从零开始AlignedReID_03

loss模块实现

前言

前面两篇博客我们已经把数据的导入以及网络的主体实现了，那么这篇博客主要是为了实现网络的损失。
还是上面这张图，我们可以看到，网络主要使用了两个损失函数，即softmax loss以及triplet loss。那么这篇博客主要包括以下几个部分：

• softmax cross entropy loss
• local distance
• Triplet loss
• deepsupervision

1.global feature的softmax loss

这里的softmax loss其实是指对global feature使用分类问题的方式进行计算，首先使用softmax计算概率，然后根据概率使用交叉熵损失计算相似度。
在utils包下新建losses.py用来存放要用到的损失函数，代码如下：

class CrossEntropyLoss(nn.Module):
    """Cross entropy loss.

    """
    def __init__(self, use_gpu=True):
        super(CrossEntropyLoss, self).__init__()
        self.use_gpu = use_gpu
        self.crossentropy_loss = nn.CrossEntropyLoss()

    def forward(self, inputs, targets):
        """
        Args:
            inputs: prediction matrix (before softmax) with shape (batch_size, num_classes)
            targets: ground truth labels with shape (num_classes)
        """
        if self.use_gpu: targets = targets.cuda()
        loss = self.crossentropy_loss(inputs, targets)
        return loss
# 防止过拟合
class CrossEntropyLabelSmooth(nn.Module):
    """Cross entropy loss with label smoothing regularizer.

    Reference:
    Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
    Equation: y = (1 - epsilon) * y + epsilon / K.

    Args:
        num_classes (int): number of classes.
        epsilon (float): weight.
    """
    def __init__(self, num_classes, epsilon=0.1, use_gpu=True):
        super(CrossEntropyLabelSmooth, self).__init__()
        self.num_classes = num_classes
        self.epsilon = epsilon
        self.use_gpu = use_gpu
        self.logsoftmax = nn.LogSoftmax(dim=1)

    def forward(self, inputs, targets):
        """
        Args:
            inputs: prediction matrix (before softmax) with shape (batch_size, num_classes)
            targets: ground truth labels with shape (num_classes)
        """
        log_probs = self.logsoftmax(inputs)
        targets = torch.zeros(log_probs.size()).scatter_(1, targets.unsqueeze(1).data.cpu(), 1)
        if self.use_gpu: targets = targets.cuda()
        targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
        loss = (- targets * log_probs).mean(0).sum()
        return loss

2.local distance

在models.py下新建local_dist.py,主要包含以下几个函数：

• enclidean_dist （计算欧式距离）
• batch_enclidean_dist（适用于多通道图片）
• shortest_dist（自定义的计算距离矩阵距离的函数）
• hard_example_mining（求最难样本对 triplet loss用）
• batch_local_dist（计算local distance）
  代码如下：

#-*-coding:utf-8-*-
# 实现最短路径的计算及选取最难样本对

import torch

# 计算欧式距离

def enclidean_dist(x,y):
    """
    Args:
      x: pytorch Variable, with shape [m, d]
      y: pytorch Variable, with shape [n, d]
    Returns:
      dist: pytorch Variable, with shape [m, n]
    """
    m,n = x.size(0),y.size(0)
    xx = torch.pow(x,2).sum(1,keepdim=True).expand(m,n)
    yy = torch.pow(y,2).sum(1,keepdim=True).expand(m,n)
    dist = xx + yy
    dist.addmm_(1,-2,x,y.t())
    dist = dist.clamp(min=1e-12).sqrt()
    return dist

def batch_enclidean_dist(x,y):
    """
    Args:
      x: pytorch Variable, with shape [Batch size, Local part, Feature channel]
      y: pytorch Variable, with shape [Batch size, Local part, Feature channel]
    Returns:
      dist: pytorch Variable, with shape [Batch size, Local part, Local part]
    """
    # 断言 在不满足条件时直接抛出异常
    assert len(x.size()) == 3
    assert len(y.size()) == 3
    assert x.size(0) == y.size(0)
    assert x.size(-1) == y.size(-1)

    N,m,d = x.size()
    N,n,d = y.size()

    # shape[N,m,n]
    xx = torch.pow(x,2).sum(-1,keepdim=True).expand(N,m,n)
    yy = torch.pow(y,2).sum(-1,keepdim=True).expand(N,n,m).permute(0,2,1)
    dist = xx + yy
    dist.baddbmm_(1,-2,x,y.permute(0,2,1))
    dist = dist.clamp(min=1e-12).sqrt()
    return dist

# 计算最短路径
def shortest_dist(dist_mat):
    """Parallel version.
    Args:
      dist_mat: pytorch Variable, available shape:
        1) [m, n]
        2) [m, n, N], N is batch size
        3) [m, n, *], *
         can be arbitrary additional dimensions
    Returns:
      dist: three cases corresponding to `dist_mat`:
        1) scalar
        2) pytorch Variable, with shape [N]
        3) pytorch Variable, with shape [*]
    """
    m,n = dist_mat.size()[:2]
    # Just offering some reference for accessing intermediate distance.
    # m×n次循环 创建数组
    dist = [[0 for _ in range(n)] for _ in range(m)]
    for i in range(m):
        for j in range(n):
            if(i==0) and (j==0):
                dist[i][j] = dist_mat[i,j]
            elif(i == 0) and (j > 0):
                dist[i][j] = dist[i][j-1] + dist_mat[i,j]
            elif(i>0) and (j == 0):
                dist[i][j] = dist[i-1][j] + dist_mat[i,j]
            else:
                dist[i][j] = torch.min(dist[i-1][j],dist[i][j-1]) + dist_mat[i,j]
    dist = dist[-1][-1]

    return dist

def batch_local_dist(x,y):
    """
    Args:
      x: pytorch Variable, with shape [N, m, d]
      y: pytorch Variable, with shape [N, n, d]
    Returns:
      dist: pytorch Variable, with shape [N]
    """
    assert len(x.size()) == 3
    assert len(y.size()) == 3
    assert x.size(0) == y.size(0)
    assert x.size(-1) == y.size(-1)

    # shape[N,m,n]
    dist_mat = batch_enclidean_dist(x,y)
    dist_mat = (torch.exp(dist_mat) - 1.)/(torch.exp(dist_mat) + 1.)

    # shape[N]
    dist = shortest_dist(dist_mat.permute(1,2,0))

    return dist


# 寻找最难样本对 dist_mat距离矩阵 labels为标签
# return_inds为是否返回正负样本对id 默认为False
# 但是AlignReID两分支需要使用同一对正负样本对 所以为True
def hard_example_mining(dist_mat,labels,return_inds=False):
    """For each anchor, find the hardest positive and negative sample.
    Args:
      dist_mat: pytorch Variable, pair wise distance between samples, shape [N, N]
      labels: pytorch LongTensor, with shape [N]
      return_inds: whether to return the indices. Save time if `False`(?)
    Returns:
      dist_ap: pytorch Variable, distance(anchor, positive); shape [N]
      dist_an: pytorch Variable, distance(anchor, negative); shape [N]
      p_inds: pytorch LongTensor, with shape [N];
        indices of selected hard positive samples; 0 <= p_inds[i] <= N - 1
      n_inds: pytorch LongTensor, with shape [N];
        indices of selected hard negative samples; 0 <= n_inds[i] <= N - 1
    NOTE: Only consider the case in which all labels have same num of samples,
      thus we can cope with all anchors in parallel.
    """
    # 断言
    assert len(dist_mat.size()) == 2
    assert dist_mat.size(0) == dist_mat.size(1)

    # 距离矩阵的尺寸是 (batch_size, batch_size)  [N,N]
    N = dist_mat.size(0)

    # shape[N,N] 选出所有正负样本对
    is_pos = labels.expand(N,N).eq(labels.expand(N,N).t()) # 两两组合， 取label相同的a-p
    is_neg = labels.expand(N,N).ne(labels.expand(N,N).t()) # 两两组合， 取label不同的a-n

    # `dist_ap` means distance(anchor, positive)
    # both `dist_ap` and `relative_p_inds` with shape [N, 1]
    dist_ap,relative_p_inds = torch.max(dist_mat[is_pos].contiguous().view(N,-1),1,keepdim=True)
    # `dist_an` means distance(anchor, negative)
    # both `dist_an` and `relative_n_inds` with shape [N, 1]
    dist_an, relative_n_inds = torch.min(dist_mat[is_neg].contiguous().view(N, -1), 1, keepdim=True)

    # shape [N]
    # 去掉维数为1的的维度，squeeze(a)将a中所有为1的维度删掉，不为1的维度没有影响。a.squeeze(N) 就是去掉a中指定的维数为一的维度。
    dist_ap = dist_ap.squeeze(1)
    dist_an = dist_an.squeeze(1)

    if return_inds:
        # shape [N, N]
        ind = (labels.new().resize_as_(labels)
               .copy_(torch.arange(0, N).long())
               .unsqueeze(0).expand(N, N))
        # shape [N, 1]
        # 收集输入的特定维度指定位置的数值(input,dim,index)
        p_inds = torch.gather(
            ind[is_pos].contiguous().view(N, -1), 1, relative_p_inds.data)
        n_inds = torch.gather(
            ind[is_neg].contiguous().view(N, -1), 1, relative_n_inds.data)
        # shape [N]
        p_inds = p_inds.squeeze(1)
        n_inds = n_inds.squeeze(1)
        return dist_ap, dist_an, p_inds, n_inds

    return dist_ap, dist_an



if __name__ == "__main__":
    x = torch.randn(32,2048)
    y = torch.randn(32,2048)
    dist_mat = enclidean_dist(x,y)

3.triplet loss

依然存放到utils的losses.py文件中：

class TripletLoss(nn.Module):
    """Triplet loss with hard positive/negative mining.

    Reference:
    Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.

    Code imported from https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py.

    Args:
        margin (float): margin for triplet.
    """
    def __init__(self, margin=0.3, mutual_flag = False):
        super(TripletLoss, self).__init__()
        self.margin = margin
        self.ranking_loss = nn.MarginRankingLoss(margin=margin)
        self.mutual = mutual_flag

    def forward(self, inputs, targets):
        """
        Args:
            inputs: feature matrix with shape (batch_size, feat_dim)
            targets: ground truth labels with shape (num_classes)
        """
        n = inputs.size(0)
        # inputs = 1. * inputs / (torch.norm(inputs, 2, dim=-1, keepdim=True).expand_as(inputs) + 1e-12)
        # Compute pairwise distance, replace by the official when merged
        dist = torch.pow(inputs, 2).sum(dim=1, keepdim=True).expand(n, n)
        dist = dist + dist.t()
        dist.addmm_(1, -2, inputs, inputs.t())
        dist = dist.clamp(min=1e-12).sqrt()  # for numerical stability
        # For each anchor, find the hardest positive and negative
        mask = targets.expand(n, n).eq(targets.expand(n, n).t())
        dist_ap, dist_an = [], []
        for i in range(n):
            dist_ap.append(dist[i][mask[i]].max().unsqueeze(0))
            dist_an.append(dist[i][mask[i] == 0].min().unsqueeze(0))
        dist_ap = torch.cat(dist_ap)
        dist_an = torch.cat(dist_an)
        # Compute ranking hinge loss
        y = torch.ones_like(dist_an)
        loss = self.ranking_loss(dist_an, dist_ap, y)
        if self.mutual:
            return loss, dist
        return loss

class TripletAlignedReIDloss(nn.Module):
    """Triplet loss with hard positive/negative mining.

    Reference:
    Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.

    Code imported from https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py.

    Args:
        margin (float): margin for triplet.
    """
    def __init__(self,margin=0.3,mutual_flag = False):
        super(TripletAlignedReIDloss,self).__init__()
        self.margin = margin
        self.ranking_loss = nn.MarginRankingLoss(margin=margin)
        self.ranking_loss_local = nn.MarginRankingLoss(margin=margin)
        self.mutual = mutual_flag

    # input[N,()]
    def forward(self,inputs,targets,local_features):
        """
        Args:
            inputs: feature matrix with shape (batch_size, feat_dim)
            targets: ground truth labels with shape (num_classes)
        """
        n = inputs.size(0)
        # 1.
        # power是逐元素点乘，sum起来就是取模的平方, keepdim保持维度不变，不要求和成一个数
        dist = torch.pow(inputs, 2).sum(dim=1, keepdim=True).expand(n, n)
        dist = dist + dist.t()  # a^2 + b^2
        # a^2 + b^2 -2*ab
        dist.addmm_(1,-2,inputs,inputs.t()) # 做的是如(a1, a2, a, b) -> a1*dist + a2*a*b
        # for numerical stability
        # 将输入input张量每个元素的夹紧到区间 [min,max][min,max]，并返回结果到一个新张量，防止他等于0，再做一个开方
        dist = dist.clamp(min=1e-12).sqrt()
        dist_ap,dist_an,p_inds,n_inds = hard_example_mining(dist,targets,return_inds=True)
        p_inds,n_inds = p_inds.long(),n_inds.long()
        local_features = local_features.permute(0,2,1)
        p_local_feature = local_features[p_inds]
        n_local_feature = local_features[n_inds]
        local_dist_ap = batch_local_dist(local_features,p_local_feature)
        local_dist_an = batch_local_dist(local_features,n_local_feature)

        y = torch.ones_like(dist_an)
        global_loss = self.ranking_loss(dist_an,dist_ap,y)
        local_loss = self.ranking_loss_local(local_dist_an,local_dist_ap,y)
        if self.mutual:
            return global_loss+local_loss,dist
        return global_loss,local_loss

if __name__ == "__main__":
    # 32
    target = [1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,6,6,6,6,7,7,7,7,8,8,8,8]
    target = torch.Tensor(target)
    features =torch.rand(32,2048)
    local_features = torch.randn(32,128,8)
    triloss = TripletAlignedReIDloss()
    global_loss,local_loss = triloss(features,target,local_features)
    embed()

4.loss求和函数

def DeepSupervision(criterion, xs, y):
    """
    Args:
        criterion: loss function
        xs: tuple of inputs
        y: ground truth
    """
    loss = 0.
    for x in xs:
        loss += criterion(x, y)
    return loss