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""" Res2Net and Res2NeXt |
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Adapted from Official Pytorch impl at: https://github.com/gasvn/Res2Net/ |
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Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169 |
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""" |
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import math |
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import torch |
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import torch.nn as nn |
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD |
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from .helpers import build_model_with_cfg |
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from .registry import register_model |
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from .resnet import ResNet |
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__all__ = [] |
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def _cfg(url='', **kwargs): |
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return { |
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'url': url, |
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), |
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'crop_pct': 0.875, 'interpolation': 'bilinear', |
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, |
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'first_conv': 'conv1', 'classifier': 'fc', |
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**kwargs |
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} |
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default_cfgs = { |
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'res2net50_26w_4s': _cfg( |
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_4s-06e79181.pth'), |
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'res2net50_48w_2s': _cfg( |
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_48w_2s-afed724a.pth'), |
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'res2net50_14w_8s': _cfg( |
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_14w_8s-6527dddc.pth'), |
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'res2net50_26w_6s': _cfg( |
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_6s-19041792.pth'), |
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'res2net50_26w_8s': _cfg( |
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_8s-2c7c9f12.pth'), |
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'res2net101_26w_4s': _cfg( |
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net101_26w_4s-02a759a1.pth'), |
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'res2next50': _cfg( |
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next50_4s-6ef7e7bf.pth'), |
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} |
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class Bottle2neck(nn.Module): |
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""" Res2Net/Res2NeXT Bottleneck |
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Adapted from https://github.com/gasvn/Res2Net/blob/master/res2net.py |
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""" |
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expansion = 4 |
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def __init__(self, inplanes, planes, stride=1, downsample=None, |
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cardinality=1, base_width=26, scale=4, dilation=1, first_dilation=None, |
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act_layer=nn.ReLU, norm_layer=None, attn_layer=None, **_): |
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super(Bottle2neck, self).__init__() |
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self.scale = scale |
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self.is_first = stride > 1 or downsample is not None |
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self.num_scales = max(1, scale - 1) |
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width = int(math.floor(planes * (base_width / 64.0))) * cardinality |
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self.width = width |
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outplanes = planes * self.expansion |
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first_dilation = first_dilation or dilation |
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self.conv1 = nn.Conv2d(inplanes, width * scale, kernel_size=1, bias=False) |
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self.bn1 = norm_layer(width * scale) |
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convs = [] |
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bns = [] |
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for i in range(self.num_scales): |
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convs.append(nn.Conv2d( |
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width, width, kernel_size=3, stride=stride, padding=first_dilation, |
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dilation=first_dilation, groups=cardinality, bias=False)) |
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bns.append(norm_layer(width)) |
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self.convs = nn.ModuleList(convs) |
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self.bns = nn.ModuleList(bns) |
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if self.is_first: |
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self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1) |
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else: |
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self.pool = None |
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self.conv3 = nn.Conv2d(width * scale, outplanes, kernel_size=1, bias=False) |
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self.bn3 = norm_layer(outplanes) |
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self.se = attn_layer(outplanes) if attn_layer is not None else None |
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self.relu = act_layer(inplace=True) |
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self.downsample = downsample |
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def zero_init_last_bn(self): |
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nn.init.zeros_(self.bn3.weight) |
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def forward(self, x): |
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shortcut = x |
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out = self.conv1(x) |
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out = self.bn1(out) |
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out = self.relu(out) |
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spx = torch.split(out, self.width, 1) |
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spo = [] |
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sp = spx[0] |
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for i, (conv, bn) in enumerate(zip(self.convs, self.bns)): |
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if i == 0 or self.is_first: |
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sp = spx[i] |
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else: |
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sp = sp + spx[i] |
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sp = conv(sp) |
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sp = bn(sp) |
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sp = self.relu(sp) |
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spo.append(sp) |
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if self.scale > 1: |
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if self.pool is not None: |
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spo.append(self.pool(spx[-1])) |
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else: |
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spo.append(spx[-1]) |
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out = torch.cat(spo, 1) |
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out = self.conv3(out) |
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out = self.bn3(out) |
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if self.se is not None: |
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out = self.se(out) |
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if self.downsample is not None: |
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shortcut = self.downsample(x) |
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out += shortcut |
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out = self.relu(out) |
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return out |
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def _create_res2net(variant, pretrained=False, **kwargs): |
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return build_model_with_cfg( |
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ResNet, variant, pretrained, |
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default_cfg=default_cfgs[variant], |
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**kwargs) |
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@register_model |
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def res2net50_26w_4s(pretrained=False, **kwargs): |
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"""Constructs a Res2Net-50 26w4s model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model_args = dict( |
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block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=4), **kwargs) |
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return _create_res2net('res2net50_26w_4s', pretrained, **model_args) |
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@register_model |
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def res2net101_26w_4s(pretrained=False, **kwargs): |
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"""Constructs a Res2Net-101 26w4s model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model_args = dict( |
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block=Bottle2neck, layers=[3, 4, 23, 3], base_width=26, block_args=dict(scale=4), **kwargs) |
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return _create_res2net('res2net101_26w_4s', pretrained, **model_args) |
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@register_model |
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def res2net50_26w_6s(pretrained=False, **kwargs): |
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"""Constructs a Res2Net-50 26w6s model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model_args = dict( |
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block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=6), **kwargs) |
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return _create_res2net('res2net50_26w_6s', pretrained, **model_args) |
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@register_model |
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def res2net50_26w_8s(pretrained=False, **kwargs): |
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"""Constructs a Res2Net-50 26w8s model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model_args = dict( |
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block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=8), **kwargs) |
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return _create_res2net('res2net50_26w_8s', pretrained, **model_args) |
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@register_model |
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def res2net50_48w_2s(pretrained=False, **kwargs): |
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"""Constructs a Res2Net-50 48w2s model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model_args = dict( |
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block=Bottle2neck, layers=[3, 4, 6, 3], base_width=48, block_args=dict(scale=2), **kwargs) |
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return _create_res2net('res2net50_48w_2s', pretrained, **model_args) |
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@register_model |
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def res2net50_14w_8s(pretrained=False, **kwargs): |
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"""Constructs a Res2Net-50 14w8s model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model_args = dict( |
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block=Bottle2neck, layers=[3, 4, 6, 3], base_width=14, block_args=dict(scale=8), **kwargs) |
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return _create_res2net('res2net50_14w_8s', pretrained, **model_args) |
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@register_model |
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def res2next50(pretrained=False, **kwargs): |
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"""Construct Res2NeXt-50 4s |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model_args = dict( |
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block=Bottle2neck, layers=[3, 4, 6, 3], base_width=4, cardinality=8, block_args=dict(scale=4), **kwargs) |
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return _create_res2net('res2next50', pretrained, **model_args) |
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