
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def forward_gdas(self, s0, s1, weightss, indexs):
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s0 = self.preprocess0(s0)
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s0 = self.preprocess0(s0)
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s1 = self.preprocess1(s1)
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s1 = self.preprocess1(s1)
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125
lib/models/cell_searchs/search_model_gdas_frc_nasnet.py
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125
lib/models/cell_searchs/search_model_gdas_frc_nasnet.py
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###########################################################################
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# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
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###########################################################################
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import torch
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import torch.nn as nn
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from copy import deepcopy
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from models.cell_searchs.search_cells import NASNetSearchCell as SearchCell
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from models.cell_operations import RAW_OP_CLASSES
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# The macro structure is based on NASNet
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class NASNetworkGDAS_FRC(nn.Module):
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def __init__(self, C, N, steps, multiplier, stem_multiplier, num_classes, search_space, affine, track_running_stats):
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super(NASNetworkGDAS_FRC, self).__init__()
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self._C = C
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self._layerN = N
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self._steps = steps
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self._multiplier = multiplier
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self.stem = nn.Sequential(
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nn.Conv2d(3, C*stem_multiplier, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(C*stem_multiplier))
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# config for each layer
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layer_channels = [C ] * N + [C*2 ] + [C*2 ] * (N-1) + [C*4 ] + [C*4 ] * (N-1)
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layer_reductions = [False] * N + [True] + [False] * (N-1) + [True] + [False] * (N-1)
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num_edge, edge2index = None, None
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C_prev_prev, C_prev, C_curr, reduction_prev = C*stem_multiplier, C*stem_multiplier, C, False
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self.cells = nn.ModuleList()
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for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
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if reduction:
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cell = RAW_OP_CLASSES['gdas_reduction'](C_prev_prev, C_prev, C_curr, reduction_prev, affine, track_running_stats)
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else:
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cell = SearchCell(search_space, steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, affine, track_running_stats)
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if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
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else: assert reduction or num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
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self.cells.append( cell )
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C_prev_prev, C_prev, reduction_prev = C_prev, cell.multiplier * C_curr, reduction
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self.op_names = deepcopy( search_space )
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self._Layer = len(self.cells)
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self.edge2index = edge2index
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self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
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self.global_pooling = nn.AdaptiveAvgPool2d(1)
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self.classifier = nn.Linear(C_prev, num_classes)
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self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
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self.tau = 10
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def get_weights(self):
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xlist = list( self.stem.parameters() ) + list( self.cells.parameters() )
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xlist+= list( self.lastact.parameters() ) + list( self.global_pooling.parameters() )
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xlist+= list( self.classifier.parameters() )
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return xlist
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def set_tau(self, tau):
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self.tau = tau
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def get_tau(self):
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return self.tau
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def get_alphas(self):
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return [self.arch_parameters]
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def show_alphas(self):
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with torch.no_grad():
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A = 'arch-normal-parameters :\n{:}'.format(nn.functional.softmax(self.arch_parameters, dim=-1).cpu())
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return '{:}'.format(A)
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def get_message(self):
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string = self.extra_repr()
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for i, cell in enumerate(self.cells):
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string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self.cells), cell.extra_repr())
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return string
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def extra_repr(self):
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return ('{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
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def genotype(self):
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def _parse(weights):
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gene = []
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for i in range(self._steps):
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edges = []
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for j in range(2+i):
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node_str = '{:}<-{:}'.format(i, j)
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ws = weights[ self.edge2index[node_str] ]
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for k, op_name in enumerate(self.op_names):
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if op_name == 'none': continue
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edges.append( (op_name, j, ws[k]) )
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edges = sorted(edges, key=lambda x: -x[-1])
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selected_edges = edges[:2]
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gene.append( tuple(selected_edges) )
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return gene
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with torch.no_grad():
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gene_normal = _parse(torch.softmax(self.arch_parameters, dim=-1).cpu().numpy())
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return {'normal': gene_normal, 'normal_concat': list(range(2+self._steps-self._multiplier, self._steps+2))}
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def forward(self, inputs):
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def get_gumbel_prob(xins):
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while True:
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gumbels = -torch.empty_like(xins).exponential_().log()
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logits = (xins.log_softmax(dim=1) + gumbels) / self.tau
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probs = nn.functional.softmax(logits, dim=1)
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index = probs.max(-1, keepdim=True)[1]
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one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0)
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hardwts = one_h - probs.detach() + probs
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if (torch.isinf(gumbels).any()) or (torch.isinf(probs).any()) or (torch.isnan(probs).any()):
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continue
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else: break
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return hardwts, index
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hardwts, index = get_gumbel_prob(self.arch_parameters)
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s0 = s1 = self.stem(inputs)
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for i, cell in enumerate(self.cells):
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||||||
|
if cell.reduction:
|
||||||
|
s0, s1 = s1, cell(s0, s1)
|
||||||
|
else:
|
||||||
|
s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index)
|
||||||
|
out = self.lastact(s1)
|
||||||
|
out = self.global_pooling( out )
|
||||||
|
out = out.view(out.size(0), -1)
|
||||||
|
logits = self.classifier(out)
|
||||||
|
|
||||||
|
return out, logits
|
38
scripts-search/NASNet-space-search-by-GDAS-FRC.sh
Normal file
38
scripts-search/NASNet-space-search-by-GDAS-FRC.sh
Normal file
@ -0,0 +1,38 @@
|
|||||||
|
#!/bin/bash
|
||||||
|
# bash ./scripts-search/NASNet-space-search-by-GDAS-FRC.sh cifar10 1 -1
|
||||||
|
echo script name: $0
|
||||||
|
echo $# arguments
|
||||||
|
if [ "$#" -ne 3 ] ;then
|
||||||
|
echo "Input illegal number of parameters " $#
|
||||||
|
echo "Need 3 parameters for dataset, track_running_stats, and seed"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
if [ "$TORCH_HOME" = "" ]; then
|
||||||
|
echo "Must set TORCH_HOME envoriment variable for data dir saving"
|
||||||
|
exit 1
|
||||||
|
else
|
||||||
|
echo "TORCH_HOME : $TORCH_HOME"
|
||||||
|
fi
|
||||||
|
|
||||||
|
dataset=$1
|
||||||
|
track_running_stats=$2
|
||||||
|
seed=$3
|
||||||
|
space=darts
|
||||||
|
|
||||||
|
if [ "$dataset" == "cifar10" ] || [ "$dataset" == "cifar100" ]; then
|
||||||
|
data_path="$TORCH_HOME/cifar.python"
|
||||||
|
else
|
||||||
|
data_path="$TORCH_HOME/cifar.python/ImageNet16"
|
||||||
|
fi
|
||||||
|
|
||||||
|
save_dir=./output/search-cell-${space}/GDAS-${dataset}-BN${track_running_stats}
|
||||||
|
|
||||||
|
OMP_NUM_THREADS=4 python ./exps/algos/GDAS.py \
|
||||||
|
--save_dir ${save_dir} \
|
||||||
|
--dataset ${dataset} --data_path ${data_path} \
|
||||||
|
--search_space_name ${space} \
|
||||||
|
--config_path configs/search-opts/GDAS-NASNet-CIFAR.config \
|
||||||
|
--model_config configs/search-archs/GDASFRC-NASNet-CIFAR.config \
|
||||||
|
--tau_max 10 --tau_min 0.1 --track_running_stats ${track_running_stats} \
|
||||||
|
--arch_learning_rate 0.0003 --arch_weight_decay 0.001 \
|
||||||
|
--workers 4 --print_freq 200 --rand_seed ${seed}
|
Loading…
Reference in New Issue
Block a user