add: the V6.1 filter model
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@ -31,10 +31,8 @@ def prepare_batch(batch_data, device="cpu"):
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return batch_tensor
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import onnxruntime as ort
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from transformers import AutoTokenizer
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from itertools import accumulate
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def prepare_batch_per_token(batch_data, max_length=1024):
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def prepare_batch_per_token(session, tokenizer, batch_data, max_length=1024):
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"""
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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]。
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@ -42,69 +40,33 @@ def prepare_batch_per_token(batch_data, max_length=1024):
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batch_data (dict): 输入的 batch 数据，格式为 {
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"title": [text1, text2, ...],
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"description": [text1, text2, ...],
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"tags": [text1, text2, ...],
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"author_info": [text1, text2, ...]
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"tags": [text1, text2, ...]
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}
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max_length (int): 最大序列长度。
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返回:
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torch.Tensor: 形状为 [batch_size, num_channels, seq_length, embedding_dim] 的张量。
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torch.Tensor: 形状为 [batch_size, num_channels, max_length, embedding_dim] 的张量。
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"""
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# 初始化 tokenizer 和 ONNX 模型
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tokenizer = AutoTokenizer.from_pretrained("alikia2x/jina-embedding-v3-m2v-1024")
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session = ort.InferenceSession("./model/embedding_256/onnx/model.onnx")
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# 1. 对每个通道的文本分别编码
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channel_embeddings = []
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for channel in ["title", "description", "tags", "author_info"]:
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texts = batch_data[channel] # 获取当前通道的文本列表
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batch_size = len(batch_data["title"])
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batch_tensor = torch.zeros(batch_size, 3, max_length, 256)
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for i in range(batch_size):
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channel_embeddings = torch.zeros((3, 1024, 256))
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for j, channel in enumerate(["title", "description", "tags"]):
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# 获取当前通道的文本
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text = batch_data[channel][i]
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encoded_inputs = tokenizer(text, truncation=True, max_length=max_length, return_tensors='np')
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# Step 1: 生成 input_ids 和 offsets
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# 对每个文本单独编码，保留原始 token 长度
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encoded_inputs = [tokenizer(text, truncation=True, max_length=max_length, return_tensors='np') for text in texts]
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# embeddings: [max_length, embedding_dim]
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embeddings = torch.zeros((1024, 256))
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for idx, token in enumerate(encoded_inputs['input_ids'][0]):
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inputs = {
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"input_ids": ort.OrtValue.ortvalue_from_numpy(np.array([token])),
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"offsets": ort.OrtValue.ortvalue_from_numpy(np.array([0], dtype=np.int64))
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}
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output = session.run(None, inputs)[0]
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embeddings[idx] = torch.from_numpy(output)
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channel_embeddings[j] = embeddings
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batch_tensor[i] = channel_embeddings
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# 提取每个文本的 input_ids 长度（考虑实际的 token 数量）
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input_ids_lengths = [len(enc["input_ids"][0]) for enc in encoded_inputs]
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# 生成 offsets: [0, len1, len1+len2, ...]
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offsets = list(accumulate([0] + input_ids_lengths[:-1])) # 累积和，排除最后一个长度
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# 将所有 input_ids 展平为一维数组
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flattened_input_ids = np.concatenate([enc["input_ids"][0] for enc in encoded_inputs], axis=0).astype(np.int64)
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# Step 2: 构建 ONNX 输入
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inputs = {
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"input_ids": ort.OrtValue.ortvalue_from_numpy(flattened_input_ids),
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"offsets": ort.OrtValue.ortvalue_from_numpy(np.array(offsets, dtype=np.int64))
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}
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# Step 3: 运行 ONNX 模型
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embeddings = session.run(None, inputs)[0] # 假设输出名为 "embeddings"
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# Step 4: 将输出重塑为 [batch_size, seq_length, embedding_dim]
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# 注意：这里假设 ONNX 输出的形状是 [total_tokens, embedding_dim]
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# 需要根据实际序列长度重新分组
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batch_size = len(texts)
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embeddings_split = np.split(embeddings, np.cumsum(input_ids_lengths[:-1]))
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padded_embeddings = []
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for emb, seq_len in zip(embeddings_split, input_ids_lengths):
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# 对每个序列填充到 max_length
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if seq_len > max_length:
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# 如果序列长度超过 max_length，截断
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emb = emb[:max_length]
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pad_length = 0
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else:
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# 否则填充到 max_length
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pad_length = max_length - seq_len
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# 填充到 [max_length, embedding_dim]
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padded = np.pad(emb, ((0, pad_length), (0, 0)), mode='constant')
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padded_embeddings.append(padded)
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# 确保所有填充后的序列形状一致
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embeddings_tensor = torch.tensor(np.stack(padded_embeddings), dtype=torch.float32)
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channel_embeddings.append(embeddings_tensor)
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# 2. 将编码结果堆叠为 [batch_size, num_channels, seq_length, embedding_dim]
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batch_tensor = torch.stack(channel_embeddings, dim=1)
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return batch_tensor
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@ -5,8 +5,8 @@ import torch.nn.functional as F
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class VideoClassifierV6_0(nn.Module):
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def __init__(self, embedding_dim=256, seq_length=1024, hidden_dim=512, output_dim=3):
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super().__init__()
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self.num_channels = 4
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self.channel_names = ['title', 'description', 'tags', 'author_info']
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self.num_channels = 3
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self.channel_names = ['title', 'description', 'tags']
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# CNN特征提取层
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self.conv_layers = nn.Sequential(
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@ -66,28 +66,3 @@ class VideoClassifierV6_0(nn.Module):
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# 全连接层分类
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return self.fc(flat_features)
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# 损失函数保持不变
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class AdaptiveRecallLoss(nn.Module):
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def __init__(self, class_weights, alpha=0.8, gamma=2.0, fp_penalty=0.5):
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super().__init__()
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self.class_weights = class_weights
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self.alpha = alpha
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self.gamma = gamma
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self.fp_penalty = fp_penalty
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def forward(self, logits, targets):
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ce_loss = F.cross_entropy(logits, targets, weight=self.class_weights, reduction='none')
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pt = torch.exp(-ce_loss)
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focal_loss = ((1 - pt) ** self.gamma) * ce_loss
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class_mask = F.one_hot(targets, num_classes=len(self.class_weights))
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class_weights = (self.alpha + (1 - self.alpha) * pt.unsqueeze(-1)) * class_mask
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recall_loss = (class_weights * focal_loss.unsqueeze(-1)).sum(dim=1)
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probs = F.softmax(logits, dim=1)
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fp_mask = (targets != 0) & (torch.argmax(logits, dim=1) == 0)
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fp_loss = self.fp_penalty * probs[:, 0][fp_mask].pow(2).sum()
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total_loss = recall_loss.mean() + fp_loss / len(targets)
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return total_loss
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108
filter/modelV6_1.py
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108
filter/modelV6_1.py
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@ -0,0 +1,108 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class VideoClassifierV6_1(nn.Module):
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def __init__(self, embedding_dim=256, seq_length=1024, hidden_dim=256, output_dim=3, num_heads=4):
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super().__init__()
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self.num_channels = 3
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self.channel_names = ['title', 'description', 'tags']
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self.embedding_dim = embedding_dim
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self.hidden_dim = hidden_dim # 每个通道处理后的特征维度
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# 通道独立处理模块（每个通道独立的Transformer编码器）
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self.channel_processors = nn.ModuleList()
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for _ in range(self.num_channels):
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self.channel_processors.append(
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nn.Sequential(
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# 自注意力层
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nn.MultiheadAttention(
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embed_dim=embedding_dim,
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num_heads=num_heads,
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dropout=0.1
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),
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# 层归一化和前馈网络
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nn.LayerNorm(embedding_dim),
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nn.Linear(embedding_dim, hidden_dim),
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nn.GELU(),
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nn.Linear(hidden_dim, hidden_dim),
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nn.LayerNorm(hidden_dim)
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)
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)
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# 通道权重（可学习，Sigmoid约束）
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self.channel_weights = nn.Parameter(torch.ones(self.num_channels))
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# 全连接层（扩展维度）
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self.fc = nn.Sequential(
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nn.Linear(self.num_channels * hidden_dim, 1024), # 拼接后的特征维度
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nn.BatchNorm1d(1024),
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nn.Dropout(0.2),
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nn.GELU(),
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nn.Linear(1024, 512),
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nn.BatchNorm1d(512),
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nn.Dropout(0.2),
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nn.GELU(),
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nn.Linear(512, output_dim)
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)
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self._init_weights()
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def _init_weights(self):
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"""权重初始化（Xavier初始化）"""
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for m in self.modules():
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if isinstance(m, nn.Linear):
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nn.init.xavier_uniform_(m.weight)
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if m.bias is not None:
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nn.init.zeros_(m.bias)
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elif isinstance(m, nn.MultiheadAttention):
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# 初始化MultiheadAttention的参数（输入投影和输出投影）
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for name, param in m.named_parameters():
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if "in_proj" in name or "out_proj" in name:
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if "weight" in name:
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nn.init.xavier_uniform_(param)
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elif "bias" in name:
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nn.init.zeros_(param)
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elif isinstance(m, nn.LayerNorm):
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nn.init.ones_(m.weight)
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def forward(self, channel_features: torch.Tensor):
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"""
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输入格式: [batch_size, num_channels, seq_length, embedding_dim]
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输出格式: [batch_size, output_dim]
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"""
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batch_size = channel_features.size(0)
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processed_channels = []
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for c in range(self.num_channels):
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# 提取当前通道的特征 [B, S, E]
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c_data = channel_features[:, c]
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# 转置为 [S, B, E] 以适配MultiheadAttention
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c_data = c_data.permute(1, 0, 2)
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# 通道独立处理
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x = c_data
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for layer in self.channel_processors[c]:
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if isinstance(layer, nn.MultiheadAttention):
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# 自注意力层需要显式提供键、值
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x = layer(x, x, x)[0]
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else:
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x = layer(x)
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# 转回 [B, S, hidden_dim]
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x = x.permute(1, 0, 2)
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# 全局池化（序列维度平均）
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pooled = x.mean(dim=1) # [B, hidden_dim]
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processed_channels.append(pooled)
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# 堆叠通道特征 [B, C, hidden_dim]
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processed_channels = torch.stack(processed_channels, dim=1)
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# 应用通道权重（Sigmoid约束）
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weights = torch.sigmoid(self.channel_weights).unsqueeze(0).unsqueeze(-1) # [1, C, 1]
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weighted_features = processed_channels * weights # [B, C, hidden_dim]
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# 拼接所有通道特征
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combined = weighted_features.view(batch_size, -1) # [B, C*hidden_dim]
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# 全连接层分类
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return self.fc(combined)
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@ -4,14 +4,16 @@ import numpy as np
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from torch.utils.data import DataLoader
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import torch.optim as optim
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from dataset import MultiChannelDataset
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from filter.modelV3_15 import AdaptiveRecallLoss, VideoClassifierV3_15
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from filter.modelV6_1 import VideoClassifierV6_1
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from sklearn.metrics import f1_score, recall_score, precision_score, accuracy_score, classification_report
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import os
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import torch
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from torch.utils.tensorboard import SummaryWriter
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import time
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from embedding import prepare_batch
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import torch.nn as nn
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from embedding import prepare_batch_per_token
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import onnxruntime as ort
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from transformers import AutoTokenizer
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from torch import nn
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run_name = f"run_{time.strftime('%Y%m%d_%H%M')}"
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@ -24,6 +26,8 @@ writer = SummaryWriter(log_dir=log_dir)
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train_dataset = MultiChannelDataset('./data/filter/labeled_data.jsonl', mode='train')
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eval_dataset = MultiChannelDataset('./data/filter/labeled_data.jsonl', mode='eval')
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samples_count = len(train_dataset)
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# 加载test数据集
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test_file = './data/filter/test.jsonl'
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if not os.path.exists(test_file):
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@ -50,21 +54,26 @@ class_weights = torch.tensor(
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device='cpu'
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)
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# 初始化模型和SentenceTransformer
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model = VideoClassifierV3_15()
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checkpoint_name = './filter/checkpoints/best_model_V3.17.pt'
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model = VideoClassifierV6_1()
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checkpoint_name = './filter/checkpoints/best_model_V6.2-test2.pt'
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# 初始化tokenizer和embedding模型
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tokenizer = AutoTokenizer.from_pretrained("alikia2x/jina-embedding-v3-m2v-1024")
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session = ort.InferenceSession("./model/embedding_256/onnx/model.onnx")
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# 模型保存路径
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os.makedirs('./filter/checkpoints', exist_ok=True)
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# 优化器
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optimizer = optim.AdamW(model.parameters(), lr=4e-4)
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criterion = AdaptiveRecallLoss(
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class_weights=class_weights,
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alpha=0.9, # 召回率权重
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gamma=1.6, # 困难样本聚焦
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fp_penalty=0.8 # 假阳性惩罚强度
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)
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eval_interval = 20
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num_epochs = 20
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total_steps = samples_count * num_epochs / train_loader.batch_size
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warmup_rate = 0.1
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optimizer = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-5)
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cosine_annealing_scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps - int(total_steps * warmup_rate))
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warmup_scheduler = optim.lr_scheduler.LinearLR(optimizer, start_factor=0.1, end_factor=1.0, total_iters=int(total_steps * warmup_rate))
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scheduler = optim.lr_scheduler.SequentialLR(optimizer, schedulers=[warmup_scheduler, cosine_annealing_scheduler], milestones=[int(total_steps * warmup_rate)])
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criterion = nn.CrossEntropyLoss(weight=class_weights)
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def count_trainable_parameters(model):
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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@ -76,7 +85,7 @@ def evaluate(model, dataloader):
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with torch.no_grad():
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for batch in dataloader:
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batch_tensor = prepare_batch(batch['texts'])
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batch_tensor = prepare_batch_per_token(session, tokenizer, batch['texts'])
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logits = model(batch_tensor)
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preds = torch.argmax(logits, dim=1)
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all_preds.extend(preds.cpu().numpy())
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@ -98,8 +107,6 @@ print(f"Trainable parameters: {count_trainable_parameters(model)}")
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# 训练循环
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best_f1 = 0
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step = 0
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eval_interval = 20
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num_epochs = 8
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for epoch in range(num_epochs):
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model.train()
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@ -109,7 +116,8 @@ for epoch in range(num_epochs):
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for batch_idx, batch in enumerate(train_loader):
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optimizer.zero_grad()
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batch_tensor = prepare_batch(batch['texts'])
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batch_tensor = prepare_batch_per_token(session, tokenizer, batch['texts'])
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logits = model(batch_tensor)
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@ -142,7 +150,8 @@ for epoch in range(num_epochs):
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best_f1 = eval_f1
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torch.save(model.state_dict(), checkpoint_name)
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print(" Saved best model")
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print("Channel weights: ", model.get_channel_weights())
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scheduler.step()
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writer.add_scalar('Train/LR', scheduler.get_last_lr()[0], step)
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# 记录每个 epoch 的平均训练损失
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avg_epoch_loss = epoch_loss / len(train_loader)
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