用pytorch写个image captioning模型

好的，您需要使用PyTorch编写一个图像字幕生成模型。您需要先加载图像数据集，然后使用卷积神经网络提取图像特征，并使用循环神经网络生成字幕。以下是一个简单的示例代码，可以作为您开始的点：

import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms
from torch.nn.utils.rnn import pack_padded_sequence

# 加载ResNet-101模型并返回提取图像特征的模块
def load_resnet():
    resnet = models.resnet101(pretrained=True)
    modules = list(resnet.children())[:-1]
    resnet = nn.Sequential(*modules)
    for p in resnet.parameters():
        p.requires_grad = False
    return resnet

# 使用循环神经网络生成图像字幕
class ImageCaptioning(nn.Module):
    def __init__(self, embed_size, hidden_size, vocab_size, num_layers=1):
        super(ImageCaptioning, self).__init__()
        self.resnet = load_resnet()
        self.embed = nn.Embedding(vocab_size, embed_size)
        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
        self.linear = nn.Linear(hidden_size, vocab_size)

    def forward(self, images, captions, lengths):
        features = self.resnet(images)
        embeddings = self.embed(captions)
        embeddings = torch.cat((features.unsqueeze(1), embeddings), 1)
        packed = pack_padded_sequence(embeddings, lengths, batch_first=True)
        hiddens, _ = self.lstm(packed)
        outputs = self.linear(hiddens[0])
        return outputs

这是一个基础模型，您可以根据自己的需求进行更改和调整，并且您需要准备自己的数据集进行训练。希望这能对您有所帮助！