一、模型框架图

1、框架解读

第一列是输入，一部分是文本（需核查文本、文本证据材料），一部分是图片（需核查图像、图像证据材料）。

第二列是pre-trained模型，用于特征提取。文本部分采用Ernie-m模型提取特征，图像部分采用Resnet模型提取特征。

第三列是多头自注意力机制，可得到相关的文本证据特征、相关的图像证据特征。

最后，使用全连接层将标题特征、图像特征、相关的文本证据特征、相关的图像证据特征四块特征拼接，输入到分类器得到最终预测结果
在这里插入图片描述

2、评价指标解读

采用在三个不同类别上的macro F1的高低进行评分，兼顾了准确率与召回率，是谣言检测领域主流的自动评价指标。

Macro-F1在sklearn里的计算方法就是计算每个类的F1-score的算数平均值，符合赛题定义。

本赛题共有三类，包含文娱、经济、健康。先分别计算每个类别的F1，再求平均值。

F1的计算，首先要了解混淆矩阵：
在这里插入图片描述
TPi 是指第 i 类的 True Positive 正类判定为正类;
FPi 是指第 i 类的 False Positive 负类判定为正类;
FNi 是指第 i 类的 FalseNegative 正类判定为负类;
TNi 是指第 i 类的 True Negative 负类判定为负类。

对第1类：TP1=a；FP1=d+g；FN1=b+c；TN1=e+f+h+i；
对第2类：TP2=e；FP2=b+h；FN2=d+f； TN2=a+c+g+i；
对第3类：TP3=i； FP3=c+f； FN3=g+h；TN3=a+b+d+e；

在这里插入图片描述
最后计算公式如下：
$macro-F1= （F1−score_1+F1−score_2+ F1−score_3)/3$
拿文娱举例，召回率：预测正确文娱的数占真实文娱数的比值；准确率：预测正确文娱的数占预测为文娱数的比值

二、代码功能

1、数据集加载

#### load Datasets ####
train_dataset = NewsContextDatasetEmbs(data_items_train, 'queries_dataset_merge','train')
val_dataset = NewsContextDatasetEmbs(data_items_val,'queries_dataset_merge','val')
test_dataset = NewsContextDatasetEmbs(data_items_test,'queries_dataset_merge','test')

训练集、测试集、验证集都是通过NewsContextDatasetEmbs这个类函数来创建的。

传入的三个参数分别为 json文件数据、指定数据集的根目录、指定数据集类别

1）NewsContextDatasetEmbs

class NewsContextDatasetEmbs(Dataset):
    def __init__(self, context_data_items_dict, queries_root_dir, split):
        self.context_data_items_dict = context_data_items_dict
        self.queries_root_dir = queries_root_dir
        self.idx_to_keys = list(context_data_items_dict.keys())
        # 使用Imagenet的均值和标准差归一化
        # 将图像大小调整为（256×256）
        # 将其转换为（224×224）
        # 将其转换为张量 - 图像中的所有元素值都将被缩放，以便在[0，1]之间而不是原来的[0，255]范围内
        # 将其正则化，使用Imagenet数据
        # 均值 = [0.485，0.456，0.406]，标准差 = [0.229，0.224，0.225]
        self.transform = T.Compose([
            T.Resize(256),
            T.CenterCrop(224),
            T.ToTensor(),
            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  
        ])
        self.split = split

    # 计算字典的长度
    def __len__(self):
        return len(self.context_data_items_dict)

    #通过pil读取img图像，深度学习模型一般只支持三通道，（其他通道可能是透明度）
    def load_img_pil(self, image_path):
        # imghdr用于探测图片的格式，实际就是图片格式遍历匹配
        if imghdr.what(image_path) == 'gif':
            try:
                with open(image_path, 'rb') as f:
                    img = Image.open(f)
                    return img.convert('RGB')
            except:
                return None
        with open(image_path, 'rb') as f:
            img = Image.open(f)
            return img.convert('RGB')

   #加载图片直接返回图片的tensor
    def load_imgs_direct_search(self, item_folder_path, direct_dict):
        list_imgs_tensors = []
        count = 0
        keys_to_check = ['images_with_captions', 'images_with_no_captions', 'images_with_caption_matched_tags']
        for key1 in keys_to_check:
            if key1 in direct_dict.keys():
                for page in direct_dict[key1]:
                    image_path = os.path.join(item_folder_path, page['image_path'].split('/')[-1])
                    try:
                        pil_img = self.load_img_pil(image_path)   #调用load_img_pil函数读入只含三通道的图片
                    except Exception as e:
                        print(e)
                        print(image_path)
                    if pil_img == None: continue
                    transform_img = self.transform(pil_img)   # 将读入的图片处理成统一大小
                    count = count + 1
                    list_imgs_tensors.append(transform_img)
        stacked_tensors = paddle.stack(list_imgs_tensors, axis=0)
        return stacked_tensors

    #加载inverse_search文件夹下的说明文字，返回说明文字，通过图匹配到的文字
    def load_captions(self, inv_dict):
        captions = ['']

        #不同的方式处理方式不一样
        pages_with_captions_keys = ['all_fully_matched_captions', 'all_partially_matched_captions']
        for key1 in pages_with_captions_keys:
            if key1 in inv_dict.keys():
                for page in inv_dict[key1]:
                    #有title的dict
                    if 'title' in page.keys():
                        item = page['title']
                        item = process_string(item)
                        captions.append(item)
                    #有caption的dict
                    if 'caption' in page.keys():
                        sub_captions_list = []
                        unfiltered_captions = []
                        for key2 in page['caption']:
                            sub_caption = page['caption'][key2]
                            sub_caption_filter = process_string(sub_caption)      
                            # 将文字中的单引号、字体加粗的网页标签过滤掉，为啥需要替换，会有信息损失吗
                            # 是否可以替换更多，或者有其他方式解决
                            if sub_caption in unfiltered_captions: continue  # 如果已经加过的caption数据就不再加了
                            sub_captions_list.append(sub_caption_filter)
                            unfiltered_captions.append(sub_caption)
                        captions = captions + sub_captions_list
        #不同的方式处理不一样
        pages_with_title_only_keys = ['partially_matched_no_text', 'fully_matched_no_text']
        for key1 in pages_with_title_only_keys:
            if key1 in inv_dict.keys():
                for page in inv_dict[key1]:
                    if 'title' in page.keys():
                        title = process_string(page['title'])
                        captions.append(title)
        return captions

    # 加载img_html_news文件夹下的说明文字，返回说明文字，通过文字匹配到的图
    def load_captions_weibo(self, direct_dict):
        captions = ['']
        keys = ['images_with_captions', 'images_with_no_captions', 'images_with_caption_matched_tags']
        for key1 in keys:
            if key1 in direct_dict.keys():
                for page in direct_dict[key1]:
                    if 'page_title' in page.keys():
                        item = page['page_title']
                        item = process_string(item)
                        captions.append(item)
                    if 'caption' in page.keys():
                        sub_captions_list = []
                        unfiltered_captions = []
                        for key2 in page['caption']:
                            sub_caption = page['caption'][key2]
                            sub_caption_filter = process_string(sub_caption)
                            if sub_caption in unfiltered_captions: continue
                            sub_captions_list.append(sub_caption_filter)
                            unfiltered_captions.append(sub_caption)
                        captions = captions + sub_captions_list
                        # print(captions)
        return captions

    # 加载 dataset_items_train.json ，img文件夹的图片，返回 transform的图片img, 和文字caption
    def load_queries(self, key):
        caption = self.context_data_items_dict[key]['caption']
        image_path = os.path.join(self.queries_root_dir, self.context_data_items_dict[key]['image_path'])
        pil_img = self.load_img_pil(image_path)
        transform_img = self.transform(pil_img)
        return transform_img, caption

    def __getitem__(self, idx):
        key = self.idx_to_keys[idx]   #对应id的key值查询
        item = self.context_data_items_dict.get(str(key))
        # 如果为test没有label属性，所以train和val一起处理，else为test处理部分
        if self.split == 'train' or self.split == 'val':
            label = paddle.to_tensor(int(item['label']))
            direct_path_item = os.path.join(self.queries_root_dir, item['direct_path'])
            inverse_path_item = os.path.join(self.queries_root_dir, item['inv_path'])
            inv_ann_dict = json.load(open(os.path.join(inverse_path_item, 'inverse_annotation.json'),'r',encoding='UTF8'))
            direct_dict = json.load(open(os.path.join(direct_path_item, 'direct_annotation.json'),'r',encoding='UTF8'))
            captions = self.load_captions(inv_ann_dict)
            captions += self.load_captions_weibo(direct_dict)
            imgs = self.load_imgs_direct_search(direct_path_item, direct_dict)
            qImg, qCap = self.load_queries(key)
            sample = {'label': label, 'caption': captions, 'imgs': imgs, 'qImg': qImg, 'qCap': qCap}
        else:
            direct_path_item = os.path.join(self.queries_root_dir, item['direct_path'])
            inverse_path_item = os.path.join(self.queries_root_dir, item['inv_path'])
            inv_ann_dict = json.load(open(os.path.join(inverse_path_item, 'inverse_annotation.json'),'r',encoding='UTF8'))
            direct_dict = json.load(open(os.path.join(direct_path_item, 'direct_annotation.json'),'r',encoding='UTF8'))
            captions = self.load_captions(inv_ann_dict)
            captions += self.load_captions_weibo(direct_dict)
            imgs = self.load_imgs_direct_search(direct_path_item, direct_dict)
            qImg, qCap = self.load_queries(key)
            sample = {'caption': captions, 'imgs': imgs, 'qImg': qImg, 'qCap': qCap}
        return sample, len(captions), imgs.shape[0]    
        # 返回样本（包含核查文本、核查图片、query图片、query文本），样本个数，图片个数

2）Dataloader
将dataset数据集传入DataLoader，实现批量读取数据。
dataset：传入的数据集
batch_size：每个batch有多少个样本
shuffle：在每个epoch开始的时候，对数据进行重新排序
collate_fn：指定如何将sample list组成一个mini-batch数据。传给它参数需要是一个callable对象，需要实现对组建的batch的处理逻辑，并返回每个batch的数据。在这里传入的是collate_context_bert_train、collate_context_bert_test函数。
return_list：数据是否以list形式返回

# load DataLoader
from paddle.io import DataLoader
train_dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True, collate_fn = collate_context_bert_train, return_list=True)
val_dataloader = DataLoader(val_dataset, batch_size=4, shuffle=False, collate_fn = collate_context_bert_train,  return_list=True)
test_dataloader = DataLoader(test_dataset, batch_size=2, shuffle=False, collate_fn = collate_context_bert_test, return_list=True)

这里的mini-batch函数有两个，实现代码如下：

#文本行图像长度不一，需要自定义整理，进行格式大小统一，将数据整理成batch
def collate_context_bert_train(batch):
    #print(batch)
    samples = [item[0] for item in batch]
    max_captions_len = max([item[1] for item in batch])
    max_images_len = max([item[2] for item in batch])
    qCap_batch = []
    qImg_batch = []
    img_batch = []
    cap_batch = []
    labels = []
    for j in range(0,len(samples)):
        sample = samples[j]
        labels.append(sample['label'])
        captions = sample['caption']
        cap_len = len(captions)
        for i in range(0,max_captions_len-cap_len):
            captions.append("")
        if len(sample['imgs'].shape) > 2:
            padding_size = (max_images_len-sample['imgs'].shape[0], sample['imgs'].shape[1], sample['imgs'].shape[2], sample['imgs'].shape[3])
        else:
            padding_size = (max_images_len-sample['imgs'].shape[0],sample['imgs'].shape[1])
        padded_mem_img = paddle.concat((sample['imgs'], paddle.zeros(padding_size)),axis=0)
        img_batch.append(padded_mem_img)#pad证据图片
        cap_batch.append(captions)
        qImg_batch.append(sample['qImg'])#[3, 224, 224]
        qCap_batch.append(sample['qCap'])
    img_batch = paddle.stack(img_batch, axis=0)
    qImg_batch = paddle.stack(qImg_batch, axis=0)
    labels = paddle.stack(labels, axis=0)
    return labels, cap_batch, img_batch, qCap_batch, qImg_batch

def collate_context_bert_test(batch):
    samples = [item[0] for item in batch]
    max_captions_len = max([item[1] for item in batch])
    max_images_len = max([item[2] for item in batch])
    qCap_batch = []
    qImg_batch = []
    img_batch = []
    cap_batch = []
    for j in range(0,len(samples)):
        sample = samples[j]
        captions = sample['caption']
        cap_len = len(captions)
        for i in range(0,max_captions_len-cap_len):
            captions.append("")
        if len(sample['imgs'].shape) > 2:
            padding_size = (max_images_len-sample['imgs'].shape[0],sample['imgs'].shape[1],sample['imgs'].shape[2],sample['imgs'].shape[3])
        else:
            padding_size = (max_images_len-sample['imgs'].shape[0],sample['imgs'].shape[1])
        padded_mem_img = paddle.concat((sample['imgs'], paddle.zeros(padding_size)),axis=0)
        img_batch.append(padded_mem_img)
        cap_batch.append(captions)
        qImg_batch.append(sample['qImg'])
        qCap_batch.append(sample['qCap'])
    img_batch = paddle.stack(img_batch, axis=0)
    qImg_batch = paddle.stack(qImg_batch, axis=0)
    return cap_batch, img_batch, qCap_batch, qImg_batch

2、模型定义

主要是Network，其中ErnieMModel由于是预训练的模型，所以不需要写forward。

class EncoderCNN(nn.Layer):
    def __init__(self, resnet_arch = 'resnet101'):
        super(EncoderCNN, self).__init__()
        if resnet_arch == 'resnet101':
            resnet = models.resnet101(pretrained=True)
        modules = list(resnet.children())[:-2]
        self.resnet = nn.Sequential(*modules)
        self.adaptive_pool = nn.AdaptiveAvgPool2D((1, 1))
    def forward(self, images, features='pool'):
        out = self.resnet(images)
        if features == 'pool':
            out = self.adaptive_pool(out)
            out = paddle.reshape(out, (out.shape[0],out.shape[1]))
        return out

class NetWork(nn.Layer):
    def __init__(self, mode):
        super(NetWork, self).__init__()
        self.mode = mode
        self.ernie = ErnieMModel.from_pretrained('ernie-m-base')
        self.tokenizer = ErnieMTokenizer.from_pretrained('ernie-m-base')
        self.resnet = EncoderCNN()
        self.classifier1 = nn.Linear(2*(768+2048),1024)
        self.classifier2 = nn.Linear(1024,3)
        self.attention_text = nn.MultiHeadAttention(768,16)
        self.attention_image = nn.MultiHeadAttention(2048,16)
        if self.mode == 'text':
            self.classifier = nn.Linear(768,3)
        self.resnet.eval()

    def forward(self,qCap,qImg,caps,imgs):
        self.resnet.eval()
        encode_dict_qcap = self.tokenizer(text = qCap ,max_length = 128 ,truncation=True, padding='max_length')
        input_ids_qcap = encode_dict_qcap['input_ids']
        input_ids_qcap = paddle.to_tensor(input_ids_qcap)
        qcap_feature, pooled_output= self.ernie(input_ids_qcap) #(b,length,dim)
        if self.mode == 'text':
            logits = self.classifier(qcap_feature[:,0,:].squeeze(1))
            return logits
        caps_feature = []
        for i,caption in enumerate (caps):
            encode_dict_cap = self.tokenizer(text = caption ,max_length = 128 ,truncation=True, padding='max_length')
            input_ids_caps = encode_dict_cap['input_ids']
            input_ids_caps = paddle.to_tensor(input_ids_caps)
            cap_feature, pooled_output= self.ernie(input_ids_caps) #(b,length,dim)
            caps_feature.append(cap_feature)
        caps_feature = paddle.stack(caps_feature,axis=0) #(b,num,length,dim)
        caps_feature = caps_feature.mean(axis=1)#(b,length,dim)
        caps_feature = self.attention_text(qcap_feature,caps_feature,caps_feature) #(b,length,dim)
        imgs_features = []
        for img in imgs:
            imgs_feature = self.resnet(img) #(length,dim)
            imgs_features.append(imgs_feature)
        imgs_features = paddle.stack(imgs_features,axis=0) #(b,length,dim)
        qImg_features = []
        for qImage in qImg:
            qImg_feature = self.resnet(qImage.unsqueeze(axis=0)) #(1,dim)
            qImg_features.append(qImg_feature)
        qImg_feature = paddle.stack(qImg_features,axis=0) #(b,1,dim)
        imgs_features = self.attention_image(qImg_feature,imgs_features,imgs_features) #(b,1,dim)
        # [1, 128, 768] [1, 128, 768] [1, 1, 2048] [1, 1, 2048] origin
        # print(qcap_feature.shape,caps_feature.shape,qImg_feature.shape,imgs_features.shape)
        # print((qcap_feature[:,0,:].shape,caps_feature[:,0,:].shape,qImg_feature.squeeze(1).shape,imgs_features.squeeze(1).shape))
        # ([1,768], [1 , 768], [1, 2048], [1,  2048])
        feature = paddle.concat(x=[qcap_feature[:,0,:], caps_feature[:,0,:], qImg_feature.squeeze(1), imgs_features.squeeze(1)], axis=-1)
        logits = self.classifier1(feature)
        logits = self.classifier2(logits)
        return logits

model = NetWork("image")

3、模型训练

训练参数设置，包含训练周期，学习率lr，优化器，损失函数，评估指标等

# train_setting
epochs = 2    #迭代周期为2，每个周期都会生成一组模型参数
num_training_steps = len(train_dataloader) * epochs
warmup_steps = int(num_training_steps*0.1)
print(num_training_steps,warmup_steps)      #5592 559
# 定义 learning_rate_scheduler，负责在训练过程中对 lr 进行调度
lr_scheduler = LinearDecayWithWarmup(1e-6, num_training_steps, warmup_steps)
# 训练结束后，存储模型参数
save_dir ="checkpoint/"   #该目录是指在每个周期中最终保存的模型参数
best_dir = "best_model"   #该目录为最好的模型参数，即为最终预测需要的模型参数
# 创建保存的文件夹
os.makedirs(save_dir,exist_ok=True)
os.makedirs(best_dir,exist_ok=True)

decay_params = [
    p.name for n, p in model.named_parameters()
    if not any(nd in n for nd in ["bias", "norm"])
]

# 定义优化器 Optimizer
optimizer = paddle.optimizer.AdamW(
    learning_rate=lr_scheduler,
    parameters=model.parameters(),
    weight_decay=1.2e-4, 
    apply_decay_param_fun=lambda x: x in decay_params)

# 定义损失函数，交叉熵损失
criterion = paddle.nn.loss.CrossEntropyLoss()

# 评估的时候采用准确率指标
metric = paddle.metric.Accuracy()

# 定义线下评估 评价指标为acc，注意线上评估是macro-f1 score
@paddle.no_grad()
def evaluate(model, criterion, metric, data_loader):
    model.eval()
    metric.reset()
    losses = []
    for batch in data_loader:
        labels, cap_batch, img_batch, qCap_batch, qImg_batch = batch
        logits = model(qCap=qCap_batch,qImg=qImg_batch,caps=cap_batch,imgs=img_batch)
        loss = criterion(logits, labels)
        losses.append(loss.numpy())
        correct = metric.compute(logits, labels)
        metric.update(correct)
        accu = metric.accumulate()
    print("eval loss: %.5f, accu: %.5f" % (np.mean(losses), accu))
    model.train()
    metric.reset()
    return np.mean(losses), accu

定义训练，包含五个部分：模型，损失函数，评价指标，训练dataloader，验证dataloader

def do_train(model, criterion, metric, val_dataloader, train_dataloader):
    print("train run start")
    global_step = 0
    tic_train = time.time()
    best_accuracy = 0.0
    for epoch in range(1, epochs + 1):
        for step, batch in enumerate(train_dataloader, start=1):
            labels, cap_batch, img_batch, qCap_batch, qImg_batch = batch
            probs = model(qCap=qCap_batch, qImg=qImg_batch, caps=cap_batch, imgs=img_batch)
            loss = criterion(probs, labels)
            correct = metric.compute(probs, labels)
            metric.update(correct)
            acc = metric.accumulate()

            global_step += 1
            # 每间隔 100 step 输出训练指标
            if global_step % 100 == 0:
                print(
                    "global step %d, epoch: %d, batch: %d, loss: %.5f, accu: %.5f, speed: %.2f step/s"
                    % (global_step, epoch, step, loss, acc,
                       10 / (time.time() - tic_train)))
                tic_train = time.time()
            loss.backward()
            optimizer.step()
            lr_scheduler.step()
            optimizer.clear_grad()

            # 每间隔一个epoch 在验证集进行评估
            if global_step % len(train_dataloader) == 0:
                eval_loss, eval_accu = evaluate(model, criterion, metric, val_dataloader)
                save_param_path = os.path.join(save_dir + str(epoch), 'model_state.pdparams')
                paddle.save(model.state_dict(), save_param_path)
                if (best_accuracy < eval_accu):
                    best_accuracy = eval_accu
                    # 保存模型
                    save_param_path = os.path.join(best_dir, 'model_best.pdparams')
                    paddle.save(model.state_dict(), save_param_path)
                    
do_train(model, criterion, metric, val_dataloader, train_dataloader)

4、模型预测

在预测模型前，需要重启内核，释放了内存（图片数据很吃内存）。

需要重新运行第一块和第二块的代码，再运行以下代码：

params_path = 'best_model/model_best.pdparams'

#加载训练好的模型参数
if params_path and os.path.isfile(params_path):
    # 加载模型参数
    state_dict = paddle.load(params_path)
    model.set_dict(state_dict)
    print("Loaded parameters from %s" % params_path)
 
results = []
# 切换model模型为评估模式，关闭dropout等随机因素
id2name ={ 0:"non-rumor", 1:"rumor",2:"unverified"}
model.eval()
count=0
bar = tqdm(test_dataloader, total=len(test_dataloader))
for batch in bar:
    count+=1
    cap_batch, img_batch, qCap_batch, qImg_batch = batch
    logits = model(qCap=qCap_batch,qImg=qImg_batch,caps=cap_batch,imgs=img_batch)
    # 预测分类
    probs = F.softmax(logits, axis=-1)
    label = paddle.argmax(probs, axis=1).numpy()
    results += label.tolist()

print(results[:5])
print(len(results))
results = [id2name[i] for i in results]

输出结果

#id/label
#字典中的key值即为csv中的列名
id_list=range(len(results))
print(id_list)
frame = pd.DataFrame({'id':id_list,'label':results})
frame.to_csv("result.csv",index=False,sep=',')

# 根据要求打包
!zip test.zip result.csv

三、写在最后

讲讲最精华的部分，需要从哪些地方入手来提升模型，谈谈我的理解：

1、数据源：数据并不干净，图片数据量很大，是否有操作空间
2、数据特征：抽取的数据特征是否存在信息丢失，或者说能补充更多通过数据探索发现的规律特征
3、模型选择：baseline是一个比较稳的方式，也可以尝试
4、参数调整：这部分尽量放到最后做，好的参数也可能让模型work更好

本次记录主要还是以学习为主，抽了工作之余来进行baseline的翻译和整理。探索了一个带大家最快上手的路径，降低大家的入门难度。

看完觉得有用的话，记得点个赞，不做白嫖党~