48 lines
1.9 KiB
Python
48 lines
1.9 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
|
|
class VideoClassifierV1_5(nn.Module):
|
|
def __init__(self, embedding_dim=1024, hidden_dim=256, output_dim=3):
|
|
super().__init__()
|
|
self.num_channels = 4
|
|
self.channel_names = ['title', 'description', 'tags', 'author_info']
|
|
|
|
# 通道权重参数（可学习）
|
|
self.channel_weights = nn.Parameter(torch.ones(self.num_channels))
|
|
|
|
# 全连接层
|
|
self.fc1 = nn.Linear(embedding_dim * self.num_channels, hidden_dim)
|
|
self.fc2 = nn.Linear(hidden_dim, output_dim)
|
|
self.log_softmax = nn.LogSoftmax(dim=1)
|
|
|
|
def forward(self, input_texts, sentence_transformer):
|
|
# 各通道特征提取
|
|
channel_features = []
|
|
for _, name in enumerate(self.channel_names):
|
|
# 获取当前通道的批量文本
|
|
batch_texts = input_texts[name]
|
|
|
|
# 使用SentenceTransformer生成嵌入
|
|
embeddings = torch.tensor(
|
|
sentence_transformer.encode(batch_texts, task="classification")
|
|
)
|
|
channel_features.append(embeddings)
|
|
|
|
# 将通道特征堆叠并加权
|
|
channel_features = torch.stack(channel_features, dim=1) # [batch_size, num_channels, embedding_dim]
|
|
channel_weights = torch.softmax(self.channel_weights, dim=0)
|
|
weighted_features = channel_features * channel_weights.unsqueeze(0).unsqueeze(-1)
|
|
|
|
# 拼接所有通道特征
|
|
combined_features = weighted_features.view(weighted_features.size(0), -1) # [batch_size, num_channels * embedding_dim]
|
|
|
|
# 全连接层
|
|
x = torch.relu(self.fc1(combined_features))
|
|
output = self.fc2(x)
|
|
output = self.log_softmax(output)
|
|
return output
|
|
|
|
def get_channel_weights(self):
|
|
"""获取各通道的权重（用于解释性分析）"""
|
|
return torch.softmax(self.channel_weights, dim=0).detach().cpu().numpy()
|