| 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()) | |