facenet train

[chaohuazhu]

facenet use triplet-loss to train net,minimizes the distance between anchor and a positive,maximizes the distance between anchor and a negative of a different identity. while the network is training,the embedding of anchor and positive,negative sample is change too,there is two solutions to generate triplets,the first is generate triplets offline every n steps,using the most recent network checkpoint and computing the argmin and argmax on a subset of the data,second is generate triplet online,this can be done bye seletcting the hard positive/negative exemplars from within a mini-batch.facenet choice second.every minibatch,according to the current embedding,generate triplets,calculate triplet-loss,update embedding,

1. minibatch begin,facenet select a group from data set.

   从数据集中进行抽样图片，参数为训练数据集，每一个batch抽样多少人，每个人抽样多少张

   def sample_people(dataset, people_per_batch, images_per_person):

   总共应该抽样多少张

   nrof_images = people_per_batch * images_per_person

   数据集中一共有多少人的图像

   nrof_classes = len(dataset)

   每个人的索引

   class_indices = np.arange(nrof_classes)

   随机打乱一下

   np.random.shuffle(class_indices) i = 0

   保存抽样出来的图像的路径

   image_paths = []

   抽样的样本是属于哪一个人的，作为label

   num_per_class = [] sampled_class_indices = []

   Sample images from these classes until we have enough

   不断抽样直到达到指定数量

   while len(image_paths)<nrof_images:

   从第i个人开始抽样

   class_index = class_indices[i]
   #第i个人有多少张图片
   nrof_images_in_class = len(dataset[class_index])
   #这些图片的索引
   image_indices = np.arange(nrof_images_in_class)
   np.random.shuffle(image_indices)
   #从第i个人中抽样的图片数量
   nrof_images_from_class = min(nrof_images_in_class, images_per_person, nrof_images-len(image_paths))
   idx = image_indices[0:nrof_images_from_class]
   #抽样出来的人的路径
   image_paths_for_class = [dataset[class_index].image_paths[j] for j in idx]
   #图片的label
   sampled_class_indices += [class_index]*nrof_images_from_class
   image_paths += image_paths_for_class
   #第i个人抽样了多少张
   num_per_class.append(nrof_images_from_class)
   i+=1

   return image_paths, num_per_class

2. calculate embedding, save to emb_array,according to the array,calculate triplet

   多少人，alpha参数

   def select_triplets(embeddings, nrof_images_per_class, image_paths, people_per_batch, alpha): """ Select the triplets for training """ trip_idx = 0

   某个人的图片的embedding在emb_arr中的开始的索引

   emb_start_idx = 0 num_trips = 0 triplets = []

   VGG Face: Choosing good triplets is crucial and should strike a balance between

   selecting informative (i.e. challenging) examples and swamping training with examples that

   are too hard. This is achieve by extending each pair (a, p) to a triplet (a, p, n) by sampling

   the image n at random, but only between the ones that violate the triplet loss margin. The

   latter is a form of hard-negative mining, but it is not as aggressive (and much cheaper) than

   choosing the maximally violating example, as often done in structured output learning.

   遍历每一个人

   for i in xrange(people_per_batch):

   这个人有多少张图片

   nrof_images = int(nrof_images_per_class[i])
   #遍历第i个人的所有图片
   for j in xrange(1,nrof_images):
       #第j张图的embedding在emb_arr 中的位置
       a_idx = emb_start_idx + j - 1
       #第j张图跟其他所有图片的欧氏距离
       neg_dists_sqr = np.sum(np.square(embeddings[a_idx] - embeddings), 1)
       #遍历每一对可能的(anchor,postive)图片，记为(a,p)吧
       for pair in xrange(j, nrof_images): # For every possible positive pair.
           #第p张图片在emb_arr中的位置
           p_idx = emb_start_idx + pair
           #(a,p)之前的欧式距离
           pos_dist_sqr = np.sum(np.square(embeddings[a_idx]-embeddings[p_idx]))
           #同一个人的图片不作为negative，所以将距离设为无穷大
           neg_dists_sqr[emb_start_idx:emb_start_idx+nrof_images] = np.NaN
           #all_neg = np.where(np.logical_and(neg_dists_sqr-pos_dist_sqr<alpha, pos_dist_sqr<neg_dists_sqr))[0]  # FaceNet selection
           #其他人的图片中有哪些图片与a之间的距离-p与a之间的距离小于alpha的
           all_neg = np.where(neg_dists_sqr-pos_dist_sqr<alpha)[0] # VGG Face selecction
           #所有可能的negative
           nrof_random_negs = all_neg.shape[0]
           #如果有满足条件的negative
           if nrof_random_negs>0:
               #从中随机选取一个作为n
               rnd_idx = np.random.randint(nrof_random_negs)
               n_idx = all_neg[rnd_idx]
               # 选到(a,p,n)作为三元组
               triplets.append((image_paths[a_idx], image_paths[p_idx], image_paths[n_idx]))
               #print('Triplet %d: (%d, %d, %d), pos_dist=%2.6f, neg_dist=%2.6f (%d, %d, %d, %d, %d)' % 
               #    (trip_idx, a_idx, p_idx, n_idx, pos_dist_sqr, neg_dists_sqr[n_idx], nrof_random_negs, rnd_idx, i, j, emb_start_idx))
               trip_idx += 1
           num_trips += 1
   emb_start_idx += nrof_images

   np.random.shuffle(triplets) return triplets, num_trips, len(triplets)

3. calculate triplet loss,update network,update embedding...

[chaohuazhu]

another thing is triplet loss need less GPU memory. when use softmax loss ,8,000,000 people need 32M，if the upper middle layer 1024