resnet系列+mobilenet v2+pytorch代码实现

一.resnet系列backbone

import torch.nn as nn
import math
import torch.utils.model_zoo as model_zooBatchNorm2d = nn.BatchNorm2d__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'deformable_resnet18', 'deformable_resnet50','resnet152']model_urls = {'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth','resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth','resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth','resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth','resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}def constant_init(module, constant, bias=0):nn.init.constant_(module.weight, constant)if hasattr(module, 'bias'):nn.init.constant_(module.bias, bias)def conv3x3(in_planes, out_planes, stride=1):"""3x3 convolution with padding"""return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,padding=1, bias=False)class BasicBlock(nn.Module):expansion = 1def __init__(self, inplanes, planes, stride=1, downsample=None, dcn=None):super(BasicBlock, self).__init__()self.with_dcn = dcn is not Noneself.conv1 = conv3x3(inplanes, planes, stride)self.bn1 = BatchNorm2d(planes)self.relu = nn.ReLU(inplace=True)self.with_modulated_dcn = Falseif not self.with_dcn:self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False)else:from torchvision.ops import DeformConv2ddeformable_groups = dcn.get('deformable_groups', 1)offset_channels = 18self.conv2_offset = nn.Conv2d(planes, deformable_groups * offset_channels, kernel_size=3, padding=1)self.conv2 = DeformConv2d(planes, planes, kernel_size=3, padding=1, bias=False)self.bn2 = BatchNorm2d(planes)self.downsample = downsampleself.stride = stridedef forward(self, x):residual = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)if not self.with_dcn:out = self.conv2(out)else:offset = self.conv2_offset(out)out = self.conv2(out, offset)out = self.bn2(out)if self.downsample is not None:residual = self.downsample(x)out += residualout = self.relu(out)return outclass Bottleneck(nn.Module):expansion = 4def __init__(self, inplanes, planes, stride=1, downsample=None, dcn=None):super(Bottleneck, self).__init__()self.with_dcn = dcn is not Noneself.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)self.bn1 = BatchNorm2d(planes)self.with_modulated_dcn = Falseif not self.with_dcn:self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)else:deformable_groups = dcn.get('deformable_groups', 1)from torchvision.ops import DeformConv2doffset_channels = 18self.conv2_offset = nn.Conv2d(planes, deformable_groups * offset_channels, stride=stride, kernel_size=3, padding=1)self.conv2 = DeformConv2d(planes, planes, kernel_size=3, padding=1, stride=stride, bias=False)self.bn2 = BatchNorm2d(planes)self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)self.bn3 = BatchNorm2d(planes * 4)self.relu = nn.ReLU(inplace=True)self.downsample = downsampleself.stride = strideself.dcn = dcnself.with_dcn = dcn is not Nonedef forward(self, x):residual = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)# out = self.conv2(out)if not self.with_dcn:out = self.conv2(out)else:offset = self.conv2_offset(out)out = self.conv2(out, offset)out = self.bn2(out)out = self.relu(out)out = self.conv3(out)out = self.bn3(out)if self.downsample is not None:residual = self.downsample(x)out += residualout = self.relu(out)return outclass ResNet(nn.Module):def __init__(self, block, layers, in_channels=3, dcn=None):self.dcn = dcnself.inplanes = 64super(ResNet, self).__init__()self.out_channels = []self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3,bias=False)self.bn1 = BatchNorm2d(64)self.relu = nn.ReLU(inplace=True)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, dcn=dcn)self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dcn=dcn)self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dcn=dcn)for m in self.modules():if isinstance(m, nn.Conv2d):n = m.kernel_size[0] * m.kernel_size[1] * m.out_channelsm.weight.data.normal_(0, math.sqrt(2. / n))elif isinstance(m, BatchNorm2d):m.weight.data.fill_(1)m.bias.data.zero_()if self.dcn is not None:for m in self.modules():if isinstance(m, Bottleneck) or isinstance(m, BasicBlock):if hasattr(m, 'conv2_offset'):constant_init(m.conv2_offset, 0)def _make_layer(self, block, planes, blocks, stride=1, dcn=None):downsample = Noneif stride != 1 or self.inplanes != planes * block.expansion:downsample = nn.Sequential(nn.Conv2d(self.inplanes, planes * block.expansion,kernel_size=1, stride=stride, bias=False),BatchNorm2d(planes * block.expansion),)layers = []layers.append(block(self.inplanes, planes, stride, downsample, dcn=dcn))self.inplanes = planes * block.expansionfor i in range(1, blocks):layers.append(block(self.inplanes, planes, dcn=dcn))self.out_channels.append(planes * block.expansion)return nn.Sequential(*layers)def forward(self, x):x = self.conv1(x)x = self.bn1(x)x = self.relu(x)x = self.maxpool(x)x2 = self.layer1(x)x3 = self.layer2(x2)x4 = self.layer3(x3)x5 = self.layer4(x4)return x2, x3, x4, x5def resnet18(pretrained=True, **kwargs):"""Constructs a ResNet-18 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)if pretrained:assert kwargs['in_channels'] == 3, 'in_channels must be 3 whem pretrained is True'print('load from imagenet')model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False)return modeldef deformable_resnet18(pretrained=True, **kwargs):"""Constructs a ResNet-18 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(BasicBlock, [2, 2, 2, 2], dcn=dict(deformable_groups=1), **kwargs)if pretrained:assert kwargs['in_channels'] == 3, 'in_channels must be 3 whem pretrained is True'print('load from imagenet')model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False)return modeldef resnet34(pretrained=True, **kwargs):"""Constructs a ResNet-34 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)if pretrained:assert kwargs['in_channels'] == 3, 'in_channels must be 3 whem pretrained is True'model.load_state_dict(model_zoo.load_url(model_urls['resnet34']), strict=False)return modeldef resnet50(pretrained=True, **kwargs):"""Constructs a ResNet-50 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)if pretrained:assert kwargs['in_channels'] == 3, 'in_channels must be 3 whem pretrained is True'model.load_state_dict(model_zoo.load_url(model_urls['resnet50']), strict=False)return modeldef deformable_resnet50(pretrained=True, **kwargs):"""Constructs a ResNet-50 model with deformable conv.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(Bottleneck, [3, 4, 6, 3], dcn=dict(deformable_groups=1), **kwargs)if pretrained:assert kwargs['in_channels'] == 3, 'in_channels must be 3 whem pretrained is True'model.load_state_dict(model_zoo.load_url(model_urls['resnet50']), strict=False)return modeldef resnet101(pretrained=True, **kwargs):"""Constructs a ResNet-101 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)if pretrained:assert kwargs['in_channels'] == 3, 'in_channels must be 3 whem pretrained is True'model.load_state_dict(model_zoo.load_url(model_urls['resnet101']), strict=False)return modeldef resnet152(pretrained=True, **kwargs):"""Constructs a ResNet-152 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)if pretrained:assert kwargs['in_channels'] == 3, 'in_channels must be 3 whem pretrained is True'model.load_state_dict(model_zoo.load_url(model_urls['resnet152']), strict=False)return modelif __name__ == '__main__':import torchx = torch.zeros(2, 3, 640, 640)# net = deformable_resnet50(pretrained=False)model_resnet50 = resnet101(pretrained=False)y = model_resnet50(x)for u in y:print(u.shape)# print(model_resnet34.out_channels)

二.mobilenet v2

2.1基础知识

１．采用inverted residual，与resnet不一样的是通道１X1卷积先变宽－＞卷积提特征－>１X1卷积变窄,因为经过1x1的卷积扩大通道数以后，可以提升抽取特征的能力，图１所示。

２．最后不采用Relu，而使用Linear代替，因为降维后特征丢失部分，如果采用Relu还会丢失，图２所示.

　　　　　　　　　　　　　　　　图１　inverted residual

　　　　　　　　　　　　图２宽窄通道relu丢失信息对比

上图是嵌入高维空间的低维流形的ReLU变换示例。在这些例子中，使用随机矩阵T和ReLU将第一个图的螺旋嵌入到n维空间中，然后使用T的逆矩阵投影回2D空间。当n = 2，3导致信息损失很多，恢复后的张量坍缩严重，而对于n = 15到30，不会丢失太多的输入信息。显然当通道数较多时，如果输入流形可嵌入激活空间的显着较低维的子空间，则ReLU变换将保留信息。当通道数较少时，通道的信息很可能被丢弃。

综上所述，在通道数较少的层后，应该用线性激活代替ReLU。MobileNet V2的Linear bottleneck Inverted residual block中，降维后的1X1卷积层后接的是一个线性激活，其他情况用的是ReLU。

v1使用可分离卷积的计算减少量,如果k=3，也就是３x3卷积核，能后减少９倍左右的计算量．(k^2+dj)/(k^2*dj)

2.2 代码实现

１.BottleNeck实现

import torch.nn as nn
import torch
class BottleNeck(nn.Module):def __init__(self, inchannles, outchannels, expansion=1, stride=1, downsample=None):super(BottleNeck, self).__init__()#1*1self.conv1 = nn.Conv2d(inchannles, inchannles*expansion, kernel_size=1)self.bn1 = nn.BatchNorm2d(inchannles*expansion)#3*3 可分离卷积　groups设置self.conv2 = nn.Conv2d(inchannles*expansion, inchannles * expansion, kernel_size=3, padding=1, stride=stride,groups=inchannles * expansion)self.bn2 = nn.BatchNorm2d(inchannles * expansion)#1*1self.conv3 = nn.Conv2d(inchannles*expansion, outchannels, kernel_size=1)self.bn3 = nn.BatchNorm2d(outchannels)self.relu = nn.ReLU(inplace=True)self.downsample = downsampledef forward(self, x):residul = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out = self.relu(out)out = self.conv3(out)out = self.bn3(out)if self.downsample is not None:residul = self.downsample(x)out += residulout = self.relu(out)return out
def BottleNeck_test():inchannels = 3outchannels = 3stride =2downsample_ = nn.Sequential(nn.Conv2d(inchannels, outchannels, kernel_size=1, stride=stride),nn.BatchNorm2d(outchannels))bottleneck = BottleNeck(inchannels, outchannels, expansion=2, downsample=downsample_, stride=stride)print('bottleneck:', bottleneck)x = torch.rand((8, 3, 224, 224))out = bottleneck(x)print('out.shape', out.shape)if __name__ == '__main__':BottleNeck_test()

２．整体代码，加了权重初始化

import torch.nn as nn
import torch
class BottleNeck(nn.Module):def __init__(self, inchannles, outchannels, expansion=1, stride=1, downsample=None):super(BottleNeck, self).__init__()#1*1self.conv1 = nn.Conv2d(inchannles, inchannles*expansion, kernel_size=1)self.bn1 = nn.BatchNorm2d(inchannles*expansion)#3*3 可分离卷积　groups设置self.conv2 = nn.Conv2d(inchannles*expansion, inchannles * expansion, kernel_size=3, padding=1, stride=stride,groups=inchannles * expansion)self.bn2 = nn.BatchNorm2d(inchannles * expansion)#1*1self.conv3 = nn.Conv2d(inchannles*expansion, outchannels, kernel_size=1)self.bn3 = nn.BatchNorm2d(outchannels)self.relu = nn.ReLU(inplace=True)self.downsample = downsampledef forward(self, x):residul = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out = self.relu(out)out = self.conv3(out)out = self.bn3(out)if self.downsample is not None:residul = self.downsample(x)out += residulout = self.relu(out)return outclass MobileNetV2(nn.Module):def __init__(self, n, numclasses=1000):super(MobileNetV2, self).__init__()self.inchannels = 32self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1)self.bn1 = nn.BatchNorm2d(32)self.relu = nn.ReLU(inplace=True)self.layer1 = self.make_layer(n[0], outchannels=16, stride=1, expansion=1)self.layer2 = self.make_layer(n[1], outchannels=24, stride=2, expansion=6)self.layer3 = self.make_layer(n[2], outchannels=32, stride=2, expansion=6)self.layer4 = self.make_layer(n[3], outchannels=64, stride=2, expansion=6)self.layer5 = self.make_layer(n[4], outchannels=96, stride=1, expansion=6)self.layer6 = self.make_layer(n[5], outchannels=160, stride=2, expansion=6)self.layer7 = self.make_layer(n[6], outchannels=320, stride=1, expansion=1)self.conv8 = nn.Conv2d(320, 1280, kernel_size=1, stride=1)self.avegpool = nn.AvgPool2d(7, stride=1)self.conv9 = nn.Conv2d(1280, numclasses, kernel_size=1, stride=1)def make_layer(self, blocks_num, outchannels, stride, expansion):downsample_ = nn.Sequential(nn.Conv2d(self.inchannels, outchannels, kernel_size=1, stride=stride),nn.BatchNorm2d(outchannels))layers = []#下采样的shortcut有downsampletemp = BottleNeck(self.inchannels, outchannels, expansion=expansion, stride=stride, downsample=downsample_)layers.append(temp)#剩下的shortcut干净self.inchannels = outchannelsfor i in range(1, blocks_num):layers.append(BottleNeck(self.inchannels, outchannels, expansion=expansion, stride=1))return nn.Sequential(*layers)#取出每一层def forward(self, x):x = self.conv1(x)print('conv1.shape:', x.shape)x = self.bn1(x)x = self.relu(x)x = self.layer1(x)print('layer1.shape:', x.shape)x = self.layer2(x)print('layer2.shape:', x.shape)x = self.layer3(x)print('layer3.shape:', x.shape)x = self.layer4(x)print('layer4.shape:', x.shape)x = self.layer5(x)print('layer5.shape:', x.shape)x = self.layer6(x)print('layer6.shape:', x.shape)x = self.layer7(x)print('layer7.shape:', x.shape)x = self.conv8(x)print('conv8.shape:', x.shape)x = self.avegpool(x)print('avegpool:', x.shape)x = self.conv9(x)print('conv9.shape:', x.shape)x = x.view(x.size(0), -1)return xdef BottleNeck_test():inchannels = 3outchannels = 3stride =2downsample_ = nn.Sequential(nn.Conv2d(inchannels, outchannels, kernel_size=1, stride=stride),nn.BatchNorm2d(outchannels))bottleneck = BottleNeck(inchannels, outchannels, expansion=2, downsample=downsample_, stride=stride)print('bottleneck:', bottleneck)x = torch.rand((8, 3, 224, 224))out = bottleneck(x)print('out.shape', out.shape)def weigth_init(m):if isinstance(m, nn.Conv2d):nn.init.xavier_uniform_(m.weight.data)nn.init.constant_(m.bias.data, 0.1)elif isinstance(m, nn.BatchNorm2d):m.weight.data.fill_(1)m.bias.data.zero_()elif isinstance(m, nn.Linear):m.weight.data.normal_(0,0.01)m.bias.data.zero_()# print('weigth_init!')
def MobileNetV2_test():model = MobileNetV2(n=[1, 2, 3, 4, 3, 3, 1], numclasses=10)model.apply(weigth_init)# print('model:', model)x = torch.rand((8, 3, 224, 224))out = model(x)print('out.shape', out.shape)
if __name__ == '__main__':# BottleNeck_test()MobileNetV2_test()

1.0
==img.shape: torch.Size([1, 3, 256, 192])
==x.shape: torch.Size([1, 32, 128, 96])
==i, x.shape==: 0 torch.Size([1, 16, 128, 96])
==i, x.shape==: 1 torch.Size([1, 24, 64, 48])
==i, x.shape==: 2 torch.Size([1, 32, 32, 24])
==i, x.shape==: 3 torch.Size([1, 64, 16, 12])
==i, x.shape==: 4 torch.Size([1, 96, 16, 12])
==i, x.shape==: 5 torch.Size([1, 160, 8, 6])
==i, x.shape==: 6 torch.Size([1, 320, 8, 6])
==i, x.shape==: 7 torch.Size([1, 1280, 8, 6])0.5==img.shape: torch.Size([1, 3, 256, 192])
==x.shape: torch.Size([1, 16, 128, 96])
==i, x.shape==: 0 torch.Size([1, 8, 128, 96])
==i, x.shape==: 1 torch.Size([1, 16, 64, 48])
==i, x.shape==: 2 torch.Size([1, 16, 32, 24])
==i, x.shape==: 3 torch.Size([1, 32, 16, 12])
==i, x.shape==: 4 torch.Size([1, 48, 16, 12])
==i, x.shape==: 5 torch.Size([1, 80, 8, 6])
==i, x.shape==: 6 torch.Size([1, 160, 8, 6])
==i, x.shape==: 7 torch.Size([1, 640, 8, 6])

参考：

从MobileNet V1到MobileNet V2 - 知乎