centroid = centroid.squeeze(0)中squeeze（）函数作用

squeeze()函数的作用是从张量中去除维度为1的维度。具体而言，squeeze(dim)函数会删除张量中维度为1的维度，如果指定了dim参数，则只删除指定的维度为1的维度，否则删除所有维度为1的维度。在给定的示例中，squeeze(0)的作用是将张量中第0维的维度为1的维度去除，返回一个降维后的张量。

相关问题

matlab centroid函数

Matlab centroid函数是一个用于计算图像或物体的中心点的函数。它可以计算图像的质心（重心）或物体的重心。质心是指一个物体的所有质点的平均位置，而重心则是指一个物体的质量中心。Centroid函数可以计算二维图像或三维图像的重心或质心。

语法：

1. 二维图像的质心

centroid = regionprops(BW,'Centroid');

其中，BW为二值图像，Centroid为一个二元素向量，表示图像的质心。

2. 二维图像的重心

centroid = regionprops(BW,'WeightedCentroid');

其中，BW为二值图像，WeightedCentroid为一个二元素向量，表示图像的重心。

3. 三维图像的质心

centroid = regionprops3(BW,'Centroid');

其中，BW为三维二值图像，Centroid为一个三元素向量，表示图像的质心。

4. 三维图像的重心

centroid = regionprops3(BW,'WeightedCentroid');

其中，BW为三维二值图像，WeightedCentroid为一个三元素向量，表示图像的重心。

示例：

以下是一个二维图像的质心计算示例：

I = imread('coins.png');
BW = imbinarize(I);
centroid = regionprops(BW,'Centroid');
imshow(BW)
hold on
plot(centroid.Centroid(1), centroid.Centroid(2), 'r*')
hold off

以下是一个三维图像的重心计算示例：

load mri
D = squeeze(D);
BW = imbinarize(D);
centroid = regionprops3(BW,'WeightedCentroid');
xslice = [50, 120]; yslice = 70; zslice = [20, 40];
slice(double(D),xslice,yslice,zslice)
hold on
plot3(centroid.WeightedCentroid(1),centroid.WeightedCentroid(2),centroid.WeightedCentroid(3),'r*')
hold off

以上示例代码可以在Matlab命令窗口中直接运行。

重识别损失函数

### Person Re-Identification Loss Functions

In person re-identification (ReID), several loss functions are commonly used to train models effectively. These losses aim at improving feature representation learning so that the same identities have similar features while different ones remain distinct.

#### Triplet Loss
Triplet loss is widely adopted in ReID tasks due to its effectiveness in pulling together embeddings from images of the same identity and pushing apart those from different identities[^1]. The triplet consists of an anchor image, a positive match with the same ID as the anchor, and a negative sample which has a different ID. Mathematically, this can be expressed as:

\[ L_{\text{triplet}}(a,p,n) = \max(d(a,p)-d(a,n)+m, 0) \]

where \( d(x,y) \) represents distance between two embedding vectors, typically Euclidean or cosine distance; \( m \) denotes margin parameter ensuring separation gap.

import torch.nn.functional as F

def triplet_loss(anchor, positive, negative, margin=1.0):
    pos_dist = F.pairwise_distance(anchor, positive)
    neg_dist = F.pairwise_distance(anchor, negative)
    
    return F.relu(pos_dist - neg_dist + margin).mean()

#### Cross Entropy Loss Combined With Label Smoothing
Cross entropy combined with label smoothing regularizes softmax-based classifiers during training by reducing overfitting on training data points. This approach encourages more generalized decision boundaries among classes.

The formula for cross entropy loss incorporating label smoothing looks like below:

\[ L_{CE}(y,\hat y)=-(1-\alpha)y\log(\hat y+\epsilon)-\frac{\alpha}{K}\sum_k^{K}{}\log(\hat y_k+\epsilon)\]

Here, \( K \) stands for number of categories, \( \alpha \) controls strength of regularization term, and small constant \( \epsilon \) prevents numerical instability when computing logarithms.

import torch
from torch import nn

class SmoothedCELoss(nn.Module):
    def __init__(self, alpha=0.1, epsilon=1e-8):
        super().__init__()
        self.alpha = alpha
        self.epsilon = epsilon
    
    def forward(self, logits, targets):
        n_classes = logits.size(-1)
        
        one_hot_targets = F.one_hot(targets, num_classes=n_classes).float()
        smoothed_labels = (1-self.alpha)*one_hot_targets+self.alpha/n_classes
        
        log_probs = F.log_softmax(logits, dim=-1)
        ce_loss = -(smoothed_labels * log_probs).sum(dim=-1)

        return ce_loss.mean()

#### Center Loss
Center loss minimizes intra-class variance through penalizing distances between each instance's deep feature vector and corresponding class center point. By doing so, instances belonging to the same category cluster tightly around their respective centers leading to better discrimination power against other clusters.

Mathematical expression for center loss reads thusly:

\[ L_c=\frac{1}{2N}\sum_i^N||f(x_i)-c_{y_i}||_2^2 \]

Where \( f(x_i) \) signifies outputted feature map after passing input \( x_i \); \( c_j \) symbolizes learned centroid associated with j-th class.

import numpy as np

def update_centers(features, labels, old_centers, lr=0.5):
    new_centers = []
    counts = []

    unique_ids = list(set(labels))
    for id in unique_ids:
        mask = (labels == id)
        count = sum(mask)
        feat_sum = features[mask].sum(axis=0)
        updated_center = ((count*old_centers[id])+(lr*feat_sum))/(count+lr)
        new_centers.append(updated_center)
        counts.append(count+len(old_centers[id]))

    return np.array(new_centers), counts


def compute_center_loss(features, centers, labels):
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    features = features.to(device)
    centers = centers.to(device)
    labels = labels.to(device)

    batch_size = features.size()[0]
    distmat = torch.pow(features, 2).sum(dim=1, keepdim=True).expand(batch_size, -1)\
             +torch.pow(centers, 2).sum(dim=1, keepdim=True).expand(-1,batch_size).t()

    distmat.addmm_(features, centers.t(), beta=1, alpha=-2)

    indices = torch.arange(batch_size).long().to(device)
    loss = distmat.gather(1, labels.unsqueeze(1)).squeeze(1)

    return loss.sum()/batch_size