| 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()) |
|
|
