学习笔记：Pytorch 搭建自己的Faster-RCNN目标检测平台

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1、什么是Faster R-CNN

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2、pytorch-gpu环境配置（跳过）

3、Faster R-CNN整体结构介绍

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Faster-RCNN可以采用多种的主干特征提取网络，常用的有VGG，Resnet，Xception等等。
Faster-RCNN对输入进来的图片尺寸没有固定，但一般会把输入进来的图片短边固定成600.

4、Resnet50-主干特征提取网络介绍

具体学习见：Resnet50

import mathimport torch.nn as nn
from torch.hub import load_state_dict_from_urlclass Bottleneck(nn.Module):expansion = 4 #最后一个卷积层输出通道数相对于输入通道数的倍数def __init__(self, inplanes, planes, stride=1, downsample=None):'''inplanes：输入通道数planes：卷积层输出的通道数stride：卷积的步长，默认为1downsample：是否对输入进行下采样'''super(Bottleneck, self).__init__()#使用1*1卷积核，压缩通道数self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)#二维卷积层self.bn1 = nn.BatchNorm2d(planes)#二维批归一化层#使用3*3卷积核，特征提取，padding=1，在输入的周围使用1个零填充，以保持特征图的尺寸self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)self.bn2 = nn.BatchNorm2d(planes)#使用1*1卷积核，扩张通道数self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)self.bn3 = nn.BatchNorm2d(planes * 4)self.relu = nn.ReLU(inplace=True)self.downsample = downsampleself.stride = stridedef forward(self, x):residual = x#残差out = 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:residual = self.downsample(x)out += residualout = self.relu(out)return outclass ResNet(nn.Module):def __init__(self, block, layers, num_classes=1000):#-----------------------------------##   假设输入进来的图片是600,600,3#-----------------------------------#self.inplanes = 64 #初始化ResNet模型的通道数为64super(ResNet, self).__init__()# 600,600,3 -> 300,300,64self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)self.bn1 = nn.BatchNorm2d(64)self.relu = nn.ReLU(inplace=True)# 300,300,64 -> 150,150,64 最大池化层，用于降低特征图的空间分辨率self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True)#构建4个残差块组成的特征提取部分，每个部分的通道数和空间分辨率逐渐增加# 150,150,64 -> 150,150,256self.layer1 = self._make_layer(block, 64, layers[0])# 150,150,256 -> 75,75,512self.layer2 = self._make_layer(block, 128, layers[1], stride=2)# 75,75,512 -> 38,38,1024 到这里可以获得一个38,38,1024的共享特征层self.layer3 = self._make_layer(block, 256, layers[2], stride=2)# self.layer4被用在classifier模型中self.layer4 = self._make_layer(block, 512, layers[3], stride=2)self.avgpool = nn.AvgPool2d(7)#全局平均池化层 池化核7*7#将最终的特征映射到类别数量的空间self.fc = nn.Linear(512 * block.expansion, num_classes) #全连接层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, nn.BatchNorm2d):m.weight.data.fill_(1)m.bias.data.zero_()def _make_layer(self, block, planes, blocks, stride=1):downsample = None#-------------------------------------------------------------------##   当模型需要进行高和宽的压缩的时候，就需要用到残差边的downsample#-------------------------------------------------------------------#if 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),nn.BatchNorm2d(planes * block.expansion),)layers = []layers.append(block(self.inplanes, planes, stride, downsample))self.inplanes = planes * block.expansionfor i in range(1, blocks):layers.append(block(self.inplanes, planes))return nn.Sequential(*layers)def forward(self, x):x = self.conv1(x)x = self.bn1(x)x = self.relu(x)x = self.maxpool(x)x = self.layer1(x)x = self.layer2(x)x = self.layer3(x)x = self.layer4(x)x = self.avgpool(x)x = x.view(x.size(0), -1)x = self.fc(x)return xdef resnet50(pretrained = False):model = ResNet(Bottleneck, [3, 4, 6, 3])if pretrained:state_dict = load_state_dict_from_url("https://download.pytorch.org/models/resnet50-19c8e357.pth", model_dir="./model_data")model.load_state_dict(state_dict)#----------------------------------------------------------------------------##   获取特征提取部分，从conv1到model.layer3，最终获得一个38,38,1024的特征层#----------------------------------------------------------------------------#features    = list([model.conv1, model.bn1, model.relu, model.maxpool, model.layer1, model.layer2, model.layer3])#----------------------------------------------------------------------------##   获取分类部分，从model.layer4到model.avgpool#----------------------------------------------------------------------------#classifier  = list([model.layer4, model.avgpool])features    = nn.Sequential(*features)classifier  = nn.Sequential(*classifier)return features, classifier

5、RPN-建议框网络构建

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6、Anchors-先验框详解

import numpy as np#--------------------------------------------#
#   生成基础的先验框
#--------------------------------------------#
def generate_anchor_base(base_size=16, ratios=[0.5, 1, 2], anchor_scales=[8, 16, 32]):'''base_size：基础框的大小，默认为16ratios：生成锚框的宽高比，默认为[0.5,1,2]anchor_scales：生成锚框的尺度，默认为[8,16,32]'''#创建一个形状为((len(ratios) * len(anchor_scales), 4)的全零数组，用于存储生成的基础先验框的坐标信息#每个先验框由4个坐标值表示anchor_base = np.zeros((len(ratios) * len(anchor_scales), 4), dtype=np.float32)for i in range(len(ratios)):for j in range(len(anchor_scales)):#使用两个嵌套的循环遍历宽高比和尺度的所有组合，宽高比定义+面积不变性h = base_size * anchor_scales[j] * np.sqrt(ratios[i])w = base_size * anchor_scales[j] * np.sqrt(1. / ratios[i])index = i * len(anchor_scales) + janchor_base[index, 0] = - h / 2.anchor_base[index, 1] = - w / 2.anchor_base[index, 2] = h / 2.anchor_base[index, 3] = w / 2.return anchor_base#--------------------------------------------#
#   对基础先验框进行拓展对应到所有特征点上
#--------------------------------------------#
def _enumerate_shifted_anchor(anchor_base, feat_stride, height, width):#---------------------------------##   计算网格中心点#---------------------------------#'''anchor_base 表示基础先验框的坐标信息；feat_stride 特征点间距步长height 和 width 表示特征图的高度和宽度。'''shift_x             = np.arange(0, width * feat_stride, feat_stride)shift_y             = np.arange(0, height * feat_stride, feat_stride)shift_x, shift_y    = np.meshgrid(shift_x, shift_y)shift               = np.stack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel(),), axis=1)#---------------------------------##   每个网格点上的9个先验框#---------------------------------#A       = anchor_base.shape[0]K       = shift.shape[0]anchor  = anchor_base.reshape((1, A, 4)) + shift.reshape((K, 1, 4))#---------------------------------##   所有的先验框#---------------------------------#anchor  = anchor.reshape((K * A, 4)).astype(np.float32)return anchorif __name__ == "__main__":import matplotlib.pyplot as pltnine_anchors = generate_anchor_base()print(nine_anchors)height, width, feat_stride  = 38,38,16anchors_all                 = _enumerate_shifted_anchor(nine_anchors, feat_stride, height, width)print(np.shape(anchors_all))fig     = plt.figure()ax      = fig.add_subplot(111)plt.ylim(-300,900)plt.xlim(-300,900)shift_x = np.arange(0, width * feat_stride, feat_stride)shift_y = np.arange(0, height * feat_stride, feat_stride)shift_x, shift_y = np.meshgrid(shift_x, shift_y)plt.scatter(shift_x,shift_y)box_widths  = anchors_all[:,2]-anchors_all[:,0]box_heights = anchors_all[:,3]-anchors_all[:,1]for i in [108, 109, 110, 111, 112, 113, 114, 115, 116]:rect = plt.Rectangle([anchors_all[i, 0],anchors_all[i, 1]],box_widths[i],box_heights[i],color="r",fill=False)ax.add_patch(rect)plt.show()