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""" 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 .helpers import build_model_with_cfg
from .registry import register_model
from .resnet import ResNet
__all__ = []
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 = {
'res2net50_26w_4s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_4s-06e79181.pth'),
'res2net50_48w_2s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_48w_2s-afed724a.pth'),
'res2net50_14w_8s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_14w_8s-6527dddc.pth'),
'res2net50_26w_6s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_6s-19041792.pth'),
'res2net50_26w_8s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_8s-2c7c9f12.pth'),
'res2net101_26w_4s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net101_26w_4s-02a759a1.pth'),
'res2next50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next50_4s-6ef7e7bf.pth'),
}
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_bn(self):
nn.init.zeros_(self.bn3.weight)
def forward(self, x):
residual = 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:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
def _create_res2net(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
ResNet, variant, pretrained, default_cfg=default_cfgs[variant], **kwargs)
@register_model
def res2net50_26w_4s(pretrained=False, **kwargs):
"""Constructs a Res2Net-50 26w4s model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=4), **kwargs)
return _create_res2net('res2net50_26w_4s', pretrained, **model_args)
@register_model
def res2net101_26w_4s(pretrained=False, **kwargs):
"""Constructs a Res2Net-101 26w4s model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 23, 3], base_width=26, block_args=dict(scale=4), **kwargs)
return _create_res2net('res2net101_26w_4s', pretrained, **model_args)
@register_model
def res2net50_26w_6s(pretrained=False, **kwargs):
"""Constructs a Res2Net-50 26w6s model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=6), **kwargs)
return _create_res2net('res2net50_26w_6s', pretrained, **model_args)
@register_model
def res2net50_26w_8s(pretrained=False, **kwargs):
"""Constructs a Res2Net-50 26w8s model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=8), **kwargs)
return _create_res2net('res2net50_26w_8s', pretrained, **model_args)
@register_model
def res2net50_48w_2s(pretrained=False, **kwargs):
"""Constructs a Res2Net-50 48w2s model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=48, block_args=dict(scale=2), **kwargs)
return _create_res2net('res2net50_48w_2s', pretrained, **model_args)
@register_model
def res2net50_14w_8s(pretrained=False, **kwargs):
"""Constructs a Res2Net-50 14w8s model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=14, block_args=dict(scale=8), **kwargs)
return _create_res2net('res2net50_14w_8s', pretrained, **model_args)
@register_model
def res2next50(pretrained=False, **kwargs):
"""Construct Res2NeXt-50 4s
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=4, cardinality=8, block_args=dict(scale=4), **kwargs)
return _create_res2net('res2next50', pretrained, **model_args)
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