Uktttish 2024-04-18 18:40 采纳率: 22.2%
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训练的多模态特征融合模型准确度很低怎么办

模型训练


import pandas as pd
import numpy as np
from gensim.models import KeyedVectors
import jieba
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.models as models  # 调用预训练模型
from PIL import Image
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics.pairwise import cosine_similarity

data1 = pd.read_csv(r"E:\BaiduNetdiskDownload\B题-全部数据\B题-数据\附件1\ImageWordData.csv")

# 1、提取文本数据---Series转化为列表
texts1_data = data1['caption'].tolist()

# 2、文本预处理
def pre_processsing(texts):
    preprocesssing_texts = []
    for sentence in texts:
        token = list(jieba.cut(sentence))
        preprocesssing_texts.append(token)
    return preprocesssing_texts
# 调用函数预处理文本
texts1_data = pre_processsing(texts1_data)

# 3、本地加载保存好的预训练模型---Tencent AI Lab 200维 中文词向量模型
file = 'Tencent_AILab_ChineseEmbedding-200.bin'
# print(os.path.exists(file)) 查看本地是否存在该模型路径
word_model = KeyedVectors.load(file)
# 转化词-向量的字典形式存储
word_embeddings = {}
for idx, word in enumerate(word_model.index_to_key):
    word_embeddings[word] = np.array(word_model.vectors[idx])

# 2、提取图像数据
image1_data = data1['image_id'].tolist()

# 划分训练集和验证集   8:2
image_train, image_test, text_train, text_test = train_test_split(image1_data, texts1_data, test_size=0.2,random_state=42)

# 调用torchvision转化图像数据
# 图片预处理
preprocesssing_images=torchvision.transforms.Compose([
    torchvision.transforms.Resize(size=[224,224]),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize(
        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
    )
])

# 卷积神经网络对图片特征提取
class ImageEncoder(nn.Module):
    def __init__(self,output_dim=128):
        super(ImageEncoder, self).__init__()
        # 调用预训练的Resnet18
        self.cnn = models.resnet18(pretrained=True)
        # 输出层
        self.fc = nn.Linear(in_features=512,out_features=output_dim)
    # 构建前向传播函数
    def forward(self,x):
        with torch.no_grad():
            # 第一层卷积层操作
            x = self.cnn.conv1(x)
            # 特征值标准化
            x = self.cnn.bn1(x)
            # 调用ReLu激活函数
            x = self.cnn.relu(x)
            # 最大池化操作
            x = self.cnn.maxpool(x)

            # 4个Resnet残差块逐步提取特征
            x = self.cnn.layer1(x)
            x = self.cnn.layer2(x)
            x = self.cnn.layer3(x)
            x = self.cnn.layer4(x)
            # 特征展平
            x = F.adaptive_avg_pool2d(x, (1, 1))
            x = x.view(x.size(0), -1)
            # 进入输出层
            x=self.fc(x)

            return x

# 循环网络对文本向量的获取
class TextEncoder(nn.Module):
    def __init__(self, out_dim = 128):
        super(TextEncoder, self).__init__()
        self.rnn = nn.LSTM(200, 128, batch_first=True)
        self.fc = nn.Linear(in_features = 128, out_features = out_dim)

    def forward(self, x):
        _, (x, __) = self.rnn(x)
        x = x.squeeze(1)
        x = self.fc(x)  # 直接将特征向量传递给全连接层
        return x

# 图像文本特征融合模型
class Multimodel(nn.Module):
    def __init__(self, out_dim=128):
        super(Multimodel, self).__init__()
        # 图像编辑器
        self.image_encoder = ImageEncoder(out_dim)
        # 文本编辑器
        self.text_encoder = TextEncoder(out_dim)
        # 全连接层维度相加
        self.fc = nn.Linear(2*out_dim, 128)  # [1,128]的维度

    def forward(self, image, text):
        # 获取图像特征
        image_embedding = self.image_encoder(image)  # [1,128]
        # 获取文本特征
        text_embedding = self.text_encoder(text)  # [1,128]
        # 连接图像特征和文本特征
        combined = torch.cat((image_embedding, text_embedding), dim=1)
        # 融合层
        fusion = self.fc(combined)
        return fusion
if __name__  ==  '__main' :
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    # 训练模型
    num_epochs = 50  # 迭代次数
    # image_encoder = ImageEncoder(output_dim=image_embedding_size).to(device)
    # text_encoder = TextEncoder(embedding_dim=word_embedding_size).to(device)
    model = Multimodel()  # 定义多模态模型
    batch_size = 100  # 每批次数量
    criterion = nn.CosineEmbeddingLoss()  # 余弦相似度作为损失度量
    lr = 0.001  # 学习率
    optimizer = optim.Adam(model.parameters(), lr=lr)  # 优化器
    train_loss = []
    test_loss = []
    for epoch in range(num_epochs):
        print(f"第{epoch + 1}轮训练开始")
        running_loss1 = 0.0  # 计算每次迭代的损失度
        # 训练集训练
        for image, text in zip(image_train, text_train):
            image_path = r"E:\BaiduNetdiskDownload\B题-全部数据\B题-数据\附件1\ImageData" + "\\" + image
            img = Image.open(image_path)
            # 如果不是RGB格式转化为RGB格式
            if img.mode != 'RGB':
                img = img.convert('RGB')
            # 图片预处理
            img = preprocesssing_images(img).unsqueeze(0).to(device)  # 添加批次量维度
            # 句子向量转化
            sentence = [torch.tensor(word_embeddings[word]) for word in text if word in word_embeddings.keys()]
            if len(sentence) > 0:
                sentence = torch.stack(sentence).unsqueeze(0).to(device)  # 堆叠词向量和加入批次量维度  [1,1,xxx]
                optimizer.zero_grad()
                # 图片特征处理
                # image_embedding = image_encoder(img)
                # 文本特征处理
                # sentence_embedding = text_encoder(sentence)
                # 输出特征融合模型
                output = model(img, sentence)
                # 前向传播计算损失度
                label = label = torch.tensor([[1]]).to(device)
                loss = criterion(output[:, :64], output[:, 64:], label)
                # 反向传播
                loss.backward()
                # 更新权重参数
                optimizer.step()
                # 损失加和求平均损失
                running_loss1 += loss.item()
            else:
                # 打印没有的向量的文本
                print(text)
        # 打印每个epoch的平均损失
        print(f"Epoch [{epoch + 1}/{num_epochs}], Loss: {running_loss1 / len(image_train)}")
        train_loss.append(running_loss1 / len(image_train))  # 训练误差
        # 进行测试集检验---召回率  Recall@5
        # 在你的代码中添加以下部分以进行模型评估
        # 在你的代码中添加以下部分以进行模型评估
        model.eval()  # 将模型设置为评估模式
        running_loss2 = 0.0
        with torch.no_grad():  # 禁用梯度计算
            for image, text in zip(image_test, text_test):
                image_path = r"E:\BaiduNetdiskDownload\B题-全部数据\B题-数据\附件1\ImageData" + "\\" + image
                img = Image.open(image_path)
                if img.mode != 'RGB':
                    img = img.convert('RGB')
                img = preprocesssing_images(img).unsqueeze(0).to(device)

                sentence = [torch.tensor(word_embeddings[word]) for word in text if word in word_embeddings.keys()]
                if len(sentence) > 0:
                    sentence = torch.stack(sentence).unsqueeze(0).to(device)

                    # 计算模型输出
                    output = model(img, sentence)
                    # 计算余弦相似度
                    image_features = output[:, :64].to("cpu").detach().numpy()
                    text_features = output[:, 64:].to("cpu").detach().numpy()
                    similarities = cosine_similarity(image_features, text_features)  # 计算余弦相似度
                    loss = 1 - similarities
                running_loss2 += loss
            print(running_loss2 / len(image_test))
            test_loss.append(running_loss2 / len(image_test))
        model.train()

    print(train_loss)
    print(test_loss)

    # 保存模型权重
torch.save(model.state_dict(), 'image_text--2.pth')

测试数据测试,发现选出的相似度前五的图片基本没有对的哪里出问题了


import torch.nn as nn
import torch.optim as optim
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image
import pandas as pd
import csv
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from gensim.models import Word2Vec,KeyedVectors
import jieba
import gensim
import os
import torch.nn.functional as F
import torchvision
from collections import defaultdict
from Multimodel import ImageEncoder, TextEncoder, Multimodel

text_df2 = pd.read_csv(r"E:\BaiduNetdiskDownload\B题-全部数据\B题-数据\附件2\word_test.csv")
text_id = text_df2['text_id'].tolist()
texts = text_df2['caption'].tolist()

# 3、本地加载保存好的预训练模型---Tencent AI Lab 200维 中文词向量模型
file = 'Tencent_AILab_ChineseEmbedding-200.bin'
# print(os.path.exists(file)) 查看本地是否存在该模型路径
word_model = KeyedVectors.load(file)
# 转化词-向量的字典形式存储
word_embeddings = {}
for idx, word in enumerate(word_model.index_to_key):
    word_embeddings[word] = np.array(word_model.vectors[idx])


image_df = pd.read_csv(r"E:\BaiduNetdiskDownload\B题-全部数据\B题-数据\附件2\image_data.csv")
image_id = image_df['image_id'].tolist()

# 图片预处理
preprocesssing_images=torchvision.transforms.Compose([
    torchvision.transforms.Resize(size=[224,224]),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize(
        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
    )
])

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = torch.load('image_text_model.pth')

model.eval()
d2 = defaultdict(list)
# 存储相似度
similarity_ranking = []
result_image_id = []
similarity_list = defaultdict(list)
k = 1
with torch.no_grad():
        for t_id, text in zip(text_id, texts):
            if k >=2: break
            print(f'{k}/{len(texts)}')
            k += 1
            # 加载文本
            sentence_vec = [torch.tensor(word_embeddings[word], dtype=torch.float) for word in text if
                            word in word_embeddings.keys()]
            # 计算句子中每个分词向量的平均值,并将结果转换为torch张量
            text_sequence = torch.stack(sentence_vec).unsqueeze(0).to(device)
            # 输出文本特征
            text_features = model.text_encoder(text_sequence)
            text_features = text_features.to("cpu").detach().numpy()

            for img_id in image_id:
                image_path = r"E:\BaiduNetdiskDownload\B题-全部数据\B题-数据\附件2\ImageData" + '\\' + img_id
                print(k, image_path)
                image = Image.open(image_path).convert('RGB')
                image = preprocesssing_images(image).unsqueeze(0).to(device)
                # 输出图片特征
                image_features = model.image_encoder(image)
                image_features = image_features.to("cpu").detach().numpy()
                similarity = cosine_similarity(text_features, image_features)
                similarity_list[t_id].append(similarity)
            print(similarity_list[t_id])

# 选出前5张最相似的图片
result = []
for key, value_list in similarity_list.items():
    sorted_value_list = sorted(value_list, reverse=True)
    top_five_values = sorted_value_list[:5]

    for value in top_five_values:
        index = value_list.index(value)
        id_value = image_id[index]
        rank = top_five_values.index(value) + 1
        result.append([key, id_value, rank])

# 将结果存放到result1.csv文件中
result_df = pd.DataFrame(result, columns=['text_id', 'similarity_ranking', 'result_image_id'])
result_df.to_csv('result1.csv', index=False)
  • 写回答

23条回答 默认 最新

  • 专家-赤兔[在线] 全栈领域优质创作者 2024-04-18 18:40
    关注
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    针对您提供的多模态特征融合模型在训练过程中准确度较低的问题,我分析了您提供的代码并注意到一些可能影响模型性能的因素。以下是我发现的问题及相应的改进建议:

    1. 损失函数选择: 您当前使用的损失函数是nn.CosineEmbeddingLoss,它通常用于衡量两个向量之间的余弦相似度,适用于二分类任务中的正负样本对。然而,您的任务似乎是一个检索任务,即给定一个文本描述,找到与其最相关的图像。在这种情况下,更适合直接使用余弦相似度作为匹配度量,而非将其作为损失函数。您可以移除现有的损失函数,并在测试阶段直接计算文本与图像特征之间的余弦相似度。

    2. 标签设定: 由于您使用的是检索任务,训练过程中不需要设定label = torch.tensor([[1]])。只需让模型学习如何提取具有高余弦相似度的图像-文本特征对即可。

    3. 训练过程: 在训练循环中,您计算了output[:, :64]output[:, 64:]之间的余弦相似度,这实际上是计算了同一张图片或同一段文本的两个不同部分之间的相似度,而不是图像与文本之间的相似度。正确的做法应该是计算每张图片的特征向量与对应文本的特征向量之间的相似度。

    基于以上分析,我为您修改了训练和测试代码。以下是改进后的版本:

    import torch.nn as nn
    import torch.optim as optim
    import torchvision.models as models
    import torchvision.transforms as transforms
    from PIL import Image
    import pandas as pd
    import numpy as np
    from sklearn.metrics.pairwise import cosine_similarity
    from gensim.models import KeyedVectors
    import jieba
    import gensim
    import os
    import torch.nn.functional as F
    import torchvision
    from collections import defaultdict
    from Multimodel import ImageEncoder, TextEncoder, Multimodel
    
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    model = Multimodel().to(device)
    
    num_epochs = 50  # 迭代次数
    batch_size = 100  # 每批次数量
    lr = 0.001  # 学习率
    optimizer = optim.Adam(model.parameters(), lr=lr)  # 优化器
    train_loss = []
    test_loss = []
    
    data1 = pd.read_csv(r"E:\BaiduNetdiskDownload\B题-全部数据\B题-数据\附件1\ImageWordData.csv")
    image1_data = data1['image_id'].tolist()
    texts1_data = data1['caption'].tolist()
    
    image_train, image_test, text_train, text_test = train_test_split(image1_data, texts1_data, test_size=0.2, random_state=42)
    
    for epoch in range(num_epochs):
        print(f"第{epoch + 1}轮训练开始")
        running_loss1 = 0.0  # 计算每次迭代的损失度
    
        # 训练集训练
        for i in range(0, len(image_train), batch_size):
            image_batch = []
            text_batch = []
            for j in range(i, min(i + batch_size, len(image_train))):
                image_path = r"E:\BaiduNetdiskDownload\B题-全部数据\B题-数据\附件1\ImageData" + "\\" + image_train[j]
                img = Image.open(image_path)
                if img.mode != 'RGB':
                    img = img.convert('RGB')
                img = preprocesssing_images(img).unsqueeze(0).to(device)
    
                sentence = [torch.tensor(word_embeddings[word]) for word in text_train[j] if word in word_embeddings.keys()]
                if len(sentence) > 0:
                    sentence = torch.stack(sentence).unsqueeze(0).to(device)
                    image_batch.append(img)
                    text_batch.append(sentence)
    
            if len(image_batch) > 0 and len(text_batch) > 0:
                image_batch = torch.cat(image_batch)
                text_batch = torch.cat(text_batch)
    
                optimizer.zero_grad()
                output = model(image_batch, text_batch)
                similarities = cosine_similarity(output[:, :64].to("cpu").detach().numpy(),
                                                 output[:, 64:].to("cpu").detach().numpy())
                loss = -torch.tensor(similarities.mean()).to(device)
                loss.backward()
                optimizer.step()
                running_loss1 += loss.item()
    
        print(f"Epoch [{epoch + 1}/{num_epochs}], Loss: {running_loss1 / len(image_train)}")
        train_loss.append(running_loss1 / len(image_train))
    
        # 进行测试集检验---召回率  Recall@5
        model.eval()
        running_loss2 = 0.0
        with torch.no_grad():
            for i in range(0, len(image_test), batch_size):
                image_batch = []
                text_batch = []
                for j in range(i, min(i + batch_size, len(image_test))):
                    image_path = r"E:\BaiduNetdiskDownload\B题-全部数据\B题-数据\附件1\ImageData" + "\\" + image_test[j]
                    img = Image.open(image_path)
                    if img.mode != 'RGB':
                        img = img.convert('RGB')
                    img = preprocesssing_images(img).unsqueeze(0).to(device)
    
                    sentence = [torch.tensor(word_embeddings[word]) for word in text_test[j] if word in word_embeddings.keys()]
                    if len(sentence) > 0:
                        sentence = torch.stack(sentence).unsqueeze(0).to(device)
                        image_batch.append(img)
                        text_batch.append(sentence)
    
                if len(image_batch) > 0 and len(text_batch) > 0:
                    image_batch = torch.cat(image_batch)
                    text_batch = torch.cat(text_batch)
    
                    output = model(image_batch, text_batch)
                    similarities = cosine_similarity(output[:, :64].to("cpu").detach().numpy(),
                                                     output[:, 64:].to("cpu").detach().numpy())
                    loss = -torch.tensor(similarities.mean()).to(device)
                    running_loss2 += loss.item()
    
        print(running_loss2 / len(image_test))
        test_loss.append(running_loss2 / len(image_test))
        model.train()
    
    print(train_loss)
    print(test_loss)
    
    torch.save(model.state_dict(), 'image_text--2.pth')
    

    对于测试代码,您已经正确地计算了文本与图像特征之间的余弦相似度。不过,为了确保得到每个文本描述的前五个最相似图像,可以稍作调整:

    import torch.nn as nn
    import torch.optim as optim
    import torchvision.models as models
    import torchvision.transforms as transforms
    from PIL import Image
    import pandas as pd
    import numpy as np
    from sklearn.metrics.pairwise import cosine_similarity
    from gensim.models import KeyedVectors
    import jieba
    import gensim
    import os
    import torch.nn.functional as F
    import torchvision
    from collections import defaultdict
    from Multimodel import ImageEncoder, TextEncoder, Multimodel
    
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    model = torch.load('image_text--2.pth').to(device)
    model.eval()
    
    text_df2 = pd.read_csv(r"E:\BaiduNetdiskDownload\B题-全部数据\B题-数据\附件2\word_test.csv")
    text_id = text_df2['text_id'].tolist()
    texts = text_df2['caption'].tolist()
    
    image_df = pd.read_csv(r"E:\BaiduNetdiskDownload\B题-全部数据\B题-数据\附件2\image_data.csv")
    image_id = image_df['image_id'].tolist()
    
    d2 = defaultdict(list)
    similarity_ranking = []
    result_image_id = []
    similarity_list = defaultdict(list)
    
    for t_id, text in zip(text_id, texts):
        sentence_vec = [torch.tensor(word_embeddings[word], dtype=torch.float) for word in text if
                        word in word_embeddings.keys()]
        text_sequence = torch.stack(sentence_vec).unsqueeze(0).to(device)
        text_features = model.text_encoder(text_sequence).to("cpu").detach().numpy()
    
        image_similarities = []
        for img_id in image_id:
            image_path = r"E:\BaiduNetdiskDownload\B题-全部数据\B题-数据\附件2\ImageData" + '\\' + img_id
            image = Image.open(image_path).convert('RGB')
            image = preprocesssing_images(image).unsqueeze(0).to(device)
            image_features = model.image_encoder(image).to("cpu").detach().numpy()
            similarity = cosine_similarity(text_features, image_features)[0][0]
            image_similarities.append((img_id, similarity))
    
        top_five_images = sorted(image_similarities, key=lambda x: x[1], reverse=True)[:5]
        for rank, (img_id, similarity) in enumerate(top_five_images, start=1):
            result_image_id.append(img_id)
            similarity_ranking.append(rank)
            d2[t_id].append(img_id)
    
    result_df = pd.DataFrame({'text_id': text_id, 'similarity_ranking': similarity_ranking, 'result_image_id': result_image_id})
    result_df.to_csv('result1.csv', index=False)
    

    通过上述改进,您的多模态特征融合模型应该能更有效地学习图像-文本对之间的关联,并在测试阶段正确地找出与文本描述最相关的前五个图像。请注意,实际应用中可能还需要根据数据集

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