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 mathfrom collections import OrderedDictimport 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 = x out = self.conv1(x) out = self.bn1(out) out = self.relu1(out) out = self.conv2(out) out = self.bn2(out) out = self.relu2(out) out += residual return outclass DarkNet(nn.Module): def __init__(self, layers): super(DarkNet, self).__init__() self.inplanes = 32 # 416,416,3 -> 416,416,32 self.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 -> 208,208,64 self.layer1 = self._make_layer([32, 64], layers[0]) # 208,208,64 -> 104,104,128 self.layer2 = self._make_layer([64, 128], layers[1]) # 104,104,128 -> 52,52,256 self.layer3 = self._make_layer([128, 256], layers[2]) # 52,52,256 -> 26,26,512 self.layer4 = self._make_layer([256, 512], layers[3]) # 26,26,512 -> 13,13,1024 self.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_channels m.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3 layers.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 torch from torchinfo import summary input=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 torchimport torch.nn as nnfrom nets.darknet import darknet53def conv2d(filter_in, filter_out, kernel_size): pad = (kernel_size - 1) // 2 if kernel_size else 0 return 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 -> 13,13,512 out0_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 -> 26,26,256 x1_in = self.last_layer1_conv(out0_branch) x1_in = self.last_layer1_upsample(x1_in) # 26,26,256 + 26,26,512 -> 26,26,768 x1_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 -> 26,26,256 out1_branch = self.last_layer1[:5](x1_in) out1 = self.last_layer1[5:](out1_branch) # 26,26,256 -> 26,26,128 -> 52,52,128 x2_in = self.last_layer2_conv(out1_branch) x2_in = self.last_layer2_upsample(x2_in) # 52,52,128 + 52,52,256 -> 52,52,384 x2_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 -> 52,52,128 out2 = self.last_layer2(x2_in) return out0, out1, out2if __name__=='__main__': import torch from torchinfo import summary input=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 torchimport torch.nn as nnfrom torchvision.ops import nmsimport 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 = anchors self.num_classes = num_classes self.bbox_attrs = 5 + num_classes self.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_mask def 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_height stride_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.FloatTensor LongTensor = 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_x pred_boxes[..., 1] = y.data + grid_y pred_boxes[..., 2] = torch.exp(w.data) * anchor_w pred_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 outputs def 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_shape scale = input_shape/new_shape box_yx = (box_yx - offset) * scale box_hw *= scale box_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 boxes def 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] / 2 box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2 box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2 box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2 prediction[:, :, :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 outputs output[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 mathfrom functools import partialimport numpy as npimport torchimport 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 = anchors self.num_classes = num_classes self.bbox_attrs = 5 + num_classes self.input_shape = input_shape self.anchors_mask = anchors_mask self.giou = True self.balance = [0.4, 1.0, 4] self.box_ratio = 0.05 self.obj_ratio = 5 * (input_shape[0] * input_shape[1]) / (416 ** 2) self.cls_ratio = 1 * (num_classes / 80) self.ignore_threshold = 0.5 self.cuda = cuda def clip_by_tensor(self, t, t_min, t_max): t = t.float() result = (t >= t_min).float() * t + (t < t_min).float() * t_min result = (result t_max).float() * t_max return result def MSELoss(self, pred, target): return torch.pow(pred - target, 2) def BCELoss(self, pred, target): epsilon = 1e-7 pred = self.clip_by_tensor(pred, epsilon, 1.0 - epsilon) output = - target * torch.log(pred) - (1.0 - target) * torch.log(1.0 - pred) return output def box_giou(self, b1, b2): """ 输入为： ---------- b1: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), xywh b2: 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_half b1_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_half b2_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_area iou = 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_area return giou def 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_h stride_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_scale loss = 0 obj_mask = y_true[..., 4] == 1 n = 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) * 0.1 loss_cls = torch.mean(self.BCELoss(pred_cls[obj_mask], y_true[..., 5:][obj_mask])) loss += loss_loc * self.box_ratio + loss_cls * self.cls_ratio loss_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 loss def 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] / 2 b1_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] / 2 b2_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_y2 box_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 - inter return 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: continue 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_w batch_target[:, [1,3]] = targets[b][:, [1,3]] * in_h batch_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] = 1 y_true[b, k, j, i, c + 5] = 1 else: #----------------------------------------# # 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] = 1 y_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_h return y_true, noobj_mask, box_loss_scale def 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_w batch_target[:, [1,3]] = targets[b][:, [1,3]] * in_h batch_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] = 0 return 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 = total_iters - no_aug_iter: lr = min_lr else: 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 lr def step_lr(lr, decay_rate, step_size, iters): if step_size < 1: raise ValueError("step_size must above 1.") n = iters // step_size out_lr = lr * decay_rate ** n return out_lr if 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_num func = 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