| """ 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: |
| |
| 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): |
| 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] |
| 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: |
| |
| 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, |
| 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) |
|
|
