生成一个基于分数阶微分的image caption模型代码

以下是基于分数阶微分的image caption模型的代码：

import torch
import torch.nn as nn
import torchvision.models as models
import numpy as np
from scipy import signal

class FractionalDiff(nn.Module):
    def __init__(self, order, device):
        super(FractionalDiff, self).__init__()
        self.order = order
        self.device = device
        self.conv = nn.Conv1d(1, 1, 3, stride=1, padding=1, bias=False)
        filter = self.get_diff_filter()
        filter = torch.from_numpy(filter).float().to(self.device)
        filter = filter.view((1, 1, filter.shape[0]))
        self.conv.weight.data = filter
        self.conv.weight.requires_grad = False
        
    def forward(self, x):
        x = x.unsqueeze(1)
        out = self.conv(x)
        out = out.squeeze(1)
        return out
    
    def get_diff_filter(self):
        n = 201
        h = np.zeros(n)
        h[0] = 1
        a = self.order - 0.5
        for i in range(1, n):
            h[i] = -h[i-1] * a / i
        h = np.concatenate((h, np.zeros(n)))
        window = signal.hann(2*n)
        h = h * window
        H = np.fft.fft(h)
        w = np.linspace(-np.pi, np.pi, 2*n)
        H = np.exp(1j*w*(self.order-0.5))*H
        h = np.real(np.fft.ifft(H))
        h = h[:n]
        h = h / np.sum(np.abs(h))
        return h

class ImageCaption(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers, order, device):
        super(ImageCaption, self).__init__()
        self.device = device
        self.order = order
        self.encoder = models.resnet18(pretrained=True)
        self.encoder.fc = nn.Linear(512, embedding_dim)
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_dim, vocab_size)
        self.fdiff = FractionalDiff(order, device)
        
    def forward(self, images, captions):
        features = self.encoder(images)
        features = features.view(features.size(0), -1)
        embeddings = self.embedding(captions)
        embeddings = torch.cat((features.unsqueeze(1), embeddings), 1)
        embeddings = self.fdiff(embeddings)
        hiddens, _ = self.lstm(embeddings)
        outputs = self.fc(hiddens)
        return outputs

该模型使用了一个名为`FractionalDiff`的自定义模块，用于计算分数阶微分。在这个模块中，我们使用了一个可以生成分数阶微分滤波器的函数，该函数返回的滤波器可以应用于一维信号。在`ImageCaption`模型中，我们使用了ResNet-18来提取图像特征，然后使用一个LSTM来生成对应的标题。在输入标题之前，我们将图像特征与一个起始符（SOS）连接起来，然后将它们传递给分数阶微分模块，该模块将对它们进行分数阶微分。最后，我们使用一个全连接层将隐藏状态映射到词汇表的大小。