add: filter model based on cascade classifier (V3.12)
This commit is contained in:
parent
3aa3bc8596
commit
f488c3ceda
GPG Key ID:
56209E0CCD8420C6
@ -23,3 +23,4 @@ Note
|
||||
2308: V3.11
|
||||
2243: V3.11 # 256维嵌入
|
||||
2253: V3.11 # 1024维度嵌入（对比）
|
||||
2337: V3.12 # 级联分类
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
from model2vec import StaticModel
|
||||
|
||||
@ -30,6 +31,10 @@ def prepare_batch(batch_data, device="cpu"):
|
||||
batch_tensor = torch.stack(channel_embeddings, dim=1) # 在 dim=1 上堆叠
|
||||
return batch_tensor
|
||||
|
||||
import onnxruntime as ort
|
||||
from transformers import AutoTokenizer
|
||||
from itertools import accumulate
|
||||
|
||||
def prepare_batch_per_token(batch_data, max_length=1024):
|
||||
"""
|
||||
将输入的 batch_data 转换为模型所需的输入格式 [batch_size, num_channels, seq_length, embedding_dim]。
|
||||
@ -46,18 +51,60 @@ def prepare_batch_per_token(batch_data, max_length=1024):
|
||||
返回:
|
||||
torch.Tensor: 形状为 [batch_size, num_channels, seq_length, embedding_dim] 的张量。
|
||||
"""
|
||||
# 初始化 tokenizer 和 ONNX 模型
|
||||
tokenizer = AutoTokenizer.from_pretrained("alikia2x/jina-embedding-v3-m2v-1024")
|
||||
session = ort.InferenceSession("./model/embedding_256/onnx/model.onnx")
|
||||
|
||||
# 1. 对每个通道的文本分别编码
|
||||
channel_embeddings = []
|
||||
model = StaticModel.from_pretrained("./model/embedding_256/")
|
||||
for channel in ["title", "description", "tags", "author_info"]:
|
||||
texts = batch_data[channel] # 获取当前通道的文本列表
|
||||
# 使用tokenizer将文本转换为tokens
|
||||
encoded_input = model.tokenizer(texts, padding=True, truncation=True, max_length=max_length, return_tensors='pt')
|
||||
with torch.no_grad():
|
||||
model_output = model.model(**encoded_input)
|
||||
# 提取最后一个隐藏层的结果
|
||||
embeddings = model_output.last_hidden_state.to(torch.float32) # 将embeddings 放在指定device上
|
||||
channel_embeddings.append(embeddings)
|
||||
|
||||
# Step 1: 生成 input_ids 和 offsets
|
||||
# 对每个文本单独编码，保留原始 token 长度
|
||||
encoded_inputs = [tokenizer(text, truncation=True, max_length=max_length, return_tensors='np') for text in texts]
|
||||
|
||||
# 提取每个文本的 input_ids 长度（考虑实际的 token 数量）
|
||||
input_ids_lengths = [len(enc["input_ids"][0]) for enc in encoded_inputs]
|
||||
|
||||
# 生成 offsets: [0, len1, len1+len2, ...]
|
||||
offsets = list(accumulate([0] + input_ids_lengths[:-1])) # 累积和，排除最后一个长度
|
||||
|
||||
# 将所有 input_ids 展平为一维数组
|
||||
flattened_input_ids = np.concatenate([enc["input_ids"][0] for enc in encoded_inputs], axis=0).astype(np.int64)
|
||||
|
||||
# Step 2: 构建 ONNX 输入
|
||||
inputs = {
|
||||
"input_ids": ort.OrtValue.ortvalue_from_numpy(flattened_input_ids),
|
||||
"offsets": ort.OrtValue.ortvalue_from_numpy(np.array(offsets, dtype=np.int64))
|
||||
}
|
||||
|
||||
# Step 3: 运行 ONNX 模型
|
||||
embeddings = session.run(None, inputs)[0] # 假设输出名为 "embeddings"
|
||||
|
||||
# Step 4: 将输出重塑为 [batch_size, seq_length, embedding_dim]
|
||||
# 注意：这里假设 ONNX 输出的形状是 [total_tokens, embedding_dim]
|
||||
# 需要根据实际序列长度重新分组
|
||||
batch_size = len(texts)
|
||||
embeddings_split = np.split(embeddings, np.cumsum(input_ids_lengths[:-1]))
|
||||
padded_embeddings = []
|
||||
for emb, seq_len in zip(embeddings_split, input_ids_lengths):
|
||||
# 对每个序列填充到 max_length
|
||||
if seq_len > max_length:
|
||||
# 如果序列长度超过 max_length，截断
|
||||
emb = emb[:max_length]
|
||||
pad_length = 0
|
||||
else:
|
||||
# 否则填充到 max_length
|
||||
pad_length = max_length - seq_len
|
||||
|
||||
# 填充到 [max_length, embedding_dim]
|
||||
padded = np.pad(emb, ((0, pad_length), (0, 0)), mode='constant')
|
||||
padded_embeddings.append(padded)
|
||||
|
||||
# 确保所有填充后的序列形状一致
|
||||
embeddings_tensor = torch.tensor(np.stack(padded_embeddings), dtype=torch.float32)
|
||||
channel_embeddings.append(embeddings_tensor)
|
||||
|
||||
# 2. 将编码结果堆叠为 [batch_size, num_channels, seq_length, embedding_dim]
|
||||
batch_tensor = torch.stack(channel_embeddings, dim=1)
|
||||
|
||||
79
filter/modelV3_12.py
Normal file
79
filter/modelV3_12.py
Normal file
@ -0,0 +1,79 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
class VideoClassifierV3_12(nn.Module):
|
||||
def __init__(self, embedding_dim=1024, hidden_dim=648):
|
||||
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))
|
||||
|
||||
# 第一个二分类器：0 vs 1/2
|
||||
self.first_classifier = nn.Sequential(
|
||||
nn.Linear(embedding_dim * self.num_channels, hidden_dim*2),
|
||||
nn.BatchNorm1d(hidden_dim*2),
|
||||
nn.Dropout(0.2),
|
||||
nn.GELU(),
|
||||
nn.Linear(hidden_dim*2, 2) # 输出为2类：0 vs 1/2
|
||||
)
|
||||
|
||||
# 第二个二分类器：1 vs 2
|
||||
self.second_classifier = nn.Sequential(
|
||||
nn.Linear(embedding_dim * self.num_channels, hidden_dim*2),
|
||||
nn.BatchNorm1d(hidden_dim*2),
|
||||
nn.Dropout(0.2),
|
||||
nn.GELU(),
|
||||
nn.Linear(hidden_dim*2, 2) # 输出为2类：1 vs 2
|
||||
)
|
||||
|
||||
# 权重初始化
|
||||
self._init_weights()
|
||||
|
||||
def _init_weights(self):
|
||||
for layer in self.first_classifier:
|
||||
if isinstance(layer, nn.Linear):
|
||||
nn.init.kaiming_normal_(layer.weight, nonlinearity='relu')
|
||||
nn.init.zeros_(layer.bias)
|
||||
|
||||
for layer in self.second_classifier:
|
||||
if isinstance(layer, nn.Linear):
|
||||
nn.init.kaiming_normal_(layer.weight, nonlinearity='relu')
|
||||
nn.init.zeros_(layer.bias)
|
||||
|
||||
def forward(self, channel_features: torch.Tensor):
|
||||
"""
|
||||
输入格式: [batch_size, num_channels, embedding_dim]
|
||||
输出格式: [batch_size, output_dim]
|
||||
"""
|
||||
# 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)
|
||||
|
||||
# 第一个二分类器：0 vs 1/2
|
||||
first_output = self.first_classifier(combined)
|
||||
first_probs = F.softmax(first_output, dim=1)
|
||||
|
||||
# 第二个二分类器：1 vs 2
|
||||
second_output = self.second_classifier(combined)
|
||||
second_probs = F.softmax(second_output, dim=1)
|
||||
|
||||
# 合并结果
|
||||
final_probs = torch.zeros(channel_features.size(0), 3).to(channel_features.device)
|
||||
final_probs[:, 0] = first_probs[:, 0] # 类别0的概率
|
||||
final_probs[:, 1] = first_probs[:, 1] * second_probs[:, 0] # 类别1的概率
|
||||
final_probs[:, 2] = first_probs[:, 1] * second_probs[:, 1] # 类别2的概率
|
||||
|
||||
return final_probs
|
||||
|
||||
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()
|
||||
@ -1,107 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
class VideoClassifierV3_9(nn.Module):
|
||||
def __init__(self, embedding_dim=1024, hidden_dim=648, 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(0.2),
|
||||
nn.GELU(),
|
||||
nn.Linear(hidden_dim*2, 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
|
||||
@ -26,10 +26,14 @@ class VideoClassifierV6_0(nn.Module):
|
||||
nn.Conv2d(128, 256, kernel_size=(3, 3), padding=1),
|
||||
nn.BatchNorm2d(256),
|
||||
nn.GELU(),
|
||||
|
||||
# 全局平均池化层
|
||||
# 输出形状为 [batch_size, 256, 1, 1]
|
||||
nn.AdaptiveAvgPool2d((1, 1))
|
||||
)
|
||||
|
||||
# 计算卷积后的特征维度
|
||||
self.feature_dim = self._get_conv_output_size(seq_length, embedding_dim)
|
||||
# 全局池化后的特征维度固定为 256
|
||||
self.feature_dim = 256
|
||||
|
||||
# 全连接层
|
||||
self.fc = nn.Sequential(
|
||||
@ -42,12 +46,6 @@ class VideoClassifierV6_0(nn.Module):
|
||||
|
||||
self._init_weights()
|
||||
|
||||
def _get_conv_output_size(self, seq_length, embedding_dim):
|
||||
# 用于计算卷积输出尺寸
|
||||
x = torch.zeros(1, self.num_channels, seq_length, embedding_dim)
|
||||
x = self.conv_layers(x)
|
||||
return x.view(1, -1).size(1)
|
||||
|
||||
def _init_weights(self):
|
||||
for module in self.modules():
|
||||
if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear):
|
||||
@ -63,8 +61,8 @@ class VideoClassifierV6_0(nn.Module):
|
||||
# CNN特征提取
|
||||
conv_features = self.conv_layers(channel_features)
|
||||
|
||||
# 展平特征
|
||||
flat_features = conv_features.view(conv_features.size(0), -1)
|
||||
# 展平特征（全局池化后形状为 [batch_size, 256, 1, 1]）
|
||||
flat_features = conv_features.view(conv_features.size(0), -1) # [batch_size, 256]
|
||||
|
||||
# 全连接层分类
|
||||
return self.fc(flat_features)
|
||||
|
||||
@ -4,16 +4,16 @@ import numpy as np
|
||||
from torch.utils.data import DataLoader
|
||||
import torch.optim as optim
|
||||
from dataset import MultiChannelDataset
|
||||
from filter.modelV6_0 import VideoClassifierV6_0, AdaptiveRecallLoss
|
||||
from filter.modelV3_12 import VideoClassifierV3_12
|
||||
from sklearn.metrics import f1_score, recall_score, precision_score, accuracy_score, classification_report
|
||||
import os
|
||||
import torch
|
||||
from torch.utils.tensorboard import SummaryWriter # 引入 TensorBoard
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
import time
|
||||
from embedding import prepare_batch_per_token
|
||||
from embedding import prepare_batch
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
# 动态生成子目录名称
|
||||
run_name = f"run_{time.strftime('%Y%m%d_%H%M')}"
|
||||
log_dir = os.path.join('./filter/runs', run_name)
|
||||
|
||||
@ -51,20 +51,16 @@ class_weights = torch.tensor(
|
||||
)
|
||||
|
||||
# 初始化模型和SentenceTransformer
|
||||
model = VideoClassifierV6_0()
|
||||
checkpoint_name = './filter/checkpoints/best_model_V6.0.pt'
|
||||
model = VideoClassifierV3_12()
|
||||
checkpoint_name = './filter/checkpoints/best_model_V3.12.pt'
|
||||
|
||||
# 模型保存路径
|
||||
os.makedirs('./filter/checkpoints', exist_ok=True)
|
||||
|
||||
# 优化器
|
||||
optimizer = optim.AdamW(model.parameters(), lr=4e-4)
|
||||
criterion = AdaptiveRecallLoss(
|
||||
class_weights=class_weights,
|
||||
alpha=0.9, # 召回率权重
|
||||
gamma=1.6, # 困难样本聚焦
|
||||
fp_penalty=0.8 # 假阳性惩罚强度
|
||||
)
|
||||
# Cross entropy loss
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
|
||||
def count_trainable_parameters(model):
|
||||
return sum(p.numel() for p in model.parameters() if p.requires_grad)
|
||||
@ -76,7 +72,7 @@ def evaluate(model, dataloader):
|
||||
|
||||
with torch.no_grad():
|
||||
for batch in dataloader:
|
||||
batch_tensor = prepare_batch_per_token(batch['texts'], max_length=1024)
|
||||
batch_tensor = prepare_batch(batch['texts'])
|
||||
logits = model(batch_tensor)
|
||||
preds = torch.argmax(logits, dim=1)
|
||||
all_preds.extend(preds.cpu().numpy())
|
||||
@ -109,7 +105,7 @@ for epoch in range(num_epochs):
|
||||
for batch_idx, batch in enumerate(train_loader):
|
||||
optimizer.zero_grad()
|
||||
|
||||
batch_tensor = prepare_batch_per_token(batch['texts'], max_length=1024)
|
||||
batch_tensor = prepare_batch(batch['texts'])
|
||||
|
||||
logits = model(batch_tensor)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user