PyTorch——YOLOv1代码学习笔记

文章目录

  • 数据读取 dataset.py
  • 损失函数 yoloLoss.py

数据读取 dataset.py

txt格式:[图片名字 目标个数 左上角坐标x 左上角坐标y 右下角坐标x 右下角坐标y 类别]
数据读取代码部分最终返回的item是(img, label),其中img是读取并处理好的图像矩阵224x224大小,label是一个7x7x30的矩阵,包含了bbox坐标和类别信息。一张图被分为7x7的网格;30=(2x5+20),一个网格预测两个框 , 一个网格预测所有分类概率,VOC数据集分类为20类。
PyTorch——YOLOv1代码学习笔记_第1张图片

#encoding:utf-8
#
'''
txt描述文件 image_name.jpg   num  x_min y_min x_max y_max  c    x_min y_min x_max y_max  c 这样就是说一张图片中有两个目标
'''
import os
import sys
import os.path

import random
import numpy as np

import torch
import torch.utils.data as data
import torchvision.transforms as transforms

import cv2

class yoloDataset(data.Dataset):
    '''
    自定义封装数据集    
    '''
    image_size = 224
    def __init__(self,root,list_file,train,transform):
        print('数据初始化')
        self.root=root
        self.train = train
        self.transform=transform    #对图像转化
        self.fnames = []            #图像名字
        self.boxes = []
        self.labels = []
        self.mean = (123,117,104)   #RGB均值

        with open(list_file) as f:
            lines  = f.readlines()

        # 遍历voc2012train.txt每一行
        for line in lines:
            splited = line.strip().split()
            # 赋值图像名字
            self.fnames.append(splited[0])
            # 赋值一张图的物体总数
            num_faces = int(splited[1])
            box=[]
            label=[]
            # 遍历一张图的所有物体
            #  bbox坐标(4个值)   物体对应的类的序号(1个值)  所以要加5*i
            for i in range(num_faces):
                x = float(splited[2+5*i])
                y = float(splited[3+5*i])
                x2 = float(splited[4+5*i])
                y2 = float(splited[5+5*i])
                c = splited[6+5*i]
                box.append([x,y,x2,y2])
                label.append(int(c)+1)
            # bbox  写入所有物体的坐标值
            self.boxes.append(torch.Tensor(box))
            # label 写入标签
            self.labels.append(torch.LongTensor(label))
        # 数据集中图像总数
        self.num_samples = len(self.boxes)

    def __getitem__(self,idx):
        '''
        继承Dataset,需实现该方法,得到一个item
        '''
        fname = self.fnames[idx]
        # 读取图像
        img = cv2.imread(os.path.join(self.root+fname))
        # clone 深复制,不共享内存
        # 拿出对应的bbox及 标签对应的序号
        boxes = self.boxes[idx].clone()
        labels = self.labels[idx].clone()

        # 如果为训练集,进行数据增强
        if self.train:
            # 随机翻转
            img, boxes = self.random_flip(img, boxes)
            #固定住高度,以0.6-1.4伸缩宽度,做图像形变
            img,boxes = self.randomScale(img,boxes)
            # 随机模糊
            img = self.randomBlur(img)
            # 随机亮度
            img = self.RandomBrightness(img)
            # 随机色调
            img = self.RandomHue(img)
            # 随机饱和度
            img = self.RandomSaturation(img)
            # 随机转换
            img,boxes,labels = self.randomShift(img,boxes,labels)

        h,w,_ = img.shape
        boxes /= torch.Tensor([w,h,w,h]).expand_as(boxes)
        img = self.BGR2RGB(img) #因为pytorch自身提供的预训练好的模型期望的输入是RGB
        img = self.subMean(img,self.mean) #减去均值
        img = cv2.resize(img,(self.image_size,self.image_size))   #改变形状到(224,224)
        # 拿到图像对应的真值,以便计算loss
        target = self.encoder(boxes,labels)# 7x7x30    一张图被分为7x7的网格;30=(2x5+20) 一个网格预测两个框   一个网格预测所有分类概率,VOC数据集分类为20类
        # 图像转化
        for t in self.transform:
            img = t(img)
        #返回 最终处理好的img 以及 对应的 真值target(形状为网络的输出结果的大小)
        return img,target
    def __len__(self):
        '''
        继承Dataset,需实现该方法,得到数据集中图像总数
        '''
        return self.num_samples

    def encoder(self,boxes,labels):
        '''
        boxes (tensor) [[x1,y1,x2,y2],[x1,y1,x2,y2],[]]
        labels (tensor) [...]
        return 7x7x30
        '''
        target = torch.zeros((7,7,30))
        cell_size = 1./7
        # boxes[:, 2:]代表  2: 代表xmax,ymax
        # boxes[:, :2]代表  :2  代表xmin,ymin
        # wh代表  bbox的宽(xmax-xmin)和高(ymax-ymin)
        wh = boxes[:,2:]-boxes[:,:2]
        # bbox的中心点坐标
        cxcy = (boxes[:,2:]+boxes[:,:2])/2
        # cxcy.size()[0]代表 一张图像的物体总数
        # 遍历一张图像的物体总数
        for i in range(cxcy.size()[0]):
            # 拿到第i行数据,即第i个bbox的中心点坐标(相对于整张图,取值在0-1之间)
            cxcy_sample = cxcy[i]
            # ceil返回数字的上入整数
            # cxcy_sample为一个物体的中心点坐标,求该坐标位于7x7网格的哪个网格
            # cxcy_sample坐标在0-1之间  现在求它再0-7之间的值,故乘以7
            # ij长度为2,代表7x7框的某一个框 负责预测一个物体
            ij = (cxcy_sample/cell_size).ceil()-1
            # 每行的第4和第9的值设置为1,即每个网格提供的两个真实候选框 框住物体的概率是1.
            #xml中坐标理解:原图像左上角为原点,右边为x轴,下边为y轴。
            # 而二维矩阵(x,y)  x代表第几行,y代表第几列
            # 假设ij为(1,2) 代表x轴方向长度为1,y轴方向长度为2
            # 二维矩阵取(2,1) 从0开始,代表第2行,第1列的值
            # 画一下图就明白了
            target[int(ij[1]),int(ij[0]),4] = 1
            target[int(ij[1]),int(ij[0]),9] = 1
            # 加9是因为前0-9为两个真实候选款的值。后10-20为20分类   将对应分类标为1
            target[int(ij[1]),int(ij[0]),int(labels[i])+9] = 1
            # 匹配到的网格的左上角的坐标(取值在0-1之间)(原作者)
            # 根据二维矩阵的性质,从上到下  从左到右
            xy = ij*cell_size
            #cxcy_sample:第i个bbox的中心点坐标     xy:匹配到的网格的左上角相对坐标
            # delta_xy:真实框的中心点坐标相对于  位于该中心点所在网格的左上角   的相对坐标,此时可以将网格的左上角看做原点,你这点相对于原点的位置。取值在0-1,但是比1/7小
            delta_xy = (cxcy_sample -xy)/cell_size
            # x,y代表了检测框中心相对于网格边框的坐标。w,h的取值相对于整幅图像的尺寸
            # 写入一个网格对应两个框的x,y,   wh:bbox的宽(xmax-xmin)和高(ymax-ymin)(取值在0-1之间)
            target[int(ij[1]),int(ij[0]),2:4] = wh[i]
            target[int(ij[1]),int(ij[0]),:2] = delta_xy
            target[int(ij[1]),int(ij[0]),7:9] = wh[i]
            target[int(ij[1]),int(ij[0]),5:7] = delta_xy
        return target
    def BGR2RGB(self,img):
        return cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    def BGR2HSV(self,img):
        return cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
    def HSV2BGR(self,img):
        return cv2.cvtColor(img,cv2.COLOR_HSV2BGR)
    
    def RandomBrightness(self,bgr):
        if random.random() < 0.5:
            hsv = self.BGR2HSV(bgr)
            h,s,v = cv2.split(hsv)
            adjust = random.choice([0.5,1.5])
            v = v*adjust
            v = np.clip(v, 0, 255).astype(hsv.dtype)
            hsv = cv2.merge((h,s,v))
            bgr = self.HSV2BGR(hsv)
        return bgr
    def RandomSaturation(self,bgr):
        if random.random() < 0.5:
            hsv = self.BGR2HSV(bgr)
            h,s,v = cv2.split(hsv)
            adjust = random.choice([0.5,1.5])
            s = s*adjust
            s = np.clip(s, 0, 255).astype(hsv.dtype)
            hsv = cv2.merge((h,s,v))
            bgr = self.HSV2BGR(hsv)
        return bgr
    def RandomHue(self,bgr):
        if random.random() < 0.5:
            hsv = self.BGR2HSV(bgr)
            h,s,v = cv2.split(hsv)
            adjust = random.choice([0.5,1.5])
            h = h*adjust
            h = np.clip(h, 0, 255).astype(hsv.dtype)
            hsv = cv2.merge((h,s,v))
            bgr = self.HSV2BGR(hsv)
        return bgr

    def randomBlur(self,bgr):
        '''
         随机模糊
        '''
        if random.random()<0.5:
            bgr = cv2.blur(bgr,(5,5))
        return bgr

    def randomShift(self,bgr,boxes,labels):
        #平移变换
        center = (boxes[:,2:]+boxes[:,:2])/2
        if random.random() <0.5:
            height,width,c = bgr.shape
            after_shfit_image = np.zeros((height,width,c),dtype=bgr.dtype)
            after_shfit_image[:,:,:] = (104,117,123) #bgr
            shift_x = random.uniform(-width*0.2,width*0.2)
            shift_y = random.uniform(-height*0.2,height*0.2)
            #print(bgr.shape,shift_x,shift_y)
            #原图像的平移
            if shift_x>=0 and shift_y>=0:
                after_shfit_image[int(shift_y):,int(shift_x):,:] = bgr[:height-int(shift_y),:width-int(shift_x),:]
            elif shift_x>=0 and shift_y<0:
                after_shfit_image[:height+int(shift_y),int(shift_x):,:] = bgr[-int(shift_y):,:width-int(shift_x),:]
            elif shift_x <0 and shift_y >=0:
                after_shfit_image[int(shift_y):,:width+int(shift_x),:] = bgr[:height-int(shift_y),-int(shift_x):,:]
            elif shift_x<0 and shift_y<0:
                after_shfit_image[:height+int(shift_y),:width+int(shift_x),:] = bgr[-int(shift_y):,-int(shift_x):,:]

            shift_xy = torch.FloatTensor([[int(shift_x),int(shift_y)]]).expand_as(center)
            center = center + shift_xy
            mask1 = (center[:,0] >0) & (center[:,0] < width)
            mask2 = (center[:,1] >0) & (center[:,1] < height)
            mask = (mask1 & mask2).view(-1,1)
            boxes_in = boxes[mask.expand_as(boxes)].view(-1,4)
            if len(boxes_in) == 0:
                return bgr,boxes,labels
            box_shift = torch.FloatTensor([[int(shift_x),int(shift_y),int(shift_x),int(shift_y)]]).expand_as(boxes_in)
            boxes_in = boxes_in+box_shift
            labels_in = labels[mask.view(-1)]
            return after_shfit_image,boxes_in,labels_in
        return bgr,boxes,labels

    def randomScale(self,bgr,boxes):
        #固定住高度,以0.6-1.4伸缩宽度,做图像形变
        if random.random() < 0.5:
            scale = random.uniform(0.6,1.4)
            height,width,c = bgr.shape
            bgr = cv2.resize(bgr,(int(width*scale),height))
            scale_tensor = torch.FloatTensor([[scale,1,scale,1]]).expand_as(boxes)
            boxes = boxes * scale_tensor
            return bgr,boxes
        return bgr,boxes

    def randomCrop(self,bgr,boxes,labels):
        if random.random() < 0.5:
            center = (boxes[:,2:]+boxes[:,:2])/2
            height,width,c = bgr.shape
            h = random.uniform(0.6*height,height)
            w = random.uniform(0.6*width,width)
            x = random.uniform(0,width-w)
            y = random.uniform(0,height-h)
            x,y,h,w = int(x),int(y),int(h),int(w)

            center = center - torch.FloatTensor([[x,y]]).expand_as(center)
            mask1 = (center[:,0]>0) & (center[:,0]<w)
            mask2 = (center[:,1]>0) & (center[:,1]<h)
            mask = (mask1 & mask2).view(-1,1)

            boxes_in = boxes[mask.expand_as(boxes)].view(-1,4)
            if(len(boxes_in)==0):
                return bgr,boxes,labels
            box_shift = torch.FloatTensor([[x,y,x,y]]).expand_as(boxes_in)

            boxes_in = boxes_in - box_shift
            labels_in = labels[mask.view(-1)]
            img_croped = bgr[y:y+h,x:x+w,:]
            return img_croped,boxes_in,labels_in
        return bgr,boxes,labels




    def subMean(self,bgr,mean):
        mean = np.array(mean, dtype=np.float32)
        bgr = bgr - mean
        return bgr

    def random_flip(self, im, boxes):
        '''
        随机翻转
        '''
        if random.random() < 0.5:
            im_lr = np.fliplr(im).copy()
            h,w,_ = im.shape
            xmin = w - boxes[:,2]
            xmax = w - boxes[:,0]
            boxes[:,0] = xmin
            boxes[:,2] = xmax
            return im_lr, boxes
        return im, boxes
    def random_bright(self, im, delta=16):
        alpha = random.random()
        if alpha > 0.3:
            im = im * alpha + random.randrange(-delta,delta)
            im = im.clip(min=0,max=255).astype(np.uint8)
        return im

损失函数 yoloLoss.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

class yoloLoss(nn.Module):
    '''
    定义一个torch.nn中并未实现的网络层,以使得代码更加模块化
    torch.nn.Modules相当于是对网络某种层的封装,包括网络结构以及网络参数,和其他有用的操作如输出参数
    继承Modules类,需实现__init__()方法,以及forward()方法
    '''
    def __init__(self,S,B,l_coord,l_noobj):
        super(yoloLoss,self).__init__()
        self.S = S    #7代表将图像分为7x7的网格
        self.B = B    #2代表一个网格预测两个框
        self.l_coord = l_coord   #5代表 λcoord  更重视8维的坐标预测
        self.l_noobj = l_noobj   #0.5代表没有object的bbox的confidence loss

    def compute_iou(self, box1, box2):
        '''
        计算两个框的重叠率IOU
        通过两组框的联合计算交集,每个框为[x1,y1,x2,y2]。
        Compute the intersection over union of two set of boxes, each box is [x1,y1,x2,y2].
        Args:
          box1: (tensor) bounding boxes, sized [N,4].
          box2: (tensor) bounding boxes, sized [M,4].
        Return:
          (tensor) iou, sized [N,M].
        '''
        N = box1.size(0)
        M = box2.size(0)

        lt = torch.max(
            box1[:,:2].unsqueeze(1).expand(N,M,2),  # [N,2] -> [N,1,2] -> [N,M,2]
            box2[:,:2].unsqueeze(0).expand(N,M,2),  # [M,2] -> [1,M,2] -> [N,M,2]
        )

        rb = torch.min(
            box1[:,2:].unsqueeze(1).expand(N,M,2),  # [N,2] -> [N,1,2] -> [N,M,2]
            box2[:,2:].unsqueeze(0).expand(N,M,2),  # [M,2] -> [1,M,2] -> [N,M,2]
        )

        wh = rb - lt  # [N,M,2]
        # wh(wh<0)= 0  # clip at 0
        wh= (wh < 0).float()
        inter = wh[:,:,0] * wh[:,:,1]  # [N,M]

        area1 = (box1[:,2]-box1[:,0]) * (box1[:,3]-box1[:,1])  # [N,]
        area2 = (box2[:,2]-box2[:,0]) * (box2[:,3]-box2[:,1])  # [M,]
        area1 = area1.unsqueeze(1).expand_as(inter)  # [N,] -> [N,1] -> [N,M]
        area2 = area2.unsqueeze(0).expand_as(inter)  # [M,] -> [1,M] -> [N,M]

        iou = inter / (area1 + area2 - inter)
        return iou

    def forward(self,pred_tensor,target_tensor):
        '''
        pred_tensor: (tensor) size(batchsize,S,S,Bx5+20=30) [x,y,w,h,c]
        target_tensor: (tensor) size(batchsize,S,S,30)

        Mr.Li个人见解:
        本来有,预测无--》计算response loss响应损失
        本来有,预测有--》计算not response loss 未响应损失
        本来无,预测无--》无损失(不计算)
        本来无,预测有--》计算不包含obj损失  只计算第4,9位的有无物体概率的loss
        '''
        # N为batchsize
        N = pred_tensor.size()[0]
        # 坐标mask    4:是物体或者背景的confidence    >0 拿到有物体的记录
        coo_mask = target_tensor[:,:,:,4] > 0
        # 没有物体mask                                 ==0  拿到无物体的记录
        noo_mask = target_tensor[:,:,:,4] == 0
        # unsqueeze(-1) 扩展最后一维,用0填充,使得形状与target_tensor一样
        # coo_mask、noo_mask形状扩充到[32,7,7,30]
        # coo_mask 大部分为0   记录为1代表真实有物体的网格
        # noo_mask  大部分为1  记录为1代表真实无物体的网格
        coo_mask = coo_mask.unsqueeze(-1).expand_as(target_tensor)
        noo_mask = noo_mask.unsqueeze(-1).expand_as(target_tensor)
        # coo_pred 取出预测结果中有物体的网格,并改变形状为(xxx,30)  xxx代表一个batch的图片上的存在物体的网格总数    30代表2*5+20   例如:coo_pred[72,30]
        coo_pred = pred_tensor[coo_mask].view(-1,30)
        # 一个网格预测的两个box  30的前10即为2个x,y,w,h,c,并调整为(xxx,5) xxx为所有真实存在物体的预测框,而非所有真实存在物体的网格     例如:box_pred[144,5]
        # contiguous将不连续的数组调整为连续的数组
        box_pred = coo_pred[:,:10].contiguous().view(-1,5) #box[x1,y1,w1,h1,c1]
                                                            # #[x2,y2,w2,h2,c2]
        # 每个网格预测的类别  后20
        class_pred = coo_pred[:,10:]

        # 对真实标签做同样操作
        coo_target = target_tensor[coo_mask].view(-1,30)
        box_target = coo_target[:,:10].contiguous().view(-1,5)
        class_target = coo_target[:,10:]

        # 计算不包含obj损失  即本来无,预测有
        # 在预测结果中拿到真实无物体的网格,并改变形状为(xxx,30)  xxx代表一个batch的图片上的不存在物体的网格总数    30代表2*5+20   例如:[1496,30]
        noo_pred = pred_tensor[noo_mask].view(-1,30)
        noo_target = target_tensor[noo_mask].view(-1,30)      # 例如:[1496,30]
        # ByteTensor:8-bit integer (unsigned)
        noo_pred_mask = torch.cuda.ByteTensor(noo_pred.size())   # 例如:[1496,30]
        noo_pred_mask.zero_()   #初始化全为0
        # 将第4、9  即有物体的confidence置为1
        noo_pred_mask[:,4]=1;noo_pred_mask[:,9]=1
        # 拿到第4列和第9列里面的值(即拿到真实无物体的网格中,网络预测这些网格有物体的概率值)    一行有两个值(第4和第9位)                           例如noo_pred_c:2992        noo_target_c:2992
        # noo pred只需要计算类别c的损失
        noo_pred_c = noo_pred[noo_pred_mask]
        # 拿到第4列和第9列里面的值  真值为0,真实无物体(即拿到真实无物体的网格中,这些网格有物体的概率值,为0)
        noo_target_c = noo_target[noo_pred_mask]
        # 均方误差    如果 size_average = True,返回 loss.mean()。    例如noo_pred_c:2992        noo_target_c:2992
        # nooobj_loss 一个标量
        nooobj_loss = F.mse_loss(noo_pred_c,noo_target_c,size_average=False)


        #计算包含obj损失  即本来有,预测有  和  本来有,预测无
        coo_response_mask = torch.cuda.ByteTensor(box_target.size())
        coo_response_mask.zero_()
        coo_not_response_mask = torch.cuda.ByteTensor(box_target.size())
        coo_not_response_mask.zero_()
        # 选择最好的IOU
        for i in range(0,box_target.size()[0],2):
            box1 = box_pred[i:i+2]
            box1_xyxy = Variable(torch.FloatTensor(box1.size()))
            box1_xyxy[:,:2] = box1[:,:2] -0.5*box1[:,2:4]
            box1_xyxy[:,2:4] = box1[:,:2] +0.5*box1[:,2:4]
            box2 = box_target[i].view(-1,5)
            box2_xyxy = Variable(torch.FloatTensor(box2.size()))
            box2_xyxy[:,:2] = box2[:,:2] -0.5*box2[:,2:4]
            box2_xyxy[:,2:4] = box2[:,:2] +0.5*box2[:,2:4]
            iou = self.compute_iou(box1_xyxy[:,:4],box2_xyxy[:,:4]) #[2,1]
            max_iou,max_index = iou.max(0)
            max_index = max_index.data.cuda()
            coo_response_mask[i+max_index]=1
            coo_not_response_mask[i+1-max_index]=1
        # 1.response loss响应损失,即本来有,预测有   有相应 坐标预测的loss  (x,y,w开方,h开方)参考论文loss公式
        # box_pred [144,5]   coo_response_mask[144,5]   box_pred_response:[72,5]
        # 选择IOU最好的box来进行调整  负责检测出某物体
        box_pred_response = box_pred[coo_response_mask].view(-1,5)
        box_target_response = box_target[coo_response_mask].view(-1,5)
        # box_pred_response:[72,5]     计算预测 有物体的概率误差,返回一个数
        contain_loss = F.mse_loss(box_pred_response[:,4],box_target_response[:,4],size_average=False)
        # 计算(x,y,w开方,h开方)参考论文loss公式
        loc_loss = F.mse_loss(box_pred_response[:,:2],box_target_response[:,:2],size_average=False) + F.mse_loss(torch.sqrt(box_pred_response[:,2:4]),torch.sqrt(box_target_response[:,2:4]),size_average=False)

        # 2.not response loss 未响应损失,即本来有,预测无   未响应
        # box_pred_not_response = box_pred[coo_not_response_mask].view(-1,5)
        # box_target_not_response = box_target[coo_not_response_mask].view(-1,5)
        # box_target_not_response[:,4]= 0
        # box_pred_response:[72,5]
        # 计算c  有物体的概率的loss
        not_contain_loss = F.mse_loss(box_pred_response[:,4],box_target_response[:,4],size_average=False)
        # 3.class loss  计算传入的真实有物体的网格  分类的类别损失 
        class_loss = F.mse_loss(class_pred,class_target,size_average=False)
        # 除以N  即平均一张图的总损失
        return (self.l_coord*loc_loss + contain_loss + not_contain_loss + self.l_noobj*nooobj_loss + class_loss)/N

一文看懂YOLO v1:https://blog.csdn.net/litt1e/article/details/88814417
一文看懂YOLO v2:https://blog.csdn.net/litt1e/article/details/88852745
一文看懂YOLO v3:https://blog.csdn.net/litt1e/article/details/88907542

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