""" Res2Net and Res2NeXt
Adapted from Official Pytorch impl at: https://github.com/gasvn/Res2Net/
Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
"""
import math
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
from .resnet import ResNet
__all__ = []
class Bottle2neck(nn.Module):
""" Res2Net/Res2NeXT Bottleneck
Adapted from https://github.com/gasvn/Res2Net/blob/master/res2net.py
"""
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
cardinality=1,
base_width=26,
scale=4,
dilation=1,
first_dilation=None,
act_layer=nn.ReLU,
norm_layer=None,
attn_layer=None,
**_,
):
super(Bottle2neck, self).__init__()
self.scale = scale
self.is_first = stride > 1 or downsample is not None
self.num_scales = max(1, scale - 1)
width = int(math.floor(planes * (base_width / 64.0))) * cardinality
self.width = width
outplanes = planes * self.expansion
first_dilation = first_dilation or dilation
self.conv1 = nn.Conv2d(inplanes, width * scale, kernel_size=1, bias=False)
self.bn1 = norm_layer(width * scale)
convs = []
bns = []
for i in range(self.num_scales):
convs.append(nn.Conv2d(
width, width, kernel_size=3, stride=stride, padding=first_dilation,
dilation=first_dilation, groups=cardinality, bias=False))
bns.append(norm_layer(width))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
if self.is_first:
# FIXME this should probably have count_include_pad=False, but hurts original weights
self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
else:
self.pool = None
self.conv3 = nn.Conv2d(width * scale, outplanes, kernel_size=1, bias=False)
self.bn3 = norm_layer(outplanes)
self.se = attn_layer(outplanes) if attn_layer is not None else None
self.relu = act_layer(inplace=True)
self.downsample = downsample
def zero_init_last(self):
if getattr(self.bn3, 'weight', None) is not None:
nn.init.zeros_(self.bn3.weight)
def forward(self, x):
shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out, self.width, 1)
spo = []
sp = spx[0] # redundant, for torchscript
for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
if i == 0 or self.is_first:
sp = spx[i]
else:
sp = sp + spx[i]
sp = conv(sp)
sp = bn(sp)
sp = self.relu(sp)
spo.append(sp)
if self.scale > 1:
if self.pool is not None: # self.is_first == True, None check for torchscript
spo.append(self.pool(spx[-1]))
else:
spo.append(spx[-1])
out = torch.cat(spo, 1)
out = self.conv3(out)
out = self.bn3(out)
if self.se is not None:
out = self.se(out)
if self.downsample is not None:
shortcut = self.downsample(x)
out += shortcut
out = self.relu(out)
return out
def _create_res2net(variant, pretrained=False, **kwargs):
return build_model_with_cfg(ResNet, variant, pretrained, **kwargs)
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 = generate_default_cfgs({
'res2net50_26w_4s.in1k': _cfg(hf_hub_id='timm/'),
'res2net50_48w_2s.in1k': _cfg(hf_hub_id='timm/'),
'res2net50_14w_8s.in1k': _cfg(hf_hub_id='timm/'),
'res2net50_26w_6s.in1k': _cfg(hf_hub_id='timm/'),
'res2net50_26w_8s.in1k': _cfg(hf_hub_id='timm/'),
'res2net101_26w_4s.in1k': _cfg(hf_hub_id='timm/'),
'res2next50.in1k': _cfg(hf_hub_id='timm/'),
'res2net50d.in1k': _cfg(hf_hub_id='timm/', first_conv='conv1.0'),
'res2net101d.in1k': _cfg(hf_hub_id='timm/', first_conv='conv1.0'),
})
@register_model
def res2net50_26w_4s(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Res2Net-50 26w4s model.
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=4))
return _create_res2net('res2net50_26w_4s', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net101_26w_4s(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Res2Net-101 26w4s model.
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 23, 3], base_width=26, block_args=dict(scale=4))
return _create_res2net('res2net101_26w_4s', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net50_26w_6s(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Res2Net-50 26w6s model.
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=6))
return _create_res2net('res2net50_26w_6s', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net50_26w_8s(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Res2Net-50 26w8s model.
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=8))
return _create_res2net('res2net50_26w_8s', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net50_48w_2s(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Res2Net-50 48w2s model.
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=48, block_args=dict(scale=2))
return _create_res2net('res2net50_48w_2s', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net50_14w_8s(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Res2Net-50 14w8s model.
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=14, block_args=dict(scale=8))
return _create_res2net('res2net50_14w_8s', pretrained, **dict(model_args, **kwargs))
@register_model
def res2next50(pretrained=False, **kwargs) -> ResNet:
"""Construct Res2NeXt-50 4s
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=4, cardinality=8, block_args=dict(scale=4))
return _create_res2net('res2next50', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net50d(pretrained=False, **kwargs) -> ResNet:
"""Construct Res2Net-50
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, stem_type='deep',
avg_down=True, stem_width=32, block_args=dict(scale=4))
return _create_res2net('res2net50d', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net101d(pretrained=False, **kwargs) -> ResNet:
"""Construct Res2Net-50
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 23, 3], base_width=26, stem_type='deep',
avg_down=True, stem_width=32, block_args=dict(scale=4))
return _create_res2net('res2net101d', pretrained, **dict(model_args, **kwargs))