yolov3的整体网络结构
主要包含了两个部分。左边的Darknet-53主干特征提取网络主要用于提取特征。右边是一个FPN金字塔结构。
主干特征提取网络（提取特征）
import math
from collections import OrderedDict
import torch.nn as nn#---------------------------------------------------------------------#
# 残差结构
# 利用一个1x1卷积下降通道数，然后利用一个3x3卷积提取特征并且上升通道数
# 最后接上一个残差边
#---------------------------------------------------------------------#
class BasicBlock(nn.Module):def __init__(self, inplanes, planes):super(BasicBlock, self).__init__()self.conv1 = nn.Conv2d(inplanes, planes[0], kernel_size=1, stride=1, padding=0, bias=False)self.bn1 = nn.BatchNorm2d(planes[0])self.relu1 = nn.LeakyReLU(0.1)self.conv2 = nn.Conv2d(planes[0], planes[1], kernel_size=3, stride=1, padding=1, bias=False)self.bn2 = nn.BatchNorm2d(planes[1])self.relu2 = nn.LeakyReLU(0.1)def forward(self, x):residual = xout = self.conv1(x)out = self.bn1(out)out = self.relu1(out)out = self.conv2(out)out = self.bn2(out)out = self.relu2(out)out += residualreturn outclass DarkNet(nn.Module):def __init__(self, layers):super(DarkNet, self).__init__()self.inplanes = 32# 416,416,3 -> 416,416,32self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)self.bn1 = nn.BatchNorm2d(self.inplanes)self.relu1 = nn.LeakyReLU(0.1)# 416,416,32 -> 208,208,64self.layer1 = self._make_layer([32, 64], layers[0])# 208,208,64 -> 104,104,128self.layer2 = self._make_layer([64, 128], layers[1])# 104,104,128 -> 52,52,256self.layer3 = self._make_layer([128, 256], layers[2])# 52,52,256 -> 26,26,512self.layer4 = self._make_layer([256, 512], layers[3])# 26,26,512 -> 13,13,1024self.layer5 = self._make_layer([512, 1024], layers[4])self.layers_out_filters = [64, 128, 256, 512, 1024]# 进行权值初始化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_()#---------------------------------------------------------------------## 在每一个layer里面，首先利用一个步长为2的3x3卷积进行下采样# 然后进行残差结构的堆叠#---------------------------------------------------------------------#def _make_layer(self, planes, blocks):layers = []# 下采样，步长为2，卷积核大小为3layers.append(("ds_conv", nn.Conv2d(self.inplanes, planes[1], kernel_size=3, stride=2, padding=1, bias=False)))layers.append(("ds_bn", nn.BatchNorm2d(planes[1])))layers.append(("ds_relu", nn.LeakyReLU(0.1)))# 加入残差结构self.inplanes = planes[1]for i in range(0, blocks):layers.append(("residual_{}".format(i), BasicBlock(self.inplanes, planes)))return nn.Sequential(OrderedDict(layers))def forward(self, x):x = self.conv1(x)x = self.bn1(x)x = self.relu1(x)x = self.layer1(x)x = self.layer2(x)out3 = self.layer3(x)out4 = self.layer4(out3)out5 = self.layer5(out4)return out3, out4, out5def darknet53():model = DarkNet([1, 2, 8, 8, 4])return modelif __name__=='__main__':import torchfrom torchinfo import summaryinput=torch.randn(1,3,416,416)model=darknet53()summary(model,input.shape)output=model(input)print(output[0].shape,output[1].shape,output[2].shape)
FPN特征金子塔加强特征提取和利用yolo head预测结果
from collections import OrderedDictimport torch
import torch.nn as nnfrom nets.darknet import darknet53def conv2d(filter_in, filter_out, kernel_size):pad = (kernel_size - 1) // 2 if kernel_size else 0return nn.Sequential(OrderedDict([("conv", nn.Conv2d(filter_in, filter_out, kernel_size=kernel_size, stride=1, padding=pad, bias=False)),("bn", nn.BatchNorm2d(filter_out)),("relu", nn.LeakyReLU(0.1)),]))#------------------------------------------------------------------------#
# make_last_layers里面一共有七个卷积，前五个用于提取特征。
# 后两个用于获得yolo网络的预测结果
#------------------------------------------------------------------------#
def make_last_layers(filters_list, in_filters, out_filter): #in_filters 表示输入通道，out_filter表示输出通道m = nn.Sequential(conv2d(in_filters, filters_list[0], 1),conv2d(filters_list[0], filters_list[1], 3),conv2d(filters_list[1], filters_list[0], 1),conv2d(filters_list[0], filters_list[1], 3),conv2d(filters_list[1], filters_list[0], 1),conv2d(filters_list[0], filters_list[1], 3),nn.Conv2d(filters_list[1], out_filter, kernel_size=1, stride=1, padding=0, bias=True))return mclass YoloBody(nn.Module):def __init__(self, anchors_mask, num_classes, pretrained = False):super(YoloBody, self).__init__()#---------------------------------------------------# # 生成darknet53的主干模型# 获得三个有效特征层，他们的shape分别是：# 52,52,256# 26,26,512# 13,13,1024#---------------------------------------------------#self.backbone = darknet53()if pretrained:self.backbone.load_state_dict(torch.load("model_data/darknet53_backbone_weights.pth"))#---------------------------------------------------## out_filters : [64, 128, 256, 512, 1024] 自己定义的属性，表示darknet五个残差模块中输出的特征通道数#---------------------------------------------------#out_filters = self.backbone.layers_out_filters#------------------------------------------------------------------------## 计算yolo_head的输出通道数，对于voc数据集而言# final_out_filter0 = final_out_filter1 = final_out_filter2 = 75#------------------------------------------------------------------------#self.last_layer0 = make_last_layers([512, 1024], out_filters[-1], len(anchors_mask[0]) * (num_classes + 5))self.last_layer1_conv = conv2d(512, 256, 1)self.last_layer1_upsample = nn.Upsample(scale_factor=2, mode='nearest')self.last_layer1 = make_last_layers([256, 512], out_filters[-2] + 256, len(anchors_mask[1]) * (num_classes + 5))self.last_layer2_conv = conv2d(256, 128, 1)self.last_layer2_upsample = nn.Upsample(scale_factor=2, mode='nearest')self.last_layer2 = make_last_layers([128, 256], out_filters[-3] + 128, len(anchors_mask[2]) * (num_classes + 5))def forward(self, x):#---------------------------------------------------# # 获得三个有效特征层，他们的shape分别是：# 52,52,256；26,26,512；13,13,1024#---------------------------------------------------#x2, x1, x0 = self.backbone(x)#---------------------------------------------------## 第一个特征层# out0 = (batch_size,255,13,13)#---------------------------------------------------## 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512out0_branch = self.last_layer0[:5](x0) ###得到特征增强的特征out0 = self.last_layer0[5:](out0_branch) ##进行回归预测# 13,13,512 -> 13,13,256 -> 26,26,256x1_in = self.last_layer1_conv(out0_branch)x1_in = self.last_layer1_upsample(x1_in)# 26,26,256 + 26,26,512 -> 26,26,768x1_in = torch.cat([x1_in, x1], 1)#---------------------------------------------------## 第二个特征层# out1 = (batch_size,255,26,26)#---------------------------------------------------## 26,26,768 -> 26,26,256 -> 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256out1_branch = self.last_layer1[:5](x1_in)out1 = self.last_layer1[5:](out1_branch)# 26,26,256 -> 26,26,128 -> 52,52,128x2_in = self.last_layer2_conv(out1_branch)x2_in = self.last_layer2_upsample(x2_in)# 52,52,128 + 52,52,256 -> 52,52,384x2_in = torch.cat([x2_in, x2], 1)#---------------------------------------------------## 第一个特征层# out3 = (batch_size,255,52,52)#---------------------------------------------------## 52,52,384 -> 52,52,128 -> 52,52,256 -> 52,52,128 -> 52,52,256 -> 52,52,128out2 = self.last_layer2(x2_in)return out0, out1, out2if __name__=='__main__':import torchfrom torchinfo import summaryinput=torch.randn(1,3,416,416)model=YoloBody(anchors_mask=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],num_classes=20)summary(model,input.shape)output=model(input)print(output[0].shape,output[1].shape,output[2].shape)
预测结果进行解码
最后网络的输出格式就像上图一样。其中13表示特征图的大小，表示将整个图像分为了13*13的网格。每个网络点具有3个先验框。所以75可以分解为3*25，其中3表示这个网络点具有三个先验框。25可以分解为20+1+4，其中20表示该先验框的分类结果，这里使用的是VOC数据集，VOC数据集共有20个类别。1表示置信度，表示该先验框包含物体的概率。4用来表示先验框的位置信息。
YoloV3的解码过程分为两步：
- 先将每个网格点加上它对应的x_offset和y_offset，加完后的结果就是预测框的中心。
- 然后再利用 先验框和h、w结合 计算出预测框的宽高。这样就能得到整个预测框的位置了。
import torch
import torch.nn as nn
from torchvision.ops import nms
import numpy as npclass DecodeBox():def __init__(self, anchors, num_classes, input_shape, anchors_mask = [[6,7,8], [3,4,5], [0,1,2]]):super(DecodeBox, self).__init__()self.anchors = anchorsself.num_classes = num_classesself.bbox_attrs = 5 + num_classesself.input_shape = input_shape#-----------------------------------------------------------## 13x13的特征层对应的anchor是[116,90],[156,198],[373,326]# 26x26的特征层对应的anchor是[30,61],[62,45],[59,119]# 52x52的特征层对应的anchor是[10,13],[16,30],[33,23]#-----------------------------------------------------------#self.anchors_mask = anchors_maskdef decode_box(self, inputs):outputs = []for i, input in enumerate(inputs):#-----------------------------------------------## 输入的input一共有三个，他们的shape分别是# batch_size, 255, 13, 13# batch_size, 255, 26, 26# batch_size, 255, 52, 52#-----------------------------------------------#batch_size = input.size(0)input_height = input.size(2)input_width = input.size(3)#-----------------------------------------------## 输入为416x416时# stride_h = stride_w = 32、16、8#-----------------------------------------------#stride_h = self.input_shape[0] / input_heightstride_w = self.input_shape[1] / input_width#-------------------------------------------------## 此时获得的scaled_anchors大小是相对于特征层的#-------------------------------------------------#scaled_anchors = [(anchor_width / stride_w, anchor_height / stride_h) for anchor_width, anchor_height in self.anchors[self.anchors_mask[i]]]#-----------------------------------------------## 输入的input一共有三个，他们的shape分别是# batch_size, 3, 13, 13, 85# batch_size, 3, 26, 26, 85# batch_size, 3, 52, 52, 85#-----------------------------------------------#prediction = input.view(batch_size, len(self.anchors_mask[i]),self.bbox_attrs, input_height, input_width).permute(0, 1, 3, 4, 2).contiguous()#-----------------------------------------------## 先验框的中心位置的调整参数#-----------------------------------------------#x = torch.sigmoid(prediction[..., 0]) y = torch.sigmoid(prediction[..., 1])#-----------------------------------------------## 先验框的宽高调整参数#-----------------------------------------------#w = prediction[..., 2]h = prediction[..., 3]#-----------------------------------------------## 获得置信度，是否有物体#-----------------------------------------------#conf = torch.sigmoid(prediction[..., 4])#-----------------------------------------------## 种类置信度#-----------------------------------------------#pred_cls = torch.sigmoid(prediction[..., 5:])FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensorLongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor#----------------------------------------------------------## 生成网格，先验框中心，网格左上角 # batch_size,3,13,13#----------------------------------------------------------#grid_x = torch.linspace(0, input_width - 1, input_width).repeat(input_height, 1).repeat(batch_size * len(self.anchors_mask[i]), 1, 1).view(x.shape).type(FloatTensor)grid_y = torch.linspace(0, input_height - 1, input_height).repeat(input_width, 1).t().repeat(batch_size * len(self.anchors_mask[i]), 1, 1).view(y.shape).type(FloatTensor)#----------------------------------------------------------## 按照网格格式生成先验框的宽高# batch_size,3,13,13#----------------------------------------------------------#anchor_w = FloatTensor(scaled_anchors).index_select(1, LongTensor([0]))anchor_h = FloatTensor(scaled_anchors).index_select(1, LongTensor([1]))anchor_w = anchor_w.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(w.shape)anchor_h = anchor_h.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(h.shape)#----------------------------------------------------------## 利用预测结果对先验框进行调整# 首先调整先验框的中心，从先验框中心向右下角偏移# 再调整先验框的宽高。#----------------------------------------------------------#pred_boxes = FloatTensor(prediction[..., :4].shape)pred_boxes[..., 0] = x.data + grid_xpred_boxes[..., 1] = y.data + grid_ypred_boxes[..., 2] = torch.exp(w.data) * anchor_wpred_boxes[..., 3] = torch.exp(h.data) * anchor_h#----------------------------------------------------------## 将输出结果归一化成小数的形式#----------------------------------------------------------#_scale = torch.Tensor([input_width, input_height, input_width, input_height]).type(FloatTensor)output = torch.cat((pred_boxes.view(batch_size, -1, 4) / _scale,conf.view(batch_size, -1, 1), pred_cls.view(batch_size, -1, self.num_classes)), -1)outputs.append(output.data)return outputsdef yolo_correct_boxes(self, box_xy, box_wh, input_shape, image_shape, letterbox_image):#-----------------------------------------------------------------## 把y轴放前面是因为方便预测框和图像的宽高进行相乘#-----------------------------------------------------------------#box_yx = box_xy[..., ::-1]box_hw = box_wh[..., ::-1]input_shape = np.array(input_shape)image_shape = np.array(image_shape)if letterbox_image:#-----------------------------------------------------------------## 这里求出来的offset是图像有效区域相对于图像左上角的偏移情况# new_shape指的是宽高缩放情况#-----------------------------------------------------------------#new_shape = np.round(image_shape * np.min(input_shape/image_shape))offset = (input_shape - new_shape)/2./input_shapescale = input_shape/new_shapebox_yx = (box_yx - offset) * scalebox_hw *= scalebox_mins = box_yx - (box_hw / 2.)box_maxes = box_yx + (box_hw / 2.)boxes = np.concatenate([box_mins[..., 0:1], box_mins[..., 1:2], box_maxes[..., 0:1], box_maxes[..., 1:2]], axis=-1)boxes *= np.concatenate([image_shape, image_shape], axis=-1)return boxesdef non_max_suppression(self, prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5, nms_thres=0.4):#----------------------------------------------------------## 将预测结果的格式转换成左上角右下角的格式。# prediction [batch_size, num_anchors, 85]#----------------------------------------------------------#box_corner = prediction.new(prediction.shape)box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2prediction[:, :, :4] = box_corner[:, :, :4]output = [None for _ in range(len(prediction))]for i, image_pred in enumerate(prediction):#----------------------------------------------------------## 对种类预测部分取max。# class_conf [num_anchors, 1] 种类置信度# class_pred [num_anchors, 1] 种类#----------------------------------------------------------#class_conf, class_pred = torch.max(image_pred[:, 5:5 + num_classes], 1, keepdim=True)#----------------------------------------------------------## 利用置信度进行第一轮筛选#----------------------------------------------------------#conf_mask = (image_pred[:, 4] * class_conf[:, 0] >= conf_thres).squeeze()#----------------------------------------------------------## 根据置信度进行预测结果的筛选#----------------------------------------------------------#image_pred = image_pred[conf_mask]class_conf = class_conf[conf_mask]class_pred = class_pred[conf_mask]if not image_pred.size(0):continue#-------------------------------------------------------------------------## detections [num_anchors, 7]# 7的内容为：x1, y1, x2, y2, obj_conf, class_conf, class_pred#-------------------------------------------------------------------------#detections = torch.cat((image_pred[:, :5], class_conf.float(), class_pred.float()), 1)#------------------------------------------## 获得预测结果中包含的所有种类#------------------------------------------#unique_labels = detections[:, -1].cpu().unique()if prediction.is_cuda:unique_labels = unique_labels.cuda()detections = detections.cuda()for c in unique_labels:#------------------------------------------## 获得某一类得分筛选后全部的预测结果#------------------------------------------#detections_class = detections[detections[:, -1] == c]#------------------------------------------## 使用官方自带的非极大抑制会速度更快一些！#------------------------------------------#keep = nms(detections_class[:, :4],detections_class[:, 4] * detections_class[:, 5],nms_thres)max_detections = detections_class[keep]# # 按照存在物体的置信度排序# _, conf_sort_index = torch.sort(detections_class[:, 4]*detections_class[:, 5], descending=True)# detections_class = detections_class[conf_sort_index]# # 进行非极大抑制# max_detections = []# while detections_class.size(0):# # 取出这一类置信度最高的，一步一步往下判断，判断重合程度是否大于nms_thres，如果是则去除掉# max_detections.append(detections_class[0].unsqueeze(0))# if len(detections_class) == 1:# break# ious = bbox_iou(max_detections[-1], detections_class[1:])# detections_class = detections_class[1:][ious < nms_thres]# # 堆叠# max_detections = torch.cat(max_detections).data# Add max detections to outputsoutput[i] = max_detections if output[i] is None else torch.cat((output[i], max_detections))if output[i] is not None:output[i] = output[i].cpu().numpy()box_xy, box_wh = (output[i][:, 0:2] + output[i][:, 2:4])/2, output[i][:, 2:4] - output[i][:, 0:2]output[i][:, :4] = self.yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)return outputif __name__=='__main__':# ---------------------------------------------------## 获得先验框# ---------------------------------------------------#def get_anchors(anchors_path):'''loads the anchors from a file'''with open(anchors_path, encoding='utf-8') as f:anchors = f.readline()anchors = [float(x) for x in anchors.split(',')]anchors = np.array(anchors).reshape(-1, 2)return anchors, len(anchors)# ---------------------------------------------------## 获得类# ---------------------------------------------------#def get_classes(classes_path):with open(classes_path, encoding='utf-8') as f:class_names = f.readlines()class_names = [c.strip() for c in class_names]return class_names, len(class_names)anchors,anchors_num=get_anchors('../model_data/yolo_anchors.txt')##anchcors表示的是先验框print(anchors)anchors_mask= [[6, 7, 8], [3, 4, 5], [0, 1, 2]]#class_name 表示类别的名称class_name,class_num=get_classes('../model_data/voc_classes.txt')print(class_name)#输入图片的大小input_shape=[416,416]decode_box=DecodeBox(anchors=anchors,num_classes=class_num,input_shape=(input_shape[0],input_shape[1]),anchors_mask=anchors_mask)from nets.yolo import YoloBody#定义模型model=YoloBody(anchors_mask=anchors_mask,num_classes=20)input=torch.randn(1,3,416,416)outputs=model(input)print(outputs[0].shape)print(outputs[1].shape)print(outputs[2].shape)outputs=decode_box.decode_box(outputs)print('outputs长度',len(outputs))print('outputs shape',outputs[0].shape)result=decode_box.non_max_suppression(prediction=torch.cat(outputs, 1),num_classes=class_num,input_shape=input_shape,image_shape=np.array([416,416]),letterbox_image=False,conf_thres=0.5,nms_thres=0.3)print(type(result))print(len(result))print(result)# print('非极大抑制',result)
loss的计算
判断真实框在图片中的位置，判断其属于哪一个网格点去检测。判断真实框和这个特征点的哪个先验框重合程度最高。计算该网格点应该有怎么样的预测结果才能获得真实框，与真实框重合度最高的先验框被用于作为正样本。
根据网络的预测结果获得预测框，计算预测框和所有真实框的重合程度，如果重合程度大于一定门限，则将该预测框对应的先验框忽略。其余作为负样本。
最终损失由三个部分组成：a、正样本，编码后的长宽与xy轴偏移量与预测值的差距。b、正样本，预测结果中置信度的值与1对比；负样本，预测结果中置信度的值与0对比。c、实际存在的框，种类预测结果与实际结果的对比。
import math
from functools import partialimport numpy as np
import torch
import torch.nn as nnclass YOLOLoss(nn.Module):def __init__(self, anchors, num_classes, input_shape, cuda, anchors_mask = [[6,7,8], [3,4,5], [0,1,2]]):super(YOLOLoss, self).__init__()#-----------------------------------------------------------## 13x13的特征层对应的anchor是[116,90],[156,198],[373,326]# 26x26的特征层对应的anchor是[30,61],[62,45],[59,119]# 52x52的特征层对应的anchor是[10,13],[16,30],[33,23]#-----------------------------------------------------------#self.anchors = anchorsself.num_classes = num_classesself.bbox_attrs = 5 + num_classesself.input_shape = input_shapeself.anchors_mask = anchors_maskself.giou = Trueself.balance = [0.4, 1.0, 4]self.box_ratio = 0.05self.obj_ratio = 5 * (input_shape[0] * input_shape[1]) / (416 ** 2)self.cls_ratio = 1 * (num_classes / 80)self.ignore_threshold = 0.5self.cuda = cudadef clip_by_tensor(self, t, t_min, t_max):t = t.float()result = (t >= t_min).float() * t + (t < t_min).float() * t_minresult = (result <= t_max).float() * result + (result > t_max).float() * t_maxreturn resultdef MSELoss(self, pred, target):return torch.pow(pred - target, 2)def BCELoss(self, pred, target):epsilon = 1e-7pred = self.clip_by_tensor(pred, epsilon, 1.0 - epsilon)output = - target * torch.log(pred) - (1.0 - target) * torch.log(1.0 - pred)return outputdef box_giou(self, b1, b2):"""输入为：----------b1: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), xywhb2: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), xywh返回为：-------giou: tensor, shape=(batch, feat_w, feat_h, anchor_num, 1)"""#----------------------------------------------------## 求出预测框左上角右下角#----------------------------------------------------#b1_xy = b1[..., :2]b1_wh = b1[..., 2:4]b1_wh_half = b1_wh/2.b1_mins = b1_xy - b1_wh_halfb1_maxes = b1_xy + b1_wh_half#----------------------------------------------------## 求出真实框左上角右下角#----------------------------------------------------#b2_xy = b2[..., :2]b2_wh = b2[..., 2:4]b2_wh_half = b2_wh/2.b2_mins = b2_xy - b2_wh_halfb2_maxes = b2_xy + b2_wh_half#----------------------------------------------------## 求真实框和预测框所有的iou#----------------------------------------------------#intersect_mins = torch.max(b1_mins, b2_mins)intersect_maxes = torch.min(b1_maxes, b2_maxes)intersect_wh = torch.max(intersect_maxes - intersect_mins, torch.zeros_like(intersect_maxes))intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]b1_area = b1_wh[..., 0] * b1_wh[..., 1]b2_area = b2_wh[..., 0] * b2_wh[..., 1]union_area = b1_area + b2_area - intersect_areaiou = intersect_area / union_area#----------------------------------------------------## 找到包裹两个框的最小框的左上角和右下角#----------------------------------------------------#enclose_mins = torch.min(b1_mins, b2_mins)enclose_maxes = torch.max(b1_maxes, b2_maxes)enclose_wh = torch.max(enclose_maxes - enclose_mins, torch.zeros_like(intersect_maxes))#----------------------------------------------------## 计算对角线距离#----------------------------------------------------#enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1]giou = iou - (enclose_area - union_area) / enclose_areareturn gioudef forward(self, l, input, targets=None):#----------------------------------------------------## l代表的是，当前输入进来的有效特征层，是第几个有效特征层# input的shape为 bs, 3*(5+num_classes), 13, 13# bs, 3*(5+num_classes), 26, 26# bs, 3*(5+num_classes), 52, 52# targets代表的是真实框。#----------------------------------------------------##--------------------------------## 获得图片数量，特征层的高和宽# 13和13#--------------------------------#bs = input.size(0)in_h = input.size(2)in_w = input.size(3)#-----------------------------------------------------------------------## 计算步长# 每一个特征点对应原来的图片上多少个像素点# 如果特征层为13x13的话，一个特征点就对应原来的图片上的32个像素点# 如果特征层为26x26的话，一个特征点就对应原来的图片上的16个像素点# 如果特征层为52x52的话，一个特征点就对应原来的图片上的8个像素点# stride_h = stride_w = 32、16、8# stride_h和stride_w都是32。#-----------------------------------------------------------------------#stride_h = self.input_shape[0] / in_hstride_w = self.input_shape[1] / in_w#-------------------------------------------------## 此时获得的scaled_anchors大小是相对于特征层的#-------------------------------------------------#scaled_anchors = [(a_w / stride_w, a_h / stride_h) for a_w, a_h in self.anchors]#-----------------------------------------------## 输入的input一共有三个，他们的shape分别是# bs, 3*(5+num_classes), 13, 13 => batch_size, 3, 13, 13, 5 + num_classes# batch_size, 3, 26, 26, 5 + num_classes# batch_size, 3, 52, 52, 5 + num_classes#-----------------------------------------------#prediction = input.view(bs, len(self.anchors_mask[l]), self.bbox_attrs, in_h, in_w).permute(0, 1, 3, 4, 2).contiguous()#-----------------------------------------------## 先验框的中心位置的调整参数#-----------------------------------------------#x = torch.sigmoid(prediction[..., 0])y = torch.sigmoid(prediction[..., 1])#-----------------------------------------------## 先验框的宽高调整参数#-----------------------------------------------#w = prediction[..., 2]h = prediction[..., 3]#-----------------------------------------------## 获得置信度，是否有物体#-----------------------------------------------#conf = torch.sigmoid(prediction[..., 4])#-----------------------------------------------## 种类置信度#-----------------------------------------------#pred_cls = torch.sigmoid(prediction[..., 5:])#-----------------------------------------------## 获得网络应该有的预测结果#-----------------------------------------------#y_true, noobj_mask, box_loss_scale = self.get_target(l, targets, scaled_anchors, in_h, in_w)#---------------------------------------------------------------## 将预测结果进行解码，判断预测结果和真实值的重合程度# 如果重合程度过大则忽略，因为这些特征点属于预测比较准确的特征点# 作为负样本不合适#----------------------------------------------------------------#noobj_mask, pred_boxes = self.get_ignore(l, x, y, h, w, targets, scaled_anchors, in_h, in_w, noobj_mask)if self.cuda:y_true = y_true.type_as(x)noobj_mask = noobj_mask.type_as(x)box_loss_scale = box_loss_scale.type_as(x)#--------------------------------------------------------------------------## box_loss_scale是真实框宽高的乘积，宽高均在0-1之间，因此乘积也在0-1之间。# 2-宽高的乘积代表真实框越大，比重越小，小框的比重更大。#--------------------------------------------------------------------------#box_loss_scale = 2 - box_loss_scaleloss = 0obj_mask = y_true[..., 4] == 1n = torch.sum(obj_mask)if n != 0:if self.giou:#---------------------------------------------------------------## 计算预测结果和真实结果的giou#----------------------------------------------------------------#giou = self.box_giou(pred_boxes, y_true[..., :4]).type_as(x)loss_loc = torch.mean((1 - giou)[obj_mask])else:#-----------------------------------------------------------## 计算中心偏移情况的loss，使用BCELoss效果好一些#-----------------------------------------------------------#loss_x = torch.mean(self.BCELoss(x[obj_mask], y_true[..., 0][obj_mask]) * box_loss_scale[obj_mask])loss_y = torch.mean(self.BCELoss(y[obj_mask], y_true[..., 1][obj_mask]) * box_loss_scale[obj_mask])#-----------------------------------------------------------## 计算宽高调整值的loss#-----------------------------------------------------------#loss_w = torch.mean(self.MSELoss(w[obj_mask], y_true[..., 2][obj_mask]) * box_loss_scale[obj_mask])loss_h = torch.mean(self.MSELoss(h[obj_mask], y_true[..., 3][obj_mask]) * box_loss_scale[obj_mask])loss_loc = (loss_x + loss_y + loss_h + loss_w) * 0.1loss_cls = torch.mean(self.BCELoss(pred_cls[obj_mask], y_true[..., 5:][obj_mask]))loss += loss_loc * self.box_ratio + loss_cls * self.cls_ratioloss_conf = torch.mean(self.BCELoss(conf, obj_mask.type_as(conf))[noobj_mask.bool() | obj_mask])loss += loss_conf * self.balance[l] * self.obj_ratio# if n != 0:# print(loss_loc * self.box_ratio, loss_cls * self.cls_ratio, loss_conf * self.balance[l] * self.obj_ratio)return lossdef calculate_iou(self, _box_a, _box_b):#-----------------------------------------------------------## 计算真实框的左上角和右下角#-----------------------------------------------------------#b1_x1, b1_x2 = _box_a[:, 0] - _box_a[:, 2] / 2, _box_a[:, 0] + _box_a[:, 2] / 2b1_y1, b1_y2 = _box_a[:, 1] - _box_a[:, 3] / 2, _box_a[:, 1] + _box_a[:, 3] / 2#-----------------------------------------------------------## 计算先验框获得的预测框的左上角和右下角#-----------------------------------------------------------#b2_x1, b2_x2 = _box_b[:, 0] - _box_b[:, 2] / 2, _box_b[:, 0] + _box_b[:, 2] / 2b2_y1, b2_y2 = _box_b[:, 1] - _box_b[:, 3] / 2, _box_b[:, 1] + _box_b[:, 3] / 2#-----------------------------------------------------------## 将真实框和预测框都转化成左上角右下角的形式#-----------------------------------------------------------#box_a = torch.zeros_like(_box_a)box_b = torch.zeros_like(_box_b)box_a[:, 0], box_a[:, 1], box_a[:, 2], box_a[:, 3] = b1_x1, b1_y1, b1_x2, b1_y2box_b[:, 0], box_b[:, 1], box_b[:, 2], box_b[:, 3] = b2_x1, b2_y1, b2_x2, b2_y2#-----------------------------------------------------------## A为真实框的数量，B为先验框的数量#-----------------------------------------------------------#A = box_a.size(0)B = box_b.size(0)#-----------------------------------------------------------## 计算交的面积#-----------------------------------------------------------#max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), box_b[:, 2:].unsqueeze(0).expand(A, B, 2))min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), box_b[:, :2].unsqueeze(0).expand(A, B, 2))inter = torch.clamp((max_xy - min_xy), min=0)inter = inter[:, :, 0] * inter[:, :, 1]#-----------------------------------------------------------## 计算预测框和真实框各自的面积#-----------------------------------------------------------#area_a = ((box_a[:, 2]-box_a[:, 0]) * (box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B]area_b = ((box_b[:, 2]-box_b[:, 0]) * (box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B]#-----------------------------------------------------------## 求IOU#-----------------------------------------------------------#union = area_a + area_b - interreturn inter / union # [A,B]def get_target(self, l, targets, anchors, in_h, in_w):#-----------------------------------------------------## 计算一共有多少张图片#-----------------------------------------------------#bs = len(targets)#-----------------------------------------------------## 用于选取哪些先验框不包含物体#-----------------------------------------------------#noobj_mask = torch.ones(bs, len(self.anchors_mask[l]), in_h, in_w, requires_grad = False)#-----------------------------------------------------## 让网络更加去关注小目标#-----------------------------------------------------#box_loss_scale = torch.zeros(bs, len(self.anchors_mask[l]), in_h, in_w, requires_grad = False)#-----------------------------------------------------## batch_size, 3, 13, 13, 5 + num_classes#-----------------------------------------------------#y_true = torch.zeros(bs, len(self.anchors_mask[l]), in_h, in_w, self.bbox_attrs, requires_grad = False)for b in range(bs): if len(targets[b])==0:continuebatch_target = torch.zeros_like(targets[b])#-------------------------------------------------------## 计算出正样本在特征层上的中心点#-------------------------------------------------------#batch_target[:, [0,2]] = targets[b][:, [0,2]] * in_wbatch_target[:, [1,3]] = targets[b][:, [1,3]] * in_hbatch_target[:, 4] = targets[b][:, 4]batch_target = batch_target.cpu()#-------------------------------------------------------## 将真实框转换一个形式# num_true_box, 4#-------------------------------------------------------#gt_box = torch.FloatTensor(torch.cat((torch.zeros((batch_target.size(0), 2)), batch_target[:, 2:4]), 1))#-------------------------------------------------------## 将先验框转换一个形式# 9, 4#-------------------------------------------------------#anchor_shapes = torch.FloatTensor(torch.cat((torch.zeros((len(anchors), 2)), torch.FloatTensor(anchors)), 1))#-------------------------------------------------------## 计算交并比# self.calculate_iou(gt_box, anchor_shapes) = [num_true_box, 9]每一个真实框和9个先验框的重合情况# best_ns:# [每个真实框最大的重合度max_iou, 每一个真实框最重合的先验框的序号]#-------------------------------------------------------#best_ns = torch.argmax(self.calculate_iou(gt_box, anchor_shapes), dim=-1)for t, best_n in enumerate(best_ns):if best_n not in self.anchors_mask[l]:continue#----------------------------------------## 判断这个先验框是当前特征点的哪一个先验框#----------------------------------------#k = self.anchors_mask[l].index(best_n)#----------------------------------------## 获得真实框属于哪个网格点#----------------------------------------#i = torch.floor(batch_target[t, 0]).long()j = torch.floor(batch_target[t, 1]).long()#----------------------------------------## 取出真实框的种类#----------------------------------------#c = batch_target[t, 4].long()#----------------------------------------## noobj_mask代表无目标的特征点#----------------------------------------#noobj_mask[b, k, j, i] = 0#----------------------------------------## tx、ty代表中心调整参数的真实值#----------------------------------------#if not self.giou:#----------------------------------------## tx、ty代表中心调整参数的真实值#----------------------------------------#y_true[b, k, j, i, 0] = batch_target[t, 0] - i.float()y_true[b, k, j, i, 1] = batch_target[t, 1] - j.float()y_true[b, k, j, i, 2] = math.log(batch_target[t, 2] / anchors[best_n][0])y_true[b, k, j, i, 3] = math.log(batch_target[t, 3] / anchors[best_n][1])y_true[b, k, j, i, 4] = 1y_true[b, k, j, i, c + 5] = 1else:#----------------------------------------## tx、ty代表中心调整参数的真实值#----------------------------------------#y_true[b, k, j, i, 0] = batch_target[t, 0]y_true[b, k, j, i, 1] = batch_target[t, 1]y_true[b, k, j, i, 2] = batch_target[t, 2]y_true[b, k, j, i, 3] = batch_target[t, 3]y_true[b, k, j, i, 4] = 1y_true[b, k, j, i, c + 5] = 1#----------------------------------------## 用于获得xywh的比例# 大目标loss权重小，小目标loss权重大#----------------------------------------#box_loss_scale[b, k, j, i] = batch_target[t, 2] * batch_target[t, 3] / in_w / in_hreturn y_true, noobj_mask, box_loss_scaledef get_ignore(self, l, x, y, h, w, targets, scaled_anchors, in_h, in_w, noobj_mask):#-----------------------------------------------------## 计算一共有多少张图片#-----------------------------------------------------#bs = len(targets)#-----------------------------------------------------## 生成网格，先验框中心，网格左上角#-----------------------------------------------------#grid_x = torch.linspace(0, in_w - 1, in_w).repeat(in_h, 1).repeat(int(bs * len(self.anchors_mask[l])), 1, 1).view(x.shape).type_as(x)grid_y = torch.linspace(0, in_h - 1, in_h).repeat(in_w, 1).t().repeat(int(bs * len(self.anchors_mask[l])), 1, 1).view(y.shape).type_as(x)# 生成先验框的宽高scaled_anchors_l = np.array(scaled_anchors)[self.anchors_mask[l]]anchor_w = torch.Tensor(scaled_anchors_l).index_select(1, torch.LongTensor([0])).type_as(x)anchor_h = torch.Tensor(scaled_anchors_l).index_select(1, torch.LongTensor([1])).type_as(x)anchor_w = anchor_w.repeat(bs, 1).repeat(1, 1, in_h * in_w).view(w.shape)anchor_h = anchor_h.repeat(bs, 1).repeat(1, 1, in_h * in_w).view(h.shape)#-------------------------------------------------------## 计算调整后的先验框中心与宽高#-------------------------------------------------------#pred_boxes_x = torch.unsqueeze(x + grid_x, -1)pred_boxes_y = torch.unsqueeze(y + grid_y, -1)pred_boxes_w = torch.unsqueeze(torch.exp(w) * anchor_w, -1)pred_boxes_h = torch.unsqueeze(torch.exp(h) * anchor_h, -1)pred_boxes = torch.cat([pred_boxes_x, pred_boxes_y, pred_boxes_w, pred_boxes_h], dim = -1)for b in range(bs): #-------------------------------------------------------## 将预测结果转换一个形式# pred_boxes_for_ignore num_anchors, 4#-------------------------------------------------------#pred_boxes_for_ignore = pred_boxes[b].view(-1, 4)#-------------------------------------------------------## 计算真实框，并把真实框转换成相对于特征层的大小# gt_box num_true_box, 4#-------------------------------------------------------#if len(targets[b]) > 0:batch_target = torch.zeros_like(targets[b])#-------------------------------------------------------## 计算出正样本在特征层上的中心点#-------------------------------------------------------#batch_target[:, [0,2]] = targets[b][:, [0,2]] * in_wbatch_target[:, [1,3]] = targets[b][:, [1,3]] * in_hbatch_target = batch_target[:, :4].type_as(x)#-------------------------------------------------------## 计算交并比# anch_ious num_true_box, num_anchors#-------------------------------------------------------#anch_ious = self.calculate_iou(batch_target, pred_boxes_for_ignore)#-------------------------------------------------------## 每个先验框对应真实框的最大重合度# anch_ious_max num_anchors#-------------------------------------------------------#anch_ious_max, _ = torch.max(anch_ious, dim = 0)anch_ious_max = anch_ious_max.view(pred_boxes[b].size()[:3])noobj_mask[b][anch_ious_max > self.ignore_threshold] = 0return noobj_mask, pred_boxesdef weights_init(net, init_type='normal', init_gain = 0.02):def init_func(m):classname = m.__class__.__name__if hasattr(m, 'weight') and classname.find('Conv') != -1:if init_type == 'normal':torch.nn.init.normal_(m.weight.data, 0.0, init_gain)elif init_type == 'xavier':torch.nn.init.xavier_normal_(m.weight.data, gain=init_gain)elif init_type == 'kaiming':torch.nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')elif init_type == 'orthogonal':torch.nn.init.orthogonal_(m.weight.data, gain=init_gain)else:raise NotImplementedError('initialization method [%s] is not implemented' % init_type)elif classname.find('BatchNorm2d') != -1:torch.nn.init.normal_(m.weight.data, 1.0, 0.02)torch.nn.init.constant_(m.bias.data, 0.0)print('initialize network with %s type' % init_type)net.apply(init_func)def get_lr_scheduler(lr_decay_type, lr, min_lr, total_iters, warmup_iters_ratio = 0.05, warmup_lr_ratio = 0.1, no_aug_iter_ratio = 0.05, step_num = 10):def yolox_warm_cos_lr(lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter, iters):if iters <= warmup_total_iters:# lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_startlr = (lr - warmup_lr_start) * pow(iters / float(warmup_total_iters), 2) + warmup_lr_startelif iters >= total_iters - no_aug_iter:lr = min_lrelse:lr = min_lr + 0.5 * (lr - min_lr) * (1.0 + math.cos(math.pi* (iters - warmup_total_iters) / (total_iters - warmup_total_iters - no_aug_iter)))return lrdef step_lr(lr, decay_rate, step_size, iters):if step_size < 1:raise ValueError("step_size must above 1.")n = iters // step_sizeout_lr = lr * decay_rate ** nreturn out_lrif lr_decay_type == "cos":warmup_total_iters = min(max(warmup_iters_ratio * total_iters, 1), 3)warmup_lr_start = max(warmup_lr_ratio * lr, 1e-6)no_aug_iter = min(max(no_aug_iter_ratio * total_iters, 1), 15)func = partial(yolox_warm_cos_lr ,lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter)else:decay_rate = (min_lr / lr) ** (1 / (step_num - 1))step_size = total_iters / step_numfunc = partial(step_lr, lr, decay_rate, step_size)return funcdef set_optimizer_lr(optimizer, lr_scheduler_func, epoch):lr = lr_scheduler_func(epoch)for param_group in optimizer.param_groups:param_group['lr'] = lr第一次看yolov3的代码，感觉代码好多啊，里面的原理很多都不太清楚。慢慢学吧
参考文献：
YOLOv3详解 - 简书 (jianshu.com)
睿智的目标检测26——Pytorch搭建yolo3目标检测平台_Bubbliiiing的博客-CSDN博客_睿智的目标检测
