109 lines
4.1 KiB
Python
109 lines
4.1 KiB
Python
import torch
|
||
import torch.nn as nn
|
||
import torch.nn.functional as F
|
||
|
||
class VideoClassifierV3_5(nn.Module):
|
||
def __init__(self, embedding_dim=1024, hidden_dim=512, output_dim=3):
|
||
super().__init__()
|
||
self.num_channels = 4
|
||
self.channel_names = ['title', 'description', 'tags', 'author_info']
|
||
|
||
# 可学习温度系数
|
||
self.temperature = nn.Parameter(torch.tensor(1.7))
|
||
|
||
# 带约束的通道权重(使用Sigmoid替代Softmax)
|
||
self.channel_weights = nn.Parameter(torch.ones(self.num_channels))
|
||
|
||
# 增强的非线性层
|
||
self.fc = nn.Sequential(
|
||
nn.Linear(embedding_dim * self.num_channels, hidden_dim*2),
|
||
nn.BatchNorm1d(hidden_dim*2),
|
||
nn.Dropout(0.1),
|
||
nn.ReLU(),
|
||
nn.Linear(hidden_dim*2, hidden_dim),
|
||
nn.LayerNorm(hidden_dim),
|
||
nn.Linear(hidden_dim, output_dim)
|
||
)
|
||
|
||
# 权重初始化
|
||
self._init_weights()
|
||
|
||
def _init_weights(self):
|
||
for layer in self.fc:
|
||
if isinstance(layer, nn.Linear):
|
||
# 使用ReLU的初始化参数(GELU的近似)
|
||
nn.init.kaiming_normal_(layer.weight, nonlinearity='relu') # 修改这里
|
||
|
||
# 或者使用Xavier初始化(更适合通用场景)
|
||
# nn.init.xavier_normal_(layer.weight, gain=nn.init.calculate_gain('relu'))
|
||
|
||
nn.init.zeros_(layer.bias)
|
||
|
||
|
||
def forward(self, input_texts, sentence_transformer):
|
||
# 合并文本进行批量编码
|
||
all_texts = [text for channel in self.channel_names for text in input_texts[channel]]
|
||
|
||
# 冻结的文本编码
|
||
with torch.no_grad():
|
||
embeddings = torch.tensor(
|
||
sentence_transformer.encode(all_texts),
|
||
device=next(self.parameters()).device
|
||
)
|
||
|
||
# 分割并加权通道特征
|
||
split_sizes = [len(input_texts[name]) for name in self.channel_names]
|
||
channel_features = torch.split(embeddings, split_sizes, dim=0)
|
||
channel_features = torch.stack(channel_features, dim=1)
|
||
|
||
# 自适应通道权重(Sigmoid约束)
|
||
weights = torch.sigmoid(self.channel_weights) # [0,1]范围
|
||
weighted_features = channel_features * weights.unsqueeze(0).unsqueeze(-1)
|
||
|
||
# 特征拼接
|
||
combined = weighted_features.view(weighted_features.size(0), -1)
|
||
|
||
return self.fc(combined)
|
||
|
||
def get_channel_weights(self):
|
||
"""获取各通道权重(带温度调节)"""
|
||
return torch.softmax(self.channel_weights / self.temperature, dim=0).detach().cpu().numpy()
|
||
|
||
|
||
class AdaptiveRecallLoss(nn.Module):
|
||
def __init__(self, class_weights, alpha=0.8, gamma=2.0, fp_penalty=0.5):
|
||
"""
|
||
Args:
|
||
class_weights (torch.Tensor): 类别权重
|
||
alpha (float): 召回率调节因子(0-1)
|
||
gamma (float): Focal Loss参数
|
||
fp_penalty (float): 类别0假阳性惩罚强度
|
||
"""
|
||
super().__init__()
|
||
self.class_weights = class_weights
|
||
self.alpha = alpha
|
||
self.gamma = gamma
|
||
self.fp_penalty = fp_penalty
|
||
|
||
def forward(self, logits, targets):
|
||
# 基础交叉熵损失
|
||
ce_loss = F.cross_entropy(logits, targets, weight=self.class_weights, reduction='none')
|
||
|
||
# Focal Loss组件
|
||
pt = torch.exp(-ce_loss)
|
||
focal_loss = ((1 - pt) ** self.gamma) * ce_loss
|
||
|
||
# 召回率增强(对困难样本加权)
|
||
class_mask = F.one_hot(targets, num_classes=len(self.class_weights))
|
||
class_weights = (self.alpha + (1 - self.alpha) * pt.unsqueeze(-1)) * class_mask
|
||
recall_loss = (class_weights * focal_loss.unsqueeze(-1)).sum(dim=1)
|
||
|
||
# 类别0假阳性惩罚
|
||
probs = F.softmax(logits, dim=1)
|
||
fp_mask = (targets != 0) & (torch.argmax(logits, dim=1) == 0)
|
||
fp_loss = self.fp_penalty * probs[:, 0][fp_mask].pow(2).sum()
|
||
|
||
# 总损失
|
||
total_loss = recall_loss.mean() + fp_loss / len(targets)
|
||
|
||
return total_loss |