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import torch.nn as nn
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import math
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import torch.utils.model_zoo as model_zoo
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import torch
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__all__ = ['Res2Net', 'res2net50_v1b', 'res2net101_v1b', 'res2net50_v1b_26w_4s']
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model_urls = {
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'res2net50_v1b_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_v1b_26w_4s-3cf99910.pth',
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'res2net101_v1b_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net101_v1b_26w_4s-0812c246.pth',
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}
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class Bottle2neck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None, baseWidth=26, scale=4, stype='normal'):
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""" Constructor
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Args:
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inplanes: input channel dimensionality
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planes: output channel dimensionality
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stride: conv stride. Replaces pooling layer.
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downsample: None when stride = 1
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baseWidth: basic width of conv3x3
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scale: number of scale.
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type: 'normal': normal set. 'stage': first block of a new stage.
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"""
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super(Bottle2neck, self).__init__()
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width = int(math.floor(planes * (baseWidth / 64.0)))
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self.conv1 = nn.Conv2d(inplanes, width * scale, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(width * scale)
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if scale == 1:
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self.nums = 1
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else:
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self.nums = scale - 1
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if stype == 'stage':
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self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
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convs = []
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bns = []
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for i in range(self.nums):
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convs.append(nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, bias=False))
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bns.append(nn.BatchNorm2d(width))
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self.convs = nn.ModuleList(convs)
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self.bns = nn.ModuleList(bns)
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self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stype = stype
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self.scale = scale
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self.width = width
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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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for i in range(self.nums):
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if i == 0 or self.stype == 'stage':
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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 = self.convs[i](sp)
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sp = self.relu(self.bns[i](sp))
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if i == 0:
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out = sp
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else:
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out = torch.cat((out, sp), 1)
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if self.scale != 1 and self.stype == 'normal':
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out = torch.cat((out, spx[self.nums]), 1)
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elif self.scale != 1 and self.stype == 'stage':
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out = torch.cat((out, self.pool(spx[self.nums])), 1)
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out = self.conv3(out)
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out = self.bn3(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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class Res2Net(nn.Module):
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def __init__(self, block, layers, baseWidth=26, scale=4, num_classes=1000):
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self.inplanes = 64
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super(Res2Net, self).__init__()
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self.baseWidth = baseWidth
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self.scale = scale
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self.conv1 = nn.Sequential(
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nn.Conv2d(3, 32, 3, 2, 1, bias=False),
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nn.BatchNorm2d(32),
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nn.ReLU(inplace=True),
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nn.Conv2d(32, 32, 3, 1, 1, bias=False),
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nn.BatchNorm2d(32),
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nn.ReLU(inplace=True),
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nn.Conv2d(32, 64, 3, 1, 1, bias=False)
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)
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU()
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
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self.avgpool = nn.AdaptiveAvgPool2d(1)
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self.fc = nn.Linear(512 * block.expansion, num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.AvgPool2d(kernel_size=stride, stride=stride,
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ceil_mode=True, count_include_pad=False),
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nn.Conv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=1, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample=downsample,
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stype='stage', baseWidth=self.baseWidth, scale=self.scale))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes, baseWidth=self.baseWidth, scale=self.scale))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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x = self.fc(x)
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return x
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def res2net50_v1b(pretrained=False, **kwargs):
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"""Constructs a Res2Net-50_v1b lib.
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Res2Net-50 refers to the Res2Net-50_v1b_26w_4s.
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Args:
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pretrained (bool): If True, returns a lib pre-trained on ImageNet
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"""
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model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['res2net50_v1b_26w_4s']))
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return model
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def res2net101_v1b(pretrained=False, **kwargs):
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"""Constructs a Res2Net-50_v1b_26w_4s lib.
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Args:
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pretrained (bool): If True, returns a lib pre-trained on ImageNet
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"""
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model = Res2Net(Bottle2neck, [3, 4, 23, 3], baseWidth=26, scale=4, **kwargs)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['res2net101_v1b_26w_4s']))
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return model
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def res2net50_v1b_26w_4s(pretrained=False, **kwargs):
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"""Constructs a Res2Net-50_v1b_26w_4s lib.
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Args:
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pretrained (bool): If True, returns a lib pre-trained on ImageNet
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"""
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model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['res2net50_v1b_26w_4s']))
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return model
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def res2net101_v1b_26w_4s(pretrained=False, **kwargs):
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"""Constructs a Res2Net-50_v1b_26w_4s lib.
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Args:
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pretrained (bool): If True, returns a lib pre-trained on ImageNet
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"""
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model = Res2Net(Bottle2neck, [3, 4, 23, 3], baseWidth=26, scale=4, **kwargs)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['res2net101_v1b_26w_4s']))
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return model
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def res2net152_v1b_26w_4s(pretrained=False, **kwargs):
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"""Constructs a Res2Net-50_v1b_26w_4s lib.
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Args:
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pretrained (bool): If True, returns a lib pre-trained on ImageNet
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"""
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model = Res2Net(Bottle2neck, [3, 8, 36, 3], baseWidth=26, scale=4, **kwargs)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['res2net152_v1b_26w_4s']))
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return model
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if __name__ == '__main__':
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images = torch.rand(1, 3, 224, 224).cuda(0)
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model = res2net50_v1b_26w_4s(pretrained=True)
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model = model.cuda(0)
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print(model(images).size())
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