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update NAS-Bench-102 baselines / support track_running_stats

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This commit is contained in:
D-X-Y
2019-12-23 13:32:20 +11:00
parent 729ce136db
commit 2dc8dce6d3
9 changed files with 56 additions and 57 deletions
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4
lib/models/cell_infers/cells.py
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@@ -19,9 +19,9 @@ class InferCell(nn.Module):
cur_innod = []
for (op_name, op_in) in node_info:
if op_in == 0:
layer = OPS[op_name](C_in , C_out, stride, True)
layer = OPS[op_name](C_in , C_out, stride, True, True)
else:
layer = OPS[op_name](C_out, C_out, 1, True)
layer = OPS[op_name](C_out, C_out, 1, True, True)
cur_index.append( len(self.layers) )
cur_innod.append( op_in )
self.layers.append( layer )

26
lib/models/cell_operations.py
View File

@@ -7,13 +7,13 @@ import torch.nn as nn
__all__ = ['OPS', 'ResNetBasicblock', 'SearchSpaceNames']
OPS = {
'none' : lambda C_in, C_out, stride, affine: Zero(C_in, C_out, stride),
'avg_pool_3x3' : lambda C_in, C_out, stride, affine: POOLING(C_in, C_out, stride, 'avg'),
'max_pool_3x3' : lambda C_in, C_out, stride, affine: POOLING(C_in, C_out, stride, 'max'),
'nor_conv_7x7' : lambda C_in, C_out, stride, affine: ReLUConvBN(C_in, C_out, (7,7), (stride,stride), (3,3), (1,1), affine),
'nor_conv_3x3' : lambda C_in, C_out, stride, affine: ReLUConvBN(C_in, C_out, (3,3), (stride,stride), (1,1), (1,1), affine),
'nor_conv_1x1' : lambda C_in, C_out, stride, affine: ReLUConvBN(C_in, C_out, (1,1), (stride,stride), (0,0), (1,1), affine),
'skip_connect' : lambda C_in, C_out, stride, affine: Identity() if stride == 1 and C_in == C_out else FactorizedReduce(C_in, C_out, stride, affine),
'none' : lambda C_in, C_out, stride, affine, track_running_stats: Zero(C_in, C_out, stride),
'avg_pool_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: POOLING(C_in, C_out, stride, 'avg', affine, track_running_stats),
'max_pool_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: POOLING(C_in, C_out, stride, 'max', affine, track_running_stats),
'nor_conv_7x7' : lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (7,7), (stride,stride), (3,3), (1,1), affine, track_running_stats),
'nor_conv_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (3,3), (stride,stride), (1,1), (1,1), affine, track_running_stats),
'nor_conv_1x1' : lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (1,1), (stride,stride), (0,0), (1,1), affine, track_running_stats),
'skip_connect' : lambda C_in, C_out, stride, affine, track_running_stats: Identity() if stride == 1 and C_in == C_out else FactorizedReduce(C_in, C_out, stride, affine, track_running_stats),
}
CONNECT_NAS_BENCHMARK = ['none', 'skip_connect', 'nor_conv_3x3']
@@ -27,12 +27,12 @@ SearchSpaceNames = {'connect-nas' : CONNECT_NAS_BENCHMARK,
class ReLUConvBN(nn.Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine):
def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine, track_running_stats=True):
super(ReLUConvBN, self).__init__()
self.op = nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=False),
nn.BatchNorm2d(C_out, affine=affine)
nn.BatchNorm2d(C_out, affine=affine, track_running_stats=track_running_stats)
)
def forward(self, x):
@@ -77,12 +77,12 @@ class ResNetBasicblock(nn.Module):
class POOLING(nn.Module):
def __init__(self, C_in, C_out, stride, mode, affine=True):
def __init__(self, C_in, C_out, stride, mode, affine=True, track_running_stats=True):
super(POOLING, self).__init__()
if C_in == C_out:
self.preprocess = None
else:
self.preprocess = ReLUConvBN(C_in, C_out, 1, 1, 0, affine)
self.preprocess = ReLUConvBN(C_in, C_out, 1, 1, 0, affine, track_running_stats)
if mode == 'avg' : self.op = nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False)
elif mode == 'max': self.op = nn.MaxPool2d(3, stride=stride, padding=1)
else : raise ValueError('Invalid mode={:} in POOLING'.format(mode))
@@ -127,7 +127,7 @@ class Zero(nn.Module):
class FactorizedReduce(nn.Module):
def __init__(self, C_in, C_out, stride, affine):
def __init__(self, C_in, C_out, stride, affine, track_running_stats):
super(FactorizedReduce, self).__init__()
self.stride = stride
self.C_in = C_in
@@ -142,7 +142,7 @@ class FactorizedReduce(nn.Module):
self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
else:
raise ValueError('Invalid stride : {:}'.format(stride))
self.bn = nn.BatchNorm2d(C_out, affine=affine)
self.bn = nn.BatchNorm2d(C_out, affine=affine, track_running_stats=track_running_stats)
def forward(self, x):
x = self.relu(x)

6
lib/models/cell_searchs/search_cells.py
View File

@@ -11,7 +11,7 @@ from ..cell_operations import OPS
class SearchCell(nn.Module):
def __init__(self, C_in, C_out, stride, max_nodes, op_names):
def __init__(self, C_in, C_out, stride, max_nodes, op_names, affine=False, track_running_stats=True):
super(SearchCell, self).__init__()
self.op_names = deepcopy(op_names)
@@ -23,9 +23,9 @@ class SearchCell(nn.Module):
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
if j == 0:
xlists = [OPS[op_name](C_in , C_out, stride, False) for op_name in op_names]
xlists = [OPS[op_name](C_in , C_out, stride, affine, track_running_stats) for op_name in op_names]
else:
xlists = [OPS[op_name](C_in , C_out, 1, False) for op_name in op_names]
xlists = [OPS[op_name](C_in , C_out, 1, affine, track_running_stats) for op_name in op_names]
self.edges[ node_str ] = nn.ModuleList( xlists )
self.edge_keys = sorted(list(self.edges.keys()))
self.edge2index = {key:i for i, key in enumerate(self.edge_keys)}