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232 lines
8.7 KiB
232 lines
8.7 KiB
5 years ago
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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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import torch.nn.functional as F
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from .resnet import ResNet, SEModule
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from .registry import register_model
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from .helpers import load_pretrained
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from .adaptive_avgmax_pool import SelectAdaptivePool2d
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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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, use_se=False,
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norm_layer=None, dilation=1, previous_dilation=1, **_):
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super(Bottle2neck, self).__init__()
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assert dilation == 1 and previous_dilation == 1 # FIXME support dilation
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self.scale = scale
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self.is_first = True if stride > 1 or downsample is not None else False
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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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outplanes = planes * self.expansion
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self.width = width
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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=1, 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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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 = SEModule(outplanes, planes // 4) if use_se else None
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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def forward(self, x):
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residual = 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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for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
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sp = spx[i] if i == 0 or self.is_first else 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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spo.append(self.pool(spx[-1]) if self.is_first else 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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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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@register_model
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def res2net50_26w_4s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a Res2Net-50_26w_4s 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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default_cfg = default_cfgs['res2net50_26w_4s']
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res2net_block_args = dict(scale=4)
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model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=26,
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num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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@register_model
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def res2net101_26w_4s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a Res2Net-50_26w_4s 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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default_cfg = default_cfgs['res2net101_26w_4s']
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res2net_block_args = dict(scale=4)
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model = ResNet(Bottle2neck, [3, 4, 23, 3], base_width=26,
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num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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@register_model
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def res2net50_26w_6s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a Res2Net-50_26w_4s 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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default_cfg = default_cfgs['res2net50_26w_6s']
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res2net_block_args = dict(scale=6)
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model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=26,
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num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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@register_model
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def res2net50_26w_8s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a Res2Net-50_26w_4s 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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default_cfg = default_cfgs['res2net50_26w_8s']
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res2net_block_args = dict(scale=8)
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model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=26,
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num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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@register_model
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def res2net50_48w_2s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a Res2Net-50_48w_2s 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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default_cfg = default_cfgs['res2net50_48w_2s']
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res2net_block_args = dict(scale=2)
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model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=48,
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num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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@register_model
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def res2net50_14w_8s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a Res2Net-50_14w_8s 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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default_cfg = default_cfgs['res2net50_14w_8s']
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res2net_block_args = dict(scale=8)
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model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=14, num_classes=num_classes, in_chans=in_chans,
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block_args=res2net_block_args, **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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@register_model
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def res2next50(pretrained=False, num_classes=1000, in_chans=3, **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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default_cfg = default_cfgs['res2next50']
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res2net_block_args = dict(scale=4)
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model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=4, cardinality=8,
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num_classes=1000, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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