解释代码def dataIterator(feature_file,label_file,dictionary,batch_size,batch_Imagesize,maxlen,maxImagesize): fp=open(feature_file,'rb') features=pkl.load(fp) fp.close() fp2=open(label_file,'r') labels=fp2.readlines() fp2.close() targets={} # map word to int with dictionary for l in labels: tmp=l.strip().split() uid=tmp[0] w_list=[] for w in tmp[1:]: #if dictionary.has_key(w): if w in dictionary.keys(): w_list.append(dictionary[w]) else: print ('a word not in the dictionary !! sentence ',uid,'word ', w) sys.exit() targets[uid]=w_list imageSize={} for uid,fea in features.items(): imageSize[uid]=fea.shape[1]*fea.shape[2] imageSize= sorted(imageSize.items(), key=lambda d:d[1]) # sorted by sentence length, return a list with each triple element feature_batch=[] label_batch=[] feature_total=[] label_total=[] uidList=[] batch_image_size=0 biggest_image_size=0 i=0 for uid,size in imageSize: if size>biggest_image_size: biggest_image_size=size fea=features[uid] # cv2.namedWindow(uid, 0) # cv2.imshow(uid, fea) # cv2.waitKey(0) lab=targets[uid] batch_image_size=biggest_image_size*(i+1) if len(lab)>maxlen: print ('sentence', uid, 'length bigger than', maxlen, 'ignore') elif size>maxImagesize: print ('image', uid, 'size bigger than', maxImagesize, 'ignore') else: uidList.append(uid) if batch_image_size>batch_Imagesize or i==batch_size: # a batch is full feature_total.append(feature_batch) label_total.append(label_batch) i=0 biggest_image_size=size feature_batch=[] label_batch=[] feature_batch.append(fea) label_batch.append(lab) batch_image_size=biggest_image_size*(i+1) i+=1 else: feature_batch.append(fea) label_batch.append(lab) i+=1 # last batch feature_total.append(feature_batch) label_total.append(label_batch) print ('total ',len(feature_total), 'batch data loaded') return list(zip(feature_total,label_total)),uidList

这段代码定义了一个名为dataIterator的函数，它有七个参数：feature_file、label_file、dictionary、batch_size、batch_Imagesize、maxlen和maxImagesize。

函数的作用是迭代读取特征文件和标签文件中的数据，并将它们转换成模型可以处理的格式。其中，特征文件是一个pickle文件，需要使用pkl.load()函数来读取；标签文件是一个文本文件，需要使用readlines()函数来读取。

函数的返回值是一个字典targets，其中包含了每个批次的特征和标签数据。每个批次的大小由batch_size参数指定，每个图像的大小由batch_Imagesize参数指定。同时，为了避免数据过长或过大，函数还对每个样本的长度和图像大小进行了限制，分别由maxlen和maxImagesize参数指定。