import torch.nn as nn import math import torch.utils.model_zoo as model_zoo import torch import torch.nn.functional as F __all__ = ['Res2Net', 'res2net50_v1b', 'res2net101_v1b', 'res2net50_v1b_26w_4s'] model_urls = { 'res2net50_v1b_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_v1b_26w_4s-3cf99910.pth', 'res2net101_v1b_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net101_v1b_26w_4s-0812c246.pth', } class Bottle2neck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, baseWidth=26, scale=4, stype='normal'): super(Bottle2neck, self).__init__() width = int(math.floor(planes * (baseWidth / 64.0))) self.conv1 = nn.Conv2d(inplanes, width * scale, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(width * scale) if scale == 1: self.nums = 1 else: self.nums = scale - 1 if stype == 'stage': self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1) convs = [] bns = [] for i in range(self.nums): convs.append(nn.Conv2d(width, width, kernel_size=3, stride=stride, dilation=dilation, padding=dilation, bias=False)) bns.append(nn.BatchNorm2d(width)) self.convs = nn.ModuleList(convs) self.bns = nn.ModuleList(bns) self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stype = stype self.scale = scale self.width = width 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) for i in range(self.nums): if i == 0 or self.stype == 'stage': sp = spx[i] else: sp = sp + spx[i] sp = self.convs[i](sp) sp = self.relu(self.bns[i](sp)) if i == 0: out = sp else: out = torch.cat((out, sp), 1) if self.scale != 1 and self.stype == 'normal': out = torch.cat((out, spx[self.nums]), 1) elif self.scale != 1 and self.stype == 'stage': out = torch.cat((out, self.pool(spx[self.nums])), 1) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class Res2Net(nn.Module): def __init__(self, block, layers, baseWidth=26, scale=4, num_classes=1000, output_stride=32): self.inplanes = 64 super(Res2Net, self).__init__() self.baseWidth = baseWidth self.scale = scale self.output_stride = output_stride if self.output_stride == 8: self.grid = [1, 2, 1] self.stride = [1, 2, 1, 1] self.dilation = [1, 1, 2, 4] elif self.output_stride == 16: self.grid = [1, 2, 4] self.stride = [1, 2, 2, 1] self.dilation = [1, 1, 1, 2] elif self.output_stride == 32: self.grid = [1, 2, 4] self.stride = [1, 2, 2, 2] self.dilation = [1, 1, 2, 4] self.conv1 = nn.Sequential( nn.Conv2d(3, 32, 3, 2, 1, bias=False), nn.BatchNorm2d(32), nn.ReLU(inplace=True), nn.Conv2d(32, 32, 3, 1, 1, bias=False), nn.BatchNorm2d(32), nn.ReLU(inplace=True), nn.Conv2d(32, 64, 3, 1, 1, bias=False) ) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU() self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer( block, 64, layers[0], stride=self.stride[0], dilation=self.dilation[0]) self.layer2 = self._make_layer( block, 128, layers[1], stride=self.stride[1], dilation=self.dilation[1]) self.layer3 = self._make_layer( block, 256, layers[2], stride=self.stride[2], dilation=self.dilation[2]) self.layer4 = self._make_layer( block, 512, layers[3], stride=self.stride[3], dilation=self.dilation[3], grid=self.grid) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_( m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) def _make_layer(self, block, planes, blocks, stride=1, dilation=1, grid=None): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.AvgPool2d(kernel_size=stride, stride=stride, ceil_mode=True, count_include_pad=False), nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=1, bias=False), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, dilation, downsample=downsample, stype='stage', baseWidth=self.baseWidth, scale=self.scale)) self.inplanes = planes * block.expansion if grid is not None: assert len(grid) == blocks else: grid = [1] * blocks for i in range(1, blocks): layers.append(block(self.inplanes, planes, dilation=dilation * grid[i], baseWidth=self.baseWidth, scale=self.scale)) return nn.Sequential(*layers) def change_stride(self, output_stride=16): if output_stride == self.output_stride: return else: self.output_stride = output_stride if self.output_stride == 8: self.grid = [1, 2, 1] self.stride = [1, 2, 1, 1] self.dilation = [1, 1, 2, 4] elif self.output_stride == 16: self.grid = [1, 2, 4] self.stride = [1, 2, 2, 1] self.dilation = [1, 1, 1, 2] elif self.output_stride == 32: self.grid = [1, 2, 4] self.stride = [1, 2, 2, 2] self.dilation = [1, 1, 2, 4] for i, layer in enumerate([self.layer1, self.layer2, self.layer3, self.layer4]): for j, block in enumerate(layer): if block.downsample is not None: block.downsample[0].kernel_size = ( self.stride[i], self.stride[i]) block.downsample[0].stride = ( self.stride[i], self.stride[i]) if hasattr(block, 'pool'): block.pool.stride = ( self.stride[i], self.stride[i]) for conv in block.convs: conv.stride = (self.stride[i], self.stride[i]) for conv in block.convs: d = self.dilation[i] if i != 3 else self.dilation[i] * \ self.grid[j] conv.dilation = (d, d) conv.padding = (d, d) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) out = [x] x = self.layer1(x) out.append(x) x = self.layer2(x) out.append(x) x = self.layer3(x) out.append(x) x = self.layer4(x) out.append(x) return out def res2net50_v1b(pretrained=False, **kwargs): model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url( model_urls['res2net50_v1b_26w_4s'])) return model def res2net101_v1b(pretrained=False, **kwargs): model = Res2Net(Bottle2neck, [3, 4, 23, 3], baseWidth=26, scale=4, **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url( model_urls['res2net101_v1b_26w_4s'])) return model def res2net50_v1b_26w_4s(pretrained=True, **kwargs): model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs) if pretrained is True: model.load_state_dict(torch.load('data/backbone_ckpt/res2net50_v1b_26w_4s-3cf99910.pth', map_location='cpu')) return model def res2net101_v1b_26w_4s(pretrained=True, **kwargs): model = Res2Net(Bottle2neck, [3, 4, 23, 3], baseWidth=26, scale=4, **kwargs) if pretrained is True: model.load_state_dict(torch.load('data/backbone_ckpt/res2net101_v1b_26w_4s-0812c246.pth', map_location='cpu')) return model def res2net152_v1b_26w_4s(pretrained=False, **kwargs): model = Res2Net(Bottle2neck, [3, 8, 36, 3], baseWidth=26, scale=4, **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url( model_urls['res2net152_v1b_26w_4s'])) return model if __name__ == '__main__': images = torch.rand(1, 3, 224, 224).cuda(0) model = res2net50_v1b_26w_4s(pretrained=True) model = model.cuda(0) print(model(images).size())