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Support GDAS (FRC), see details in docs/CVPR-2019-GDAS.md

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This commit is contained in:
D-X-Y
2020-08-18 00:50:33 +00:00
parent 75eefa3d44
commit ffd23a6cbd
7 changed files with 212 additions and 17 deletions
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44
lib/models/cell_operations.py
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@@ -4,7 +4,7 @@
import torch
import torch.nn as nn
__all__ = ['OPS', 'ResNetBasicblock', 'SearchSpaceNames']
__all__ = ['OPS', 'RAW_OP_CLASSES', 'ResNetBasicblock', 'SearchSpaceNames']
OPS = {
'none' : lambda C_in, C_out, stride, affine, track_running_stats: Zero(C_in, C_out, stride),
@@ -175,7 +175,7 @@ class FactorizedReduce(nn.Module):
self.convs.append(nn.Conv2d(C_in, C_outs[i], 1, stride=stride, padding=0, bias=not affine))
self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
elif stride == 1:
self.conv = nn.Conv2d(C_in, C_out, 1, stride=stride, padding=0, bias=False)
self.conv = nn.Conv2d(C_in, C_out, 1, stride=stride, padding=0, bias=not affine)
else:
raise ValueError('Invalid stride : {:}'.format(stride))
self.bn = nn.BatchNorm2d(C_out, affine=affine, track_running_stats=track_running_stats)
@@ -256,41 +256,44 @@ def drop_path(x, drop_prob):
# Searching for A Robust Neural Architecture in Four GPU Hours
class GDAS_Reduction_Cell(nn.Module):
def __init__(self, C_prev_prev, C_prev, C, reduction_prev, multiplier, affine, track_running_stats):
def __init__(self, C_prev_prev, C_prev, C, reduction_prev, affine, track_running_stats):
super(GDAS_Reduction_Cell, self).__init__()
if reduction_prev:
self.preprocess0 = FactorizedReduce(C_prev_prev, C, 2, affine, track_running_stats)
else:
self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, 1, affine, track_running_stats)
self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, 1, affine, track_running_stats)
self.multiplier = multiplier
self.reduction = True
self.ops1 = nn.ModuleList(
[nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False),
nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False),
nn.BatchNorm2d(C, affine=True),
nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=not affine),
nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=not affine),
nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats),
nn.ReLU(inplace=False),
nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(C, affine=True)),
nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine),
nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats)),
nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False),
nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False),
nn.BatchNorm2d(C, affine=True),
nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=not affine),
nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=not affine),
nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats),
nn.ReLU(inplace=False),
nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(C, affine=True))])
nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine),
nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats))])
self.ops2 = nn.ModuleList(
[nn.Sequential(
nn.MaxPool2d(3, stride=1, padding=1),
nn.BatchNorm2d(C, affine=True)),
nn.MaxPool2d(3, stride=2, padding=1),
nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats)),
nn.Sequential(
nn.MaxPool2d(3, stride=2, padding=1),
nn.BatchNorm2d(C, affine=True))])
nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats))])
@property
def multiplier(self):
return 4
def forward(self, s0, s1, drop_prob = -1):
s0 = self.preprocess0(s0)
@@ -307,3 +310,10 @@ class GDAS_Reduction_Cell(nn.Module):
if self.training and drop_prob > 0.:
X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob)
return torch.cat([X0, X1, X2, X3], dim=1)
# To manage the useful classes in this file.
RAW_OP_CLASSES = {
'gdas_reduction': GDAS_Reduction_Cell
}