import math
from torch import nn as nn
from .registry import register_model
from .helpers import load_pretrained
from .layers import SelectiveKernelConv, ConvBnAct, create_attn
from .resnet import ResNet
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv1', 'classifier': 'fc',
**kwargs
}
default_cfgs = {
'skresnet18': _cfg(url=''),
'skresnet26d': _cfg(),
'skresnet50': _cfg(),
'skresnet50d': _cfg(),
'skresnext50_32x4d': _cfg(),
}
class SelectiveKernelBasic(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
sk_kwargs=None, reduce_first=1, dilation=1, first_dilation=None,
drop_block=None, drop_path=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_layer=None):
super(SelectiveKernelBasic, self).__init__()
sk_kwargs = sk_kwargs or {}
conv_kwargs = dict(drop_block=drop_block, act_layer=act_layer, norm_layer=norm_layer)
assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
assert base_width == 64, 'BasicBlock doest not support changing base width'
first_planes = planes // reduce_first
outplanes = planes * self.expansion
first_dilation = first_dilation or dilation
_selective_first = True # FIXME temporary, for experiments
if _selective_first:
self.conv1 = SelectiveKernelConv(
inplanes, first_planes, stride=stride, dilation=first_dilation, **conv_kwargs, **sk_kwargs)
conv_kwargs['act_layer'] = None
self.conv2 = ConvBnAct(
first_planes, outplanes, kernel_size=3, dilation=dilation, **conv_kwargs)
else:
self.conv1 = ConvBnAct(
inplanes, first_planes, kernel_size=3, stride=stride, dilation=first_dilation, **conv_kwargs)
conv_kwargs['act_layer'] = None
self.conv2 = SelectiveKernelConv(
first_planes, outplanes, dilation=dilation, **conv_kwargs, **sk_kwargs)
self.se = create_attn(attn_layer, outplanes)
self.act = act_layer(inplace=True)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
self.drop_block = drop_block
self.drop_path = drop_path
def zero_init_last_bn(self):
nn.init.zeros_(self.conv2.bn.weight)
def forward(self, x):
residual = x
x = self.conv1(x)
x = self.conv2(x)
if self.se is not None:
x = self.se(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.downsample is not None:
residual = self.downsample(residual)
x += residual
x = self.act(x)
return x
class SelectiveKernelBottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None,
cardinality=1, base_width=64, sk_kwargs=None, reduce_first=1, dilation=1, first_dilation=None,
drop_block=None, drop_path=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_layer=None):
super(SelectiveKernelBottleneck, self).__init__()
sk_kwargs = sk_kwargs or {}
conv_kwargs = dict(drop_block=drop_block, act_layer=act_layer, norm_layer=norm_layer)
width = int(math.floor(planes * (base_width / 64)) * cardinality)
first_planes = width // reduce_first
outplanes = planes * self.expansion
first_dilation = first_dilation or dilation
self.conv1 = ConvBnAct(inplanes, first_planes, kernel_size=1, **conv_kwargs)
self.conv2 = SelectiveKernelConv(
first_planes, width, stride=stride, dilation=first_dilation, groups=cardinality,
**conv_kwargs, **sk_kwargs)
conv_kwargs['act_layer'] = None
self.conv3 = ConvBnAct(width, outplanes, kernel_size=1, **conv_kwargs)
self.se = create_attn(attn_layer, outplanes)
self.act = act_layer(inplace=True)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
self.drop_block = drop_block
self.drop_path = drop_path
def zero_init_last_bn(self):
nn.init.zeros_(self.conv3.bn.weight)
def forward(self, x):
residual = x
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
if self.se is not None:
x = self.se(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.downsample is not None:
residual = self.downsample(residual)
x += residual
x = self.act(x)
return x
@register_model
def skresnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNet-18 model.
"""
default_cfg = default_cfgs['skresnet18']
sk_kwargs = dict(
min_attn_channels=16,
)
model = ResNet(
SelectiveKernelBasic, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans,
block_args=dict(sk_kwargs=sk_kwargs), **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def sksresnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNet-18 model.
"""
default_cfg = default_cfgs['skresnet18']
sk_kwargs = dict(
min_attn_channels=16,
attn_reduction=8,
split_input=True
)
model = ResNet(
SelectiveKernelBasic, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans,
block_args=dict(sk_kwargs=sk_kwargs), zero_init_last_bn=False, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def skresnet26d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNet-26 model.
"""
default_cfg = default_cfgs['skresnet26d']
sk_kwargs = dict(
keep_3x3=False,
)
model = ResNet(
SelectiveKernelBottleneck, [2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True,
num_classes=num_classes, in_chans=in_chans, block_args=dict(sk_kwargs=sk_kwargs), zero_init_last_bn=False
**kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def skresnet50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a Select Kernel ResNet-50 model.
Based on config in "Compounding the Performance Improvements of Assembled Techniques in a
Convolutional Neural Network"
"""
sk_kwargs = dict(
attn_reduction=2,
)
default_cfg = default_cfgs['skresnet50']
model = ResNet(
SelectiveKernelBottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
block_args=dict(sk_kwargs=sk_kwargs), zero_init_last_bn=False, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def skresnet50d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a Select Kernel ResNet-50-D model.
Based on config in "Compounding the Performance Improvements of Assembled Techniques in a
Convolutional Neural Network"
"""
sk_kwargs = dict(
attn_reduction=2,
)
default_cfg = default_cfgs['skresnet50d']
model = ResNet(
SelectiveKernelBottleneck, [3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True,
num_classes=num_classes, in_chans=in_chans, block_args=dict(sk_kwargs=sk_kwargs),
zero_init_last_bn=False, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def skresnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a Select Kernel ResNeXt50-32x4d model. This should be equivalent to
the SKNet50 model in the Select Kernel Paper
"""
default_cfg = default_cfgs['skresnext50_32x4d']
model = ResNet(
SelectiveKernelBottleneck, [3, 4, 6, 3], cardinality=32, base_width=4,
num_classes=num_classes, in_chans=in_chans, zero_init_last_bn=False, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model