From ab6a91dd82aa0b5a1c4f471624e49e960e8edb32 Mon Sep 17 00:00:00 2001 From: alikia2x Date: Fri, 7 Mar 2025 22:28:48 +0800 Subject: [PATCH] update: the stable filter V6.1 model --- filter/modelV6_3.py | 112 -------------------------------------------- filter/train.py | 10 ++-- 2 files changed, 5 insertions(+), 117 deletions(-) delete mode 100644 filter/modelV6_3.py diff --git a/filter/modelV6_3.py b/filter/modelV6_3.py deleted file mode 100644 index a317642..0000000 --- a/filter/modelV6_3.py +++ /dev/null @@ -1,112 +0,0 @@ -import torch -import torch.nn as nn - -class VideoClassifierV6_3(nn.Module): - def __init__(self, embedding_dim=256, hidden_dim=256, output_dim=3, num_heads=4, num_layers=2): - super().__init__() - self.num_channels = 3 - self.channel_names = ['title', 'description', 'tags'] - self.embedding_dim = embedding_dim - self.hidden_dim = hidden_dim - self.num_layers = num_layers - - # 通道独立处理模块（每个通道独立的Transformer编码器） - self.channel_processors = nn.ModuleList() - for _ in range(self.num_channels): - layers = [] - # 首先将输入维度转换为hidden_dim - layers.extend([ - nn.Linear(embedding_dim, hidden_dim), - nn.GELU(), - nn.LayerNorm(hidden_dim) - ]) - # 添加num_layers层的Transformer块 - for _ in range(num_layers): - layers.extend([ - # 自注意力层（使用hidden_dim作为embed_dim） - nn.MultiheadAttention( - embed_dim=hidden_dim, # 修改为hidden_dim - num_heads=num_heads, - dropout=0.1 - ), - nn.LayerNorm(hidden_dim), - # 前馈网络部分 - nn.Linear(hidden_dim, hidden_dim), - nn.GELU(), - nn.Linear(hidden_dim, hidden_dim), - nn.LayerNorm(hidden_dim) - ]) - self.channel_processors.append(nn.Sequential(*layers)) - - # 通道权重（可学习，Sigmoid约束） - self.channel_weights = nn.Parameter(torch.ones(self.num_channels)) - - # 全连接层（扩展维度） - self.fc = nn.Sequential( - nn.Linear(self.num_channels * hidden_dim, 1024), - nn.BatchNorm1d(1024), - nn.Dropout(0.2), - nn.GELU(), - nn.Linear(1024, 512), - nn.BatchNorm1d(512), - nn.Dropout(0.2), - nn.GELU(), - nn.Linear(512, output_dim) - ) - - self._init_weights() - - def _init_weights(self): - """权重初始化（Xavier初始化）""" - for m in self.modules(): - if isinstance(m, nn.Linear): - nn.init.xavier_uniform_(m.weight) - if m.bias is not None: - nn.init.zeros_(m.bias) - elif isinstance(m, nn.MultiheadAttention): - # 初始化MultiheadAttention的参数（输入投影和输出投影） - for name, param in m.named_parameters(): - if "in_proj" in name or "out_proj" in name: - if "weight" in name: - nn.init.xavier_uniform_(param) - elif "bias" in name: - nn.init.zeros_(param) - elif isinstance(m, nn.LayerNorm): - nn.init.ones_(m.weight) - - def forward(self, channel_features: torch.Tensor): - """ - 输入格式: [batch_size, num_channels, seq_length, embedding_dim] - 输出格式: [batch_size, output_dim] - """ - batch_size = channel_features.size(0) - processed_channels = [] - - for c in range(self.num_channels): - c_data = channel_features[:, c].permute(1, 0, 2) # 转为 [S, B, E] - - # 通道独立处理 - x = c_data - for layer in self.channel_processors[c]: - if isinstance(layer, nn.MultiheadAttention): - # 自注意力层需要显式提供键、值 - x = layer(x, x, x)[0] - else: - x = layer(x) - - # 转换回 [B, S, hidden_dim] 并全局平均池化 - x = x.permute(1, 0, 2) - pooled = x.mean(dim=1) - processed_channels.append(pooled) - - # 堆叠通道特征 - processed_channels = torch.stack(processed_channels, dim=1) - - # 应用通道权重（Sigmoid约束） - weights = torch.sigmoid(self.channel_weights).view(1, -1, 1) - weighted_features = processed_channels * weights - - # 拼接所有通道特征 - combined = weighted_features.view(batch_size, -1) - - return self.fc(combined) \ No newline at end of file diff --git a/filter/train.py b/filter/train.py index 3a65346..5295fde 100644 --- a/filter/train.py +++ b/filter/train.py @@ -4,7 +4,7 @@ import numpy as np from torch.utils.data import DataLoader import torch.optim as optim from dataset import MultiChannelDataset -from filter.modelV6_3 import VideoClassifierV6_3 +from filter.modelV6_1 import VideoClassifierV6_1 from sklearn.metrics import f1_score, recall_score, precision_score, accuracy_score, classification_report import os import torch @@ -61,8 +61,8 @@ class_weights = torch.tensor( device=device ) -model = VideoClassifierV6_3().to(device) -checkpoint_name = './filter/checkpoints/best_model_V6.3-II.pt' +model = VideoClassifierV6_1().to(device) +checkpoint_name = './filter/checkpoints/best_model_V6.1.pt' # 初始化tokenizer和embedding模型 tokenizer = AutoTokenizer.from_pretrained("alikia2x/jina-embedding-v3-m2v-1024") @@ -74,9 +74,9 @@ os.makedirs('./filter/checkpoints', exist_ok=True) # 优化器 eval_interval = 20 num_epochs = 20 -total_steps = samples_count * num_epochs / batch_size +total_steps = samples_count * num_epochs / train_loader.batch_size warmup_rate = 0.1 -optimizer = optim.AdamW(model.parameters(), lr=1e-5, weight_decay=1e-3) +optimizer = optim.AdamW(model.parameters(), lr=5e-5, weight_decay=1e-5) cosine_annealing_scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps - int(total_steps * warmup_rate)) warmup_scheduler = optim.lr_scheduler.LinearLR(optimizer, start_factor=0.4, end_factor=1.0, total_iters=int(total_steps * warmup_rate)) scheduler = optim.lr_scheduler.SequentialLR(optimizer, schedulers=[warmup_scheduler, cosine_annealing_scheduler], milestones=[int(total_steps * warmup_rate)])