| 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, downsample=None, baseWidth=26, scale=4, stype='normal'): |
| """ Constructor |
| Args: |
| inplanes: input channel dimensionality |
| planes: output channel dimensionality |
| stride: conv stride. Replaces pooling layer. |
| downsample: None when stride = 1 |
| baseWidth: basic width of conv3x3 |
| scale: number of scale. |
| type: 'normal': normal set. 'stage': first block of a new stage. |
| """ |
| 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, padding=1, 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): |
| self.inplanes = 64 |
| super(Res2Net, self).__init__() |
| self.baseWidth = baseWidth |
| self.scale = scale |
| 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]) |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
| self.avgpool = nn.AdaptiveAvgPool2d(1) |
| self.fc = nn.Linear(512 * block.expansion, num_classes) |
|
|
| 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): |
| 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, downsample=downsample, |
| stype='stage', baseWidth=self.baseWidth, scale=self.scale)) |
| self.inplanes = planes * block.expansion |
| for i in range(1, blocks): |
| layers.append(block(self.inplanes, planes, baseWidth=self.baseWidth, scale=self.scale)) |
|
|
| return nn.Sequential(*layers) |
|
|
| def forward(self, x): |
| x = self.conv1(x) |
| x = self.bn1(x) |
| x = self.relu(x) |
| x = self.maxpool(x) |
|
|
| x1 = self.layer1(x) |
| x2 = self.layer2(x1) |
| x3 = self.layer3(x2) |
| x4 = self.layer4(x3) |
|
|
| x = self.avgpool(x4) |
| x = x.view(x.size(0), -1) |
| x = self.fc(x) |
|
|
| return [x1, x2, x3, x4] |
|
|
|
|
| def res2net50_v1b(pretrained=False, **kwargs): |
| """Constructs a Res2Net-50_v1b lib. |
| Res2Net-50 refers to the Res2Net-50_v1b_26w_4s. |
| Args: |
| pretrained (bool): If True, returns a lib pre-trained on ImageNet |
| """ |
| 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): |
| """Constructs a Res2Net-50_v1b_26w_4s lib. |
| Args: |
| pretrained (bool): If True, returns a lib pre-trained on ImageNet |
| """ |
| 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=False, **kwargs): |
| """Constructs a Res2Net-50_v1b_26w_4s lib. |
| Args: |
| pretrained (bool): If True, returns a lib pre-trained on ImageNet |
| """ |
| model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs) |
| if pretrained: |
| model_state = torch.load('/data2/zhouhan/SINet/sinet_ori_resize/lib/res2net50_v1b_26w_4s.pth') |
| model.load_state_dict(model_state) |
| |
| return model |
|
|
|
|
| def res2net101_v1b_26w_4s(pretrained=False, **kwargs): |
| """Constructs a Res2Net-50_v1b_26w_4s lib. |
| Args: |
| pretrained (bool): If True, returns a lib pre-trained on ImageNet |
| """ |
| 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 res2net152_v1b_26w_4s(pretrained=False, **kwargs): |
| """Constructs a Res2Net-50_v1b_26w_4s lib. |
| Args: |
| pretrained (bool): If True, returns a lib pre-trained on ImageNet |
| """ |
| 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, 352, 352).cuda(0) |
| model = res2net50_v1b_26w_4s(pretrained=False) |
| model = model.cuda(0) |
| print(model(images).size()) |
|
|
