@ -125,11 +125,12 @@ class SEModule(nn.Module):
class BasicBlock ( nn . Module ) :
__constants__ = [ ' se ' , ' downsample ' ] # for pre 1.4 torchscript compat
expansion = 1
def __init__ ( self , inplanes , planes , stride = 1 , downsample = None ,
cardinality = 1 , base_width = 64 , use_se = False ,
reduce_first = 1 , dilation = 1 , previous_dilation = 1 , norm_layer= nn . BatchNorm2d ) :
reduce_first = 1 , dilation = 1 , previous_dilation = 1 , act_layer= nn . ReLU , norm_layer= nn . BatchNorm2d ) :
super ( BasicBlock , self ) . __init__ ( )
assert cardinality == 1 , ' BasicBlock only supports cardinality of 1 '
@ -141,12 +142,13 @@ class BasicBlock(nn.Module):
inplanes , first_planes , kernel_size = 3 , stride = stride , padding = dilation ,
dilation = dilation , bias = False )
self . bn1 = norm_layer ( first_planes )
self . relu = nn . ReLU ( inplace = True )
self . act1 = act_layer ( inplace = True )
self . conv2 = nn . Conv2d (
first_planes , outplanes , kernel_size = 3 , padding = previous_dilation ,
dilation = previous_dilation , bias = False )
self . bn2 = norm_layer ( outplanes )
self . se = SEModule ( outplanes , planes / / 4 ) if use_se else None
self . act2 = act_layer ( inplace = True )
self . downsample = downsample
self . stride = stride
self . dilation = dilation
@ -156,7 +158,7 @@ class BasicBlock(nn.Module):
out = self . conv1 ( x )
out = self . bn1 ( out )
out = self . relu ( out )
out = self . act1 ( out )
out = self . conv2 ( out )
out = self . bn2 ( out )
@ -167,17 +169,18 @@ class BasicBlock(nn.Module):
residual = self . downsample ( x )
out + = residual
out = self . relu ( out )
out = self . act2 ( out )
return out
class Bottleneck ( nn . Module ) :
__constants__ = [ ' se ' , ' downsample ' ] # for pre 1.4 torchscript compat
expansion = 4
def __init__ ( self , inplanes , planes , stride = 1 , downsample = None ,
cardinality = 1 , base_width = 64 , use_se = False ,
reduce_first = 1 , dilation = 1 , previous_dilation = 1 , norm_layer= nn . BatchNorm2d ) :
reduce_first = 1 , dilation = 1 , previous_dilation = 1 , act_layer= nn . ReLU , norm_layer= nn . BatchNorm2d ) :
super ( Bottleneck , self ) . __init__ ( )
width = int ( math . floor ( planes * ( base_width / 64 ) ) * cardinality )
@ -186,14 +189,16 @@ class Bottleneck(nn.Module):
self . conv1 = nn . Conv2d ( inplanes , first_planes , kernel_size = 1 , bias = False )
self . bn1 = norm_layer ( first_planes )
self . act1 = act_layer ( inplace = True )
self . conv2 = nn . Conv2d (
first_planes , width , kernel_size = 3 , stride = stride ,
padding = dilation , dilation = dilation , groups = cardinality , bias = False )
self . bn2 = norm_layer ( width )
self . act2 = act_layer ( inplace = True )
self . conv3 = nn . Conv2d ( width , outplanes , kernel_size = 1 , bias = False )
self . bn3 = norm_layer ( outplanes )
self . se = SEModule ( outplanes , planes / / 4 ) if use_se else None
self . relu = nn . ReLU ( inplace = True )
self . act3 = act_layer ( inplace = True )
self . downsample = downsample
self . stride = stride
self . dilation = dilation
@ -203,11 +208,11 @@ class Bottleneck(nn.Module):
out = self . conv1 ( x )
out = self . bn1 ( out )
out = self . relu ( out )
out = self . act1 ( out )
out = self . conv2 ( out )
out = self . bn2 ( out )
out = self . relu ( out )
out = self . act2 ( out )
out = self . conv3 ( out )
out = self . bn3 ( out )
@ -219,7 +224,7 @@ class Bottleneck(nn.Module):
residual = self . downsample ( x )
out + = residual
out = self . relu ( out )
out = self . act3 ( out )
return out
@ -284,9 +289,10 @@ class ResNet(nn.Module):
Kernel size of residual block downsampling path , 1 x1 for most archs , 3 x3 for senets
avg_down : bool , default False
Whether to use average pooling for projection skip connection between stages / downsample .
dilated : bool , default False
Applying dilation strategy to pretrained ResNet yielding a stride - 8 model ,
typically used in Semantic Segmentation .
output_stride : int , default 32
Set the output stride of the network , 32 , 16 , or 8. Typically used in segmentation .
act_layer : class , activation layer
norm_layer : class , normalization layer
drop_rate : float , default 0.
Dropout probability before classifier , for training
global_pool : str , default ' avg '
@ -294,8 +300,8 @@ class ResNet(nn.Module):
"""
def __init__ ( self , block , layers , num_classes = 1000 , in_chans = 3 , use_se = False ,
cardinality = 1 , base_width = 64 , stem_width = 64 , stem_type = ' ' ,
block_reduce_first = 1 , down_kernel_size = 1 , avg_down = False , dilated= False ,
norm_layer= nn . BatchNorm2d , drop_rate = 0.0 , global_pool = ' avg ' ,
block_reduce_first = 1 , down_kernel_size = 1 , avg_down = False , output_stride= 32 ,
act_layer= nn . ReLU , norm_layer= nn . BatchNorm2d , drop_rate = 0.0 , global_pool = ' avg ' ,
zero_init_last_bn = True , block_args = None ) :
block_args = block_args or dict ( )
self . num_classes = num_classes
@ -305,9 +311,9 @@ class ResNet(nn.Module):
self . base_width = base_width
self . drop_rate = drop_rate
self . expansion = block . expansion
self . dilated = dilated
super ( ResNet , self ) . __init__ ( )
# Stem
if deep_stem :
stem_chs_1 = stem_chs_2 = stem_width
if ' tiered ' in stem_type :
@ -316,25 +322,37 @@ class ResNet(nn.Module):
self . conv1 = nn . Sequential ( * [
nn . Conv2d ( in_chans , stem_chs_1 , 3 , stride = 2 , padding = 1 , bias = False ) ,
norm_layer ( stem_chs_1 ) ,
nn. ReLU ( inplace = True ) ,
act_layer ( inplace = True ) ,
nn . Conv2d ( stem_chs_1 , stem_chs_2 , 3 , stride = 1 , padding = 1 , bias = False ) ,
norm_layer ( stem_chs_2 ) ,
nn. ReLU ( inplace = True ) ,
act_layer ( inplace = True ) ,
nn . Conv2d ( stem_chs_2 , self . inplanes , 3 , stride = 1 , padding = 1 , bias = False ) ] )
else :
self . conv1 = nn . Conv2d ( in_chans , self . inplanes , kernel_size = 7 , stride = 2 , padding = 3 , bias = False )
self . bn1 = norm_layer ( self . inplanes )
self . relu = nn . ReLU ( inplace = True )
self . act1 = act_layer ( inplace = True )
self . maxpool = nn . MaxPool2d ( kernel_size = 3 , stride = 2 , padding = 1 )
stride_3_4 = 1 if self . dilated else 2
dilation_3 = 2 if self . dilated else 1
dilation_4 = 4 if self . dilated else 1
largs = dict ( use_se = use_se , reduce_first = block_reduce_first , norm_layer = norm_layer ,
# Feature Blocks
channels , strides , dilations = [ 64 , 128 , 256 , 512 ] , [ 1 , 2 , 2 , 2 ] , [ 1 ] * 4
if output_stride == 16 :
strides [ 3 ] = 1
dilations [ 3 ] = 2
elif output_stride == 8 :
strides [ 2 : 4 ] = [ 1 , 1 ]
dilations [ 2 : 4 ] = [ 2 , 4 ]
else :
assert output_stride == 32
llargs = list ( zip ( channels , layers , strides , dilations ) )
lkwargs = dict (
use_se = use_se , reduce_first = block_reduce_first , act_layer = act_layer , norm_layer = norm_layer ,
avg_down = avg_down , down_kernel_size = down_kernel_size , * * block_args )
self . layer1 = self . _make_layer ( block , 64 , layers [ 0 ] , stride = 1 , * * largs )
self . layer2 = self . _make_layer ( block , 128 , layers [ 1 ] , stride = 2 , * * largs )
self . layer3 = self . _make_layer ( block , 256 , layers [ 2 ] , stride = stride_3_4 , dilation = dilation_3 , * * largs )
self . layer4 = self . _make_layer ( block , 512 , layers [ 3 ] , stride = stride_3_4 , dilation = dilation_4 , * * largs )
self . layer1 = self . _make_layer ( block , * llargs [ 0 ] , * * lkwargs )
self . layer2 = self . _make_layer ( block , * llargs [ 1 ] , * * lkwargs )
self . layer3 = self . _make_layer ( block , * llargs [ 2 ] , * * lkwargs )
self . layer4 = self . _make_layer ( block , * llargs [ 3 ] , * * lkwargs )
# Head (Pooling and Classifier)
self . global_pool = SelectAdaptivePool2d ( pool_type = global_pool )
self . num_features = 512 * block . expansion
self . fc = nn . Linear ( self . num_features * self . global_pool . feat_mult ( ) , num_classes )
@ -352,7 +370,8 @@ class ResNet(nn.Module):
nn . init . constant_ ( m . bias , 0. )
def _make_layer ( self , block , planes , blocks , stride = 1 , dilation = 1 , reduce_first = 1 ,
use_se = False , avg_down = False , down_kernel_size = 1 , norm_layer = nn . BatchNorm2d , * * kwargs ) :
use_se = False , avg_down = False , down_kernel_size = 1 , * * kwargs ) :
norm_layer = kwargs . get ( ' norm_layer ' )
downsample = None
down_kernel_size = 1 if stride == 1 and dilation == 1 else down_kernel_size
if stride != 1 or self . inplanes != planes * block . expansion :
@ -370,15 +389,15 @@ class ResNet(nn.Module):
downsample = nn . Sequential ( * downsample_layers )
first_dilation = 1 if dilation in ( 1 , 2 ) else 2
b args = dict (
b kw args = dict (
cardinality = self . cardinality , base_width = self . base_width , reduce_first = reduce_first ,
use_se = use_se , norm_layer = norm_layer , * * kwargs )
use_se = use_se , * * kwargs )
layers = [ block (
self . inplanes , planes , stride , downsample , dilation = first_dilation , previous_dilation = dilation , * * b args) ]
self . inplanes , planes , stride , downsample , dilation = first_dilation , previous_dilation = dilation , * * b kw args) ]
self . inplanes = planes * block . expansion
for i in range ( 1 , blocks ) :
layers . append ( block (
self . inplanes , planes , dilation = dilation , previous_dilation = dilation , * * b args) )
self . inplanes , planes , dilation = dilation , previous_dilation = dilation , * * b kw args) )
return nn . Sequential ( * layers )
@ -394,7 +413,7 @@ class ResNet(nn.Module):
def forward_features ( self , x ) :
x = self . conv1 ( x )
x = self . bn1 ( x )
x = self . relu ( x )
x = self . act1 ( x )
x = self . maxpool ( x )
x = self . layer1 ( x )