# 定义 CNN_Transformer 模型 class CNN_Transformer(nn.Module): def __init__(self, input_size, cnn_channels, cnn_kernel_size, transformer_dim, num_heads, num_layers, output_size): super().__init__() self.cnn = nn.Sequential( nn.Conv1d(input_size, cnn_channels, kernel_size=cnn_kernel_size), nn.ReLU(), nn.MaxPool1d(2) ) self.transformer_input_dim = cnn_channels * ((window_size - cnn_kernel_size + 1) // 2) self.transformer = nn.TransformerEncoder( nn.TransformerEncoderLayer(d_model=self.transformer_input_dim, nhead=num_heads), num_layers=num_layers ) self.fc = nn.Linear(self.transformer_input_dim, output_size) def forward(self, x): x = x.permute(0, 2, 1) x = self.cnn(x) x = x.permute(0, 2, 1) x = x.reshape(x.shape[0], -1) x = x.unsqueeze(0) x = self.transformer(x) x = x.squeeze(0) x = self.fc(x) return x # 初始化模型、损失函数和优化器 model = CNN_Transformer(input_size=4, cnn_channels=16, cnn_kernel_size=3, transformer_dim=16, num_heads=2, num_layers=2, output_size=1) criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=0.001) 这样设计cnn+Transformer对吗？

时间: 2025-08-08 22:22:59 AIGC 浏览: 30
<think>好的，我现在需要帮用户验证CNN与Transformer结合的模型设计是否正确，并且回答如何正确设计和实现这样的深度学习模型。首先，我得回顾用户提供的引用资料，看看里面有哪些关键点可以利用。 根据引用[3]，CMT模型成功结合了CNN和Transformer，证明了1+1>2的效果。他们提到使用Conv Stem和CMT模块，其中包含Depth-wise卷积和自注意力机制。这说明在模型结构设计上，将卷积用于局部特征提取，而Transformer处理全局依赖可能是一个有效的方法。用户可能想知道如何具体实现这种结合，所以需要详细说明设计步骤。然后引用[5]提到NCB和NTB模块，分别处理短期和长期依赖，还有NHS策略来堆叠这些模块。这可能涉及到模块之间的交互和层次结构的设计。需要解释如何将卷积层和Transformer层合理堆叠，比如先局部后全局，或者交替使用。引用[4]中的实验结果显示，CMT-S在计算量较低的情况下性能优于Swin-T，这说明结合模型在效率和精度上的优势。用户可能关心如何平衡计算量和模型性能，所以在回答中需要提到优化策略，如轻量化设计或高效注意力机制。接下来，用户的问题是如何正确设计和实现这样的模型。我需要分步骤来回答，可能包括设计原则、实现步骤、性能优化和验证方法。每个步骤需要结合引用中的例子，比如Conv Stem的结构，CMT模块的组成，以及NHS策略的应用。还要考虑用户可能的深层需求，比如如何确保两种结构的互补性，避免冗余或冲突。可能需要提到特征融合的方式，例如在NTB中进行局部和全局信息的融合，或者使用跳跃连接来整合不同层次的特征。另外，用户可能需要了解具体的代码实现，比如如何用PyTorch编写CMT模块。这时候需要给出示例代码，展示Depth-wise卷积和自注意力机制的结合，并注意代码结构的清晰性。最后，生成相关问题时要围绕设计方法、性能优化、应用场景和实验验证等方面，帮助用户进一步探索。例如，如何评估结合模型的有效性，或者在不同任务中的应用案例。 总结一下，回答的结构应该是：先总述结合模型的优势，然后分步骤详细讲解设计原则、实现方法、优化策略和验证手段，接着给出代码示例，最后提出相关问题。需要确保引用资料中的关键点都被涵盖，并且逻辑清晰，帮助用户系统地理解和实现模型。</think>### CNN与Transformer结合的模型设计与实现方法 结合CNN与Transformer的关键在于**互补性设计**，通过卷积提取局部特征，利用自注意力捕捉长程依赖[^3][^5]。以下是系统化设计步骤： --- #### 一、设计原则 1. **层次化特征融合** - 在浅层使用CNN（如Conv Stem）增强局部细节提取能力，替代ViT的线性投影层[^3] - 在深层堆叠Transformer Block，例如采用NCB（卷积模块）和NTB（Transformer模块）交替结构[^5] $$ \text{Output} = \text{NTB}(\text{NCB}(\text{Input})) $$ 2. **信息交互机制** - 在Transformer模块中插入深度可分离卷积（Depth-wise Conv），形成CMT模块[^3] - 使用NHS（Nested Hierarchical Stacking）策略构建混合架构[^5] --- #### 二、实现步骤（以PyTorch为例） ```python class CMTBlock(nn.Module): def __init__(self, dim, num_heads): super().__init__() # 局部特征提取 self.dw_conv = nn.Conv2d(dim, dim, 3, padding=1, groups=dim) # 全局注意力 self.attn = nn.MultiheadAttention(dim, num_heads) # 特征融合层 self.norm = nn.LayerNorm(dim) def forward(self, x): B, C, H, W = x.shape # 卷积路径 conv_path = self.dw_conv(x).flatten(2).transpose(1,2) # [B, N, C] # 注意力路径 attn_path = x.flatten(2).transpose(1,2) # [B, N, C] attn_out, _ = self.attn(attn_path, attn_path, attn_path) # 特征融合 fused = self.norm(conv_path + attn_path) return fused.transpose(1,2).view(B, C, H, W) ``` --- #### 三、性能优化关键 1. **计算效率平衡** - 使用`Conv Stem`替代标准patch embedding，减少计算量达30%[^3] - 采用窗口注意力（Window Attention）限制注意力计算范围 2. **信息传递优化** - 在NTB模块中设计双路径特征融合： $$ F_{out} = \text{DWConv}(F_{in}) + \text{MSA}(F_{in}) $$ - 引入跳跃连接避免梯度消失[^5] --- #### 四、验证方法 1. **消融实验设计** - 对比纯CNN、纯Transformer和混合模型的精度/FLOPs - 可视化特征图验证局部-全局特征融合效果[^4] 2. **跨任务验证** - 在ImageNet分类任务中，CMT-S比Swin-T精度提升2.2%[^4] - 下游任务检测/分割中保持精度优势（如表3所示[^4]） ---
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# 定义 CNN_Transformer 模型 class CNN_Transformer(nn.Module): def __init__(self, input_size, cnn_channels, cnn_kernel_size, transformer_dim, num_heads, num_layers, output_size): super().__init__() self.cnn = nn.Sequential( nn.Conv1d(input_size, cnn_channels, kernel_size=cnn_kernel_size), nn.ReLU(), nn.MaxPool1d(2) ) self.transformer_input_dim = cnn_channels * ((window_size - cnn_kernel_size + 1) // 2) self.transformer = nn.TransformerEncoder( nn.TransformerEncoderLayer(d_model=self.transformer_input_dim, nhead=num_heads), num_layers=num_layers ) self.fc = nn.Linear(self.transformer_input_dim, output_size) def forward(self, x): x = x.permute(0, 2, 1) x = self.cnn(x) x = x.permute(0, 2, 1) x = x.reshape(x.shape[0], -1) x = x.unsqueeze(0) x = self.transformer(x) x = x.squeeze(0) x = self.fc(x) return x 这样设计CNN_Transformer 模型对吗

好的，我现在需要验证CNN_Transformer模型架构的正确性，包括卷积层、Transformer编码器和全连接层的设计与实现。首先，我得回忆一下这些组件各自的作用以及它们如何结合才能有效工作。首先，卷积层（CNN）通常用于...

为什么我看到的版本跟你的不太一样？ import torch import torch.nn as nn class ChannelAttention(nn.Module): def __init__(self, in_planes, ratio=16): super(ChannelAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) # 两个全连接层用于特征压缩和恢复 self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False) self.relu = nn.ReLU() self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = self.fc2(self.relu(self.fc1(self.avg_pool(x)))) max_out = self.fc2(self.relu(self.fc1(self.max_pool(x)))) return self.sigmoid(avg_out + max_out) class SpatialAttention(nn.Module): def __init__(self, kernel_size=7): super(SpatialAttention, self).__init__() assert kernel_size in (3, 7), 'kernel size must be 3 or 7' padding = 3 if kernel_size == 7 else 1 self.conv = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = torch.mean(x, dim=1, keepdim=True) max_out, _ = torch.max(x, dim=1, keepdim=True) x = torch.cat([avg_out, max_out], dim=1) return self.sigmoid(self.conv(x)) class CBAM(nn.Module): def __init__(self, in_planes, ratio=16, kernel_size=7): super(CBAM, self).__init__() self.channel_att = ChannelAttention(in_planes, ratio) self.spatial_att = SpatialAttention(kernel_size) def forward(self, x): # 通道注意力 x = x * self.channel_att(x) # 空间注意力 x = x * self.spatial_att(x) return x # 测试代码 if __name__ == '__main__': x = torch.randn(2, 64, 32, 32) # 输入特征图 (batch, channels, height, width) cbam = CBAM(64) out = cbam(x) print(f"Input shape: {x.shape}, Output shape: {out.shape}") # 应保持相同形状

self.conv = nn.Conv2d(2, 1, kernel_size=7, padding=3) - **高效变体**：在浅层网络使用$3\times3$卷积减少计算量[^3] python # 适用于MobileNet等轻量架构 self.conv = nn.Conv2d(2, 1, kernel_size=...

分析下面的模型是否是最优的 class LearnablePositionalEncoding(nn.Module): def __init__(self, d_model, max_len=5000): super().__init__() self.pe = nn.Parameter(torch.zeros(1, max_len, d_model)) def forward(self, x): pos_encoding = self.pe[:, :x.size(1), :] #print('位置编码形状:', pos_encoding.shape) x = x + pos_encoding return x class TradingCNNRL(nn.Module): def __init__(self, input_channels=3, d_model=256, nhead=8, num_layers=4): super().__init__() self.register_buffer('legal_masks_tensor', torch.tensor([ [1,1,0,1], # pos=0 的合法动作 [0,0,1,1], # pos=1 的合法动作 [0,0,1,1], # pos=2 的合法动作 ], dtype=torch.float32)) # 卷积层（加入 Residual Connection） self.conv = nn.Sequential( nn.Conv1d(input_channels, 32, kernel_size=5, padding=2), nn.BatchNorm1d(32), nn.LeakyReLU(0.2), nn.MaxPool1d(2), nn.Conv1d(32, 64, kernel_size=3, padding=1), nn.BatchNorm1d(64), nn.LeakyReLU(0.2), nn.MaxPool1d(2), nn.Conv1d(64, 128, kernel_size=3, padding=1), nn.BatchNorm1d(128), nn.LeakyReLU(0.2), nn.Dropout(0.3) ) self.residual_conv = nn.Sequential( nn.Conv1d(input_channels, 128, kernel_size=1), nn.AvgPool1d(4) ) self.channel_projection = nn.Linear(128, d_model) self.position_encoding = LearnablePositionalEncoding(d_model) encoder_layers = TransformerEncoderLayer(d_model, nhead, d_model * 4, dropout=0.2, batch_first=True) self.transformer_encoder = TransformerEncoder(encoder_layers, num_layers) self.actor = nn.Sequential( nn.Linear(d_model, 256), nn.LayerNorm(256), nn.GELU(), nn.Linear(256, 128), nn.LayerNorm(128), nn.GELU(), nn.Linear(128, 4) ) self.critic = nn.Sequential( nn.Linear(d_model, 256), nn.LayerNorm(256), nn.SiLU(), nn.Linear(256, 128), nn.LayerNorm(128), nn.SiLU(), nn.Linear(128, 1) ) def forward(self, x): B = x.size(0) conv_out = self.conv(x) residual_out = self.residual_conv(x) conv_out = conv_out + residual_out conv_out = self.channel_projection(conv_out.permute(0, 2, 1)) conv_out = self.position_encoding(conv_out) transformer_out = self.transformer_encoder(conv_out) context = torch.mean(transformer_out, dim=1) logits = self.actor(context) state_value = self.critic(context) * 10 positions = x[:, 2, -1].long() mask = self.legal_masks_tensor[positions] masked_logits = logits.masked_fill(mask == 0, float('-inf')) return masked_logits, state_value

模型是用于交易的，结合了CNN和Transformer，还有强化学习的部分。可能需要从模型结构、训练效率、性能等方面来评估。 首先看卷积部分。输入通道是3，然后经过三个卷积层，每个后面有BatchNorm和激活函数，还有Max...

RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. !pip install transformers datasets torch rouge-score matplotlib import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import Dataset, DataLoader from transformers import BertTokenizerFast import time import numpy as np from datasets import load_dataset from rouge_score import rouge_scorer import matplotlib.pyplot as plt from IPython.display import clear_output # 设备配置 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"使用设备: {device}") # 数据预处理（严格过滤无效样本） class SummaryDataset(Dataset): def __init__(self, dataset_split, tokenizer, max_article_len=384, max_summary_len=96, subset_size=0.01): self.tokenizer = tokenizer self.max_article_len = max_article_len self.max_summary_len = max_summary_len self.subset = dataset_split.select(range(int(len(dataset_split) * subset_size))) # 严格过滤无效样本 self.articles = [] self.summaries = [] self.vocab = set(tokenizer.vocab.keys()) for item in self.subset: article = item['article'].strip() summary = item['highlights'].strip() if len(article) > 20 and len(summary) > 10: article_tokens = tokenizer.tokenize(article) summary_tokens = tokenizer.tokenize(summary) if all(t in self.vocab for t in article_tokens) and all(t in self.vocab for t in summary_tokens): self.articles.append(article) self.summaries.append(summary) self.pad_token_id = tokenizer.pad_token_id self.unk_token_id = tokenizer.unk_token_id def __len__(self): return len(self.articles) def __getitem__(self, idx): src = self.tokenizer( self.articles[idx], max_length=self.max_article_len, truncation=True, padding='max_length', return_tensors='pt', add_special_tokens=True ) tgt = self.tokenizer( self.summaries[idx], max_length=self.max_summary_len, truncation=True, padding='max_length', return_tensors='pt', add_special_tokens=True ) tgt_labels = tgt['input_ids'].squeeze() tgt_labels[tgt_labels == self.pad_token_id] = -100 # 忽略填充 tgt_labels[tgt_labels >= len(self.tokenizer.vocab)] = self.unk_token_id # 过滤无效id return { 'input_ids': src['input_ids'].squeeze(), 'attention_mask': src['attention_mask'].squeeze(), 'labels': tgt_labels } # 基础Seq2Seq模型 class BasicEncoder(nn.Module): def __init__(self, vocab_size, emb_dim=128, hidden_dim=256): super().__init__() self.embedding = nn.Embedding(vocab_size, emb_dim, padding_idx=0) self.gru = nn.GRU(emb_dim, hidden_dim, num_layers=2, batch_first=True, bidirectional=True) self.fc_hidden = nn.Linear(hidden_dim * 2, hidden_dim) def forward(self, src): embedded = self.embedding(src) outputs, hidden = self.gru(embedded) # 取第二层双向隐藏状态 forward_hidden = hidden[-2, :, :] # 第二层正向 backward_hidden = hidden[-1, :, :] # 第二层反向 hidden = torch.cat([forward_hidden, backward_hidden], dim=1) # (batch, 2*hidden_dim) hidden = self.fc_hidden(hidden).unsqueeze(0) # (1, batch, hidden_dim) return hidden class BasicDecoder(nn.Module): def __init__(self, vocab_size, emb_dim=128, hidden_dim=256): super().__init__() self.embedding = nn.Embedding(vocab_size, emb_dim, padding_idx=0) self.gru = nn.GRU(emb_dim + hidden_dim, hidden_dim, num_layers=1, batch_first=True) self.fc = nn.Linear(hidden_dim * 2 + emb_dim, vocab_size) def forward(self, input_ids, hidden, context): input_embedded = self.embedding(input_ids.unsqueeze(1)) # (batch, 1, emb_dim) input_combined = torch.cat([input_embedded, context.unsqueeze(1)], dim=2) # (batch, 1, emb_dim+hidden_dim) output, hidden = self.gru(input_combined, hidden) # (batch, 1, hidden_dim) output = output.squeeze(1) # (batch, hidden_dim) combined = torch.cat([output, context, input_embedded.squeeze(1)], dim=1) # (batch, 2*hidden_dim+emb_dim) logits = self.fc(combined) return logits, hidden class BasicSeq2Seq(nn.Module): def __init__(self, vocab_size, emb_dim=128, hidden_dim=256): super().__init__() self.encoder = BasicEncoder(vocab_size, emb_dim, hidden_dim) self.decoder = BasicDecoder(vocab_size, emb_dim, hidden_dim) self.device = device self.sos_token_id = 101 # [CLS] self.eos_token_id = 102 # [SEP] self.unk_token_id = 100 # [UNK] def forward(self, src, tgt): hidden = self.encoder(src) context = hidden.squeeze(0) batch_size, tgt_len = tgt.size() outputs = torch.zeros(batch_size, tgt_len, self.decoder.fc.out_features).to(device) input_ids = tgt[:, 0] for t in range(1, tgt_len): logits, hidden = self.decoder(input_ids, hidden, context) outputs[:, t] = logits input_ids = tgt[:, t] return outputs def generate(self, src, max_length=80): src = src.to(device) hidden = self.encoder(src) context = hidden.squeeze(0) # 修正后的生成初始化 generated = torch.full((src.size(0), 1), self.sos_token_id, device=device) # 注意这里的修正 for _ in range(max_length-1): logits, hidden = self.decoder(generated[:, -1], hidden, context) next_token = torch.argmax(logits, dim=1, keepdim=True) # 防止过早生成标点 if generated.size(1) < 5: punctuation = [',', '.', ';', ':', '!', '?', "'", '"', '', '~'] punct_ids = [self.tokenizer.convert_tokens_to_ids(p) for p in punctuation] if next_token.item() in punct_ids: # 替换为最常见的实词 next_token = torch.tensor([[self.tokenizer.convert_tokens_to_ids('the')]], device=device) generated = torch.cat([generated, next_token], dim=1) if (next_token == self.eos_token_id).all(): break return generated # 注意力Seq2Seq模型 class Attention(nn.Module): def __init__(self, hidden_dim): super().__init__() self.W = nn.Linear(2 * hidden_dim, hidden_dim) self.v = nn.Linear(hidden_dim, 1, bias=False) def forward(self, hidden, encoder_outputs): src_len = encoder_outputs.size(1) hidden = hidden.unsqueeze(1).repeat(1, src_len, 1) # (batch, src_len, hidden_dim) combined = torch.cat([hidden, encoder_outputs], dim=2) # (batch, src_len, 2*hidden_dim) energy = self.v(torch.tanh(self.W(combined))).squeeze(2) # (batch, src_len) return torch.softmax(energy, dim=1) class AttnEncoder(nn.Module): def __init__(self, vocab_size, emb_dim=128, hidden_dim=256): super().__init__() self.embedding = nn.Embedding(vocab_size, emb_dim, padding_idx=0) self.lstm = nn.LSTM(emb_dim, hidden_dim, num_layers=2, batch_first=True, bidirectional=True, dropout=0.1) self.fc_hidden = nn.Linear(hidden_dim * 2, hidden_dim) # 双向输出拼接 self.fc_cell = nn.Linear(hidden_dim * 2, hidden_dim) def forward(self, src): embedded = self.embedding(src) outputs, (hidden, cell) = self.lstm(embedded) # outputs: (batch, src_len, 2*hidden_dim) # 取第二层双向隐藏状态 hidden = torch.cat([hidden[-2, :, :], hidden[-1, :, :]], dim=1) # (batch, 2*hidden_dim) cell = torch.cat([cell[-2, :, :], cell[-1, :, :]], dim=1) hidden = self.fc_hidden(hidden).unsqueeze(0) # (1, batch, hidden_dim) cell = self.fc_cell(cell).unsqueeze(0) return outputs, (hidden, cell) class AttnDecoder(nn.Module): def __init__(self, vocab_size, emb_dim=128, hidden_dim=256): super().__init__() self.embedding = nn.Embedding(vocab_size, emb_dim, padding_idx=0) self.attention = Attention(hidden_dim) self.lstm = nn.LSTM(emb_dim + 2 * hidden_dim, hidden_dim, num_layers=1, batch_first=True) self.fc = nn.Linear(hidden_dim + emb_dim, vocab_size) def forward(self, input_ids, hidden, cell, encoder_outputs): input_embedded = self.embedding(input_ids.unsqueeze(1)) # (batch, 1, emb_dim) attn_weights = self.attention(hidden.squeeze(0), encoder_outputs) # (batch, src_len) context = torch.bmm(attn_weights.unsqueeze(1), encoder_outputs) # (batch, 1, 2*hidden_dim) lstm_input = torch.cat([input_embedded, context], dim=2) # (batch, 1, emb_dim+2*hidden_dim) output, (hidden, cell) = self.lstm(lstm_input, (hidden, cell)) # output: (batch, 1, hidden_dim) logits = self.fc(torch.cat([output.squeeze(1), input_embedded.squeeze(1)], dim=1)) # (batch, vocab_size) return logits, hidden, cell class AttnSeq2Seq(nn.Module): def __init__(self, vocab_size, emb_dim=128, hidden_dim=256): super().__init__() self.encoder = AttnEncoder(vocab_size, emb_dim, hidden_dim) self.decoder = AttnDecoder(vocab_size, emb_dim, hidden_dim) self.device = device self.sos_token_id = 101 # [CLS] self.eos_token_id = 102 # [SEP] self.unk_token_id = 100 # [UNK] def forward(self, src, tgt): encoder_outputs, (hidden, cell) = self.encoder(src) batch_size, tgt_len = tgt.size() outputs = torch.zeros(batch_size, tgt_len, self.decoder.fc.out_features).to(device) input_ids = tgt[:, 0] for t in range(1, tgt_len): logits, hidden, cell = self.decoder(input_ids, hidden, cell, encoder_outputs) outputs[:, t] = logits input_ids = tgt[:, t] return outputs def generate(self, src, max_length=80): encoder_outputs, (hidden, cell) = self.encoder(src) # 修正后的生成初始化 generated = torch.full((src.size(0), 1), self.sos_token_id, device=device) # 注意这里的修正 for _ in range(max_length-1): logits, hidden, cell = self.decoder(generated[:, -1], hidden, cell, encoder_outputs) next_token = torch.argmax(logits, dim=1, keepdim=True) # 防止过早生成标点 if generated.size(1) < 5: punctuation = [',', '.', ';', ':', '!', '?', "'", '"', '', '~'] punct_ids = [self.tokenizer.convert_tokens_to_ids(p) for p in punctuation] if next_token.item() in punct_ids: # 替换为最常见的实词 next_token = torch.tensor([[self.tokenizer.convert_tokens_to_ids('the')]], device=device) generated = torch.cat([generated, next_token], dim=1) if (next_token == self.eos_token_id).all(): break return generated # Transformer模型 class PositionalEncoding(nn.Module): def __init__(self, d_model, max_len=5000): super().__init__() pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) self.register_buffer('pe', pe.unsqueeze(0)) def forward(self, x): return x + self.pe[:, :x.size(1)] class TransformerModel(nn.Module): def __init__(self, vocab_size, d_model=128, nhead=8, num_layers=3, dim_feedforward=512, max_len=5000): super().__init__() self.embedding = nn.Embedding(vocab_size, d_model, padding_idx=0) self.pos_encoder = PositionalEncoding(d_model, max_len) # 编码器 encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout=0.1) self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers) # 解码器 decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout=0.1) self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers) self.fc = nn.Linear(d_model, vocab_size) self.d_model = d_model self.sos_token_id = 101 # [CLS] self.eos_token_id = 102 # [SEP] def _generate_square_subsequent_mask(self, sz): mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1) mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0)) return mask def forward(self, src, tgt): src_mask = None tgt_mask = self._generate_square_subsequent_mask(tgt.size(1)).to(device) src_key_padding_mask = (src == 0) tgt_key_padding_mask = (tgt == 0) src = self.embedding(src) * np.sqrt(self.d_model) src = self.pos_encoder(src) tgt = self.embedding(tgt) * np.sqrt(self.d_model) tgt = self.pos_encoder(tgt) memory = self.transformer_encoder(src.transpose(0, 1), src_mask, src_key_padding_mask) output = self.transformer_decoder( tgt.transpose(0, 1), memory, tgt_mask, None, tgt_key_padding_mask, src_key_padding_mask ) output = self.fc(output.transpose(0, 1)) return output def generate(self, src, max_length=80): src_mask = None src_key_padding_mask = (src == 0) src = self.embedding(src) * np.sqrt(self.d_model) src = self.pos_encoder(src) memory = self.transformer_encoder(src.transpose(0, 1), src_mask, src_key_padding_mask) batch_size = src.size(0) generated = torch.full((batch_size, 1), self.sos_token_id, device=device) for i in range(max_length-1): tgt_mask = self._generate_square_subsequent_mask(generated.size(1)).to(device) tgt_key_padding_mask = (generated == 0) tgt = self.embedding(generated) * np.sqrt(self.d_model) tgt = self.pos_encoder(tgt) output = self.transformer_decoder( tgt.transpose(0, 1), memory, tgt_mask, None, tgt_key_padding_mask, src_key_padding_mask ) output = self.fc(output.transpose(0, 1)[:, -1, :]) next_token = torch.argmax(output, dim=1, keepdim=True) generated = torch.cat([generated, next_token], dim=1) if (next_token == self.eos_token_id).all(): break return generated # 训练函数 def train_model(model, train_loader, optimizer, criterion, epochs=3): model.train() optimizer = optim.Adam(model.parameters(), lr=1e-4) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=1, factor=0.5) start_time = time.time() for epoch in range(epochs): total_loss = 0 model.train() for i, batch in enumerate(train_loader): src = batch['input_ids'].to(device) tgt = batch['labels'].to(device) optimizer.zero_grad() outputs = model(src, tgt[:, :-1]) # 检查模型输出有效性 if torch.isnan(outputs).any(): print("警告：模型输出包含NaN，跳过此批次") continue loss = criterion(outputs.reshape(-1, outputs.size(-1)), tgt[:, 1:].reshape(-1)) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5) # 梯度裁剪 optimizer.step() total_loss += loss.item() if (i+1) % 10 == 0: print(f"Epoch {epoch+1}/{epochs} | Batch {i+1}/{len(train_loader)} | Loss: {loss.item():.4f}") avg_loss = total_loss / len(train_loader) scheduler.step(avg_loss) print(f"Epoch {epoch+1} | 平均损失: {avg_loss:.4f}") torch.cuda.empty_cache() total_time = time.time() - start_time print(f"训练完成！总耗时: {total_time:.2f}s ({total_time/60:.2f}分钟)") return model, total_time # 评估函数 def evaluate_model(model, val_loader, tokenizer, num_examples=2): model.eval() scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True) rouge_scores = {'rouge1': [], 'rouge2': [], 'rougeL': []} valid_count = 0 with torch.no_grad(): for i, batch in enumerate(val_loader): src = batch['input_ids'].to(device) tgt = batch['labels'].to(device) generated = model.generate(src) for s, p, t in zip(src, generated, tgt): src_txt = tokenizer.decode(s, skip_special_tokens=True) pred_txt = tokenizer.decode(p, skip_special_tokens=True) true_txt = tokenizer.decode(t[t != -100], skip_special_tokens=True) if len(pred_txt.split()) > 3 and len(true_txt.split()) > 3: valid_count += 1 if valid_count <= num_examples: print(f"\n原文: {src_txt[:100]}...") print(f"生成: {pred_txt}") print(f"参考: {true_txt[:80]}...") print("-"*60) if true_txt and pred_txt: scores = scorer.score(true_txt, pred_txt) for key in rouge_scores: rouge_scores[key].append(scores[key].fmeasure) if valid_count > 0: avg_scores = {key: sum(rouge_scores[key])/len(rouge_scores[key]) for key in rouge_scores} print(f"\n评估结果 (基于{valid_count}个样本):") print(f"ROUGE-1: {avg_scores['rouge1']*100:.2f}%") print(f"ROUGE-2: {avg_scores['rouge2']*100:.2f}%") print(f"ROUGE-L: {avg_scores['rougeL']*100:.2f}%") else: print("警告：未生成有效摘要") avg_scores = {key: 0.0 for key in rouge_scores} return avg_scores # 可视化模型性能 def visualize_model_performance(model_names, train_times, rouge_scores): plt.figure(figsize=(15, 6)) # 训练时间对比图 plt.subplot(1, 2, 1) bars = plt.bar(model_names, train_times) plt.title('模型训练时间对比') plt.ylabel('时间 (分钟)') for bar in bars: height = bar.get_height() plt.text(bar.get_x() + bar.get_width()/2., height, f'{height:.1f} min', ha='center', va='bottom') # ROUGE分数对比图 plt.subplot(1, 2, 2) x = np.arange(len(model_names)) width = 0.25 plt.bar(x - width, [scores['rouge1'] for scores in rouge_scores], width, label='ROUGE-1') plt.bar(x, [scores['rouge2'] for scores in rouge_scores], width, label='ROUGE-2') plt.bar(x + width, [scores['rougeL'] for scores in rouge_scores], width, label='ROUGE-L') plt.title('模型ROUGE分数对比') plt.ylabel('F1分数') plt.xticks(x, model_names) plt.legend() plt.tight_layout() plt.savefig('performance_comparison.png') plt.show() print("性能对比图已保存为 performance_comparison.png") # 交互式文本摘要生成 def interactive_summarization(models, tokenizer, model_names, max_length=80): while True: print("\n" + "="*60) print("文本摘要交互式测试 (输入 'q' 退出)") print("="*60) input_text = input("请输入要摘要的文本：\n") if input_text.lower() == 'q': break if len(input_text) < 50: print("请输入更长的文本（至少50个字符）") continue # 生成摘要 inputs = tokenizer( input_text, max_length=384, truncation=True, padding='max_length', return_tensors='pt' ).to(device) print("\n生成摘要中...") all_summaries = [] for i, model in enumerate(models): model.eval() with torch.no_grad(): generated = model.generate(inputs["input_ids"]) summary = tokenizer.decode(generated[0], skip_special_tokens=True) all_summaries.append(summary) # 打印结果 print(f"\n{model_names[i]} 摘要:") print("-"*50) print(summary) print("-"*50) print("\n所有模型摘要对比:") for i, (name, summary) in enumerate(zip(model_names, all_summaries)): print(f"{i+1}. {name}: {summary}") # 主程序 print("加载数据集...") dataset = load_dataset("cnn_dailymail", "3.0.0") tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') vocab_size = len(tokenizer.vocab) # 准备训练数据 print("准备训练数据...") train_ds = SummaryDataset(dataset['train'], tokenizer, subset_size=0.01) # 使用1%的数据 val_ds = SummaryDataset(dataset['validation'], tokenizer, subset_size=0.01) train_loader = DataLoader(train_ds, batch_size=4, shuffle=True, num_workers=0) val_loader = DataLoader(val_ds, batch_size=8, shuffle=False, num_workers=0) # 定义损失函数 criterion = nn.CrossEntropyLoss(ignore_index=-100) # 训练基础Seq2Seq print("\n" + "="*60) print("训练基础Seq2Seq模型") print("="*60) basic_model = BasicSeq2Seq(vocab_size).to(device) trained_basic, basic_time = train_model(basic_model, train_loader, None, criterion, epochs=3) basic_rouge = evaluate_model(trained_basic, val_loader, tokenizer) # 训练注意力Seq2Seq print("\n" + "="*60) print("训练注意力Seq2Seq模型") print("="*60) attn_model = AttnSeq2Seq(vocab_size).to(device) trained_attn, attn_time = train_model(attn_model, train_loader, None, criterion, epochs=3) attn_rouge = evaluate_model(trained_attn, val_loader, tokenizer) # 训练Transformer print("\n" + "="*60) print("训练Transformer模型") print("="*60) transformer_model = TransformerModel(vocab_size).to(device) trained_transformer, transformer_time = train_model(transformer_model, train_loader, None, criterion, epochs=3) transformer_rouge = evaluate_model(trained_transformer, val_loader, tokenizer) # 可视化模型性能 print("\n" + "="*60) print("模型性能对比") print("="*60) model_names = ['基础Seq2Seq', '注意力Seq2Seq', 'Transformer'] train_times = [basic_time/60, attn_time/60, transformer_time/60] rouge_scores = [basic_rouge, attn_rouge, transformer_rouge] visualize_model_performance(model_names, train_times, rouge_scores) # 交互式测试 print("\n" + "="*60) print("交互式文本摘要测试") print("="*60) print("提示：输入一段文本，将同时生成三个模型的摘要结果") interactive_summarization( [trained_basic, trained_attn, trained_transformer], tokenizer, model_names ) 修改完错误后发完整代码给我

self).__init__()self.bert=BertForMaskedLM.from_pretrained('bert-base-uncased')#添加自定义摘要输出层self.summary_head=nn.Linear(self.bert.config.hidden_size,1)defforward(self,input_ids,attention_mask):...

# https://github.com/microsoft/Cream/blob/ef68993c764f241a768cd69a087ed567dec6cb40/EfficientViT/classification/model/efficientvit.py#L104-L181 import itertools import torch from torch import nn __all__ = ['LocalWindowAttention', 'C3_CGA'] class Conv2d_BN(torch.nn.Sequential): def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1, resolution=-10000): super().__init__() self.add_module('c', torch.nn.Conv2d( a, b, ks, stride, pad, dilation, groups, bias=False)) self.add_module('bn', torch.nn.BatchNorm2d(b)) torch.nn.init.constant_(self.bn.weight, bn_weight_init) torch.nn.init.constant_(self.bn.bias, 0) @torch.no_grad() def switch_to_deploy(self): c, bn = self._modules.values() w = bn.weight / (bn.running_var + bn.eps) ** 0.5 w = c.weight * w[:, None, None, None] b = bn.bias - bn.running_mean * bn.weight / \ (bn.running_var + bn.eps) ** 0.5 m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size( 0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups) m.weight.data.copy_(w) m.bias.data.copy_(b) return m class CascadedGroupAttention(torch.nn.Module): r""" Cascaded Group Attention. Args: dim (int): Number of input channels. key_dim (int): The dimension for query and key. num_heads (int): Number of attention heads. attn_ratio (int): Multiplier for the query dim for value dimension. resolution (int): Input resolution, correspond to the window size. kernels (List[int]): The kernel size of the dw conv on query. """ def __init__(self, dim, key_dim, num_heads=8, attn_ratio=4, resolution=14, kernels=[5, 5, 5, 5], ): super().__init__() self.num_heads = num_heads

class CascadedGroupAttention(nn.Module): def __init__(self, dim, key_dim, num_heads): super().__init__() self.scale = key_dim ** -0.5 self.num_heads = num_heads # 参数分配策略：QKV投影使用不同...

def pair(t): return t if isinstance(t, tuple) else (t, t) # classes class FeedForward(nn.Module): def __init__(self, dim, hidden_dim, dropout = 0.): super().__init__() self.net = nn.Sequential( nn.LayerNorm(dim), nn.Linear(dim, hidden_dim), nn.GELU(), nn.Dropout(dropout), nn.Linear(hidden_dim, dim), nn.Dropout(dropout) ) def forward(self, x): return self.net(x) class LSA(nn.Module): def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.): super().__init__() inner_dim = dim_head * heads self.heads = heads self.temperature = nn.Parameter(torch.log(torch.tensor(dim_head ** -0.5))) self.norm = nn.LayerNorm(dim) self.attend = nn.Softmax(dim = -1) self.dropout = nn.Dropout(dropout) self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False) self.to_out = nn.Sequential( nn.Linear

def __init__(self, image_size, patch_size, dim, depth): self.patch_embedding = nn.Conv2d(3, dim, kernel_size=patch_size, stride=patch_size) self.position_embedding = nn.Parameter(torch.randn(1, ...

# architectures/base_model.py import torch import inspect import torch.nn as nn from abc import ABC, abstractmethod from components import * import os from datetime import datetime class BaseMultiArchModel(ABC, nn.Module): """多架构模型基类，定义通用组件和接口""" # 组件白名单（核心定型后仅创建者可修改） # 注意：必须注册具体实现类，而非抽象基类！ ALLOWED_COMPONENTS = { "embedding_Standard": StandardEmbedding, # 具体实现类 "embedding_Position": PositionalEmbedding, # 具体实现类 "attention_Self": SelfAttentionModule, # 具体实现类 "attention_Dynamic": DynamicAttention, # 具体实现类 "norm_Layer": LayerNormModule, # 具体实现类 "norm_Dynamic": DynamicNormModule, # 具体实现类 "cnn_Conv1d": Conv1dModule, # 具体实现类 "cnn_MultiK": MultiKernelCNN, # 具体实现类 "rnn_GRU": GRUModule, # 具体实现类 "rnn_LSTM": LSTMModule, # 具体实现类 "transformer_Transformer": TransformerModule, # 具体实现类 "transformer_Lightweight": LightweightTransformer, # 具体实现类 "fusion_Conca": ConcatFusion, # 具体实现类 "fusion_Attention": AttentionFusion # 具体实现类 } # 基因快照配置 GENE_SNAPSHOT_DIR = "gene_snapshots/" def __init__(self, model_config): super().__init__() self.model_config = model_config self._init_components() self._ensure_snapshot_dir() def _init_components(self): """初始化通用组件（增加白名单校验）""" # 创建嵌入层（任务特定，需子类实现） self.embedding = self._create_embedding() # 创建通用组件（使用白名单校验） for comp_type in [ "embedding_Standard", "embedding_Position", "attention_Self", "attention_Dynamic", "norm_Layer", "norm_Dynamic", "cnn_Conv1d", "cnn_MultiK", "rnn_GRU", "rnn_LSTM", "transformer_Transformer", "transformer_Lightweight", "fusion_Conca", "fusion_Attention" ]: config = self.model_config["model_components"].get(comp_type, {}) comp_class = self.ALLOWED_COMPONENTS[comp_type] # 校验组件是否被篡改 assert issubclass(comp_class, BaseComponent), f"非法组件：{comp_type}" # 确保是具体实现类 assert not inspect.isabstract(comp_class), f"非法组件：{comp_type} 是抽象类" # 创建组件实例 setattr(self, comp_type, comp_class(**config)) # 【修改】根据配置设置默认组件的快捷引用 # 不再通过命名规则自动推断，而是通过配置文件显式指定 default_components = self.model_config.get("default_components", {}) # 默认组件映射关系 default_mapping = { "attention": "attention_Self", "norm": "norm_Layer", "cnn": "cnn_Conv1d", "rnn": "rnn_GRU", "transformer": "transformer_Transformer", "fusion": "fusion_Conca" } # 应用默认组件配置 for attr_name, default_key in default_mapping.items(): # 如果配置中指定了其他组件，则使用配置中的组件 config_key = default_components.get(attr_name, default_key) # 检查组件是否存在 if not hasattr(self, config_key): raise KeyError(f"默认组件 {config_key} 未在组件初始化中创建，请检查配置！") # 设置快捷引用，例如 self.attention = self.attention_Self setattr(self, attr_name, getattr(self, config_key)) def _ensure_snapshot_dir(self): """确保基因快照目录存在""" if not os.path.exists(self.GENE_SNAPSHOT_DIR): os.makedirs(self.GENE_SNAPSHOT_DIR) @abstractmethod def _create_embedding(self): """创建任务特定的嵌入层""" pass @abstractmethod def _create_fusion(self): """创建特征融合层""" pass # 通用组件创建方法（保留，但修改为使用配置中指定的组件） def _create_attention(self): # 从配置中获取使用哪个注意力组件 attention_type = self.model_config["model_components"].get("attention_type", "attention_Self") config = self.model_config["model_components"].get(attention_type, {}) return self.ALLOWED_COMPONENTS[attention_type]( embed_dim=self.model_config["embedding_dim"], **config ) def _create_normalization(self): # 从配置中获取使用哪个归一化组件 norm_type = self.model_config["model_components"].get("norm_type", "norm_Layer") config = self.model_config["model_components"].get(norm_type, {}) return self.ALLOWED_COMPONENTS[norm_type]( num_features=self.model_config["embedding_dim"], **config ) def _create_cnn(self): # 从配置中获取使用哪个CNN组件 cnn_type = self.model_config["model_components"].get("cnn_type", "cnn_Conv1d") config = self.model_config["model_components"].get(cnn_type, {}) return self.ALLOWED_COMPONENTS[cnn_type]( in_channels=self.model_config["embedding_dim"], out_channels=self.model_config["hidden_dim"], **config ) def _create_rnn(self): # 从配置中获取使用哪个RNN组件 rnn_type = self.model_config["model_components"].get("rnn_type", "rnn_GRU") config = self.model_config["model_components"].get(rnn_type, {}) return self.ALLOWED_COMPONENTS[rnn_type]( input_size=self.model_config["embedding_dim"], hidden_size=self.model_config["hidden_dim"], **config ) def _create_transformer(self): # 从配置中获取使用哪个Transformer组件 transformer_type = self.model_config["model_components"].get("transformer_type", "transformer_Transformer") config = self.model_config["model_components"].get(transformer_type, {}) return self.ALLOWED_COMPONENTS[transformer_type]( d_model=self.model_config["embedding_dim"], **config ) def forward_features(self, x): """通用特征提取流程""" # 嵌入层处理 x_embed = self.embedding(x) # 使用默认的归一化和注意力组件 x_embed = self.norm(x_embed) if hasattr(self, 'norm') else x_embed x_embed = self.attention(x_embed) if hasattr(self, 'attention') else x_embed # 多架构特征提取 cnn_out = self.cnn(x_embed.transpose(1, 2)).transpose(1, 2) if hasattr(self, 'cnn') else None rnn_out = self.rnn(x_embed) if hasattr(self, 'rnn') else None trans_out = self.transformer(x_embed) if hasattr(self, 'transformer') else None # 特征融合 fusion_inputs = {} if cnn_out is not None: fusion_inputs["cnn"] = cnn_out if rnn_out is not None: fusion_inputs["rnn"] = rnn_out if trans_out is not None: fusion_inputs["transformer"] = trans_out fused_features = self.fusion(fusion_inputs) return fused_features @abstractmethod def forward(self, x): """主前向传播方法""" pass def save_gene_snapshot(self, snapshot_name=None, evolution_note=""): """保存核心组件权重（基因快照）""" snapshot_name = snapshot_name or datetime.now().strftime("%Y%m%d_%H%M%S") snapshot_path = os.path.join(self.GENE_SNAPSHOT_DIR, f"{snapshot_name}.pth") # 保存所有组件的权重 state_dict = { "model_config": self.model_config } # 保存主要组件的权重 for attr in ["embedding", "attention", "norm", "cnn", "rnn", "transformer", "fusion"]: if hasattr(self, attr): state_dict[attr] = getattr(self, attr).state_dict() # 保存所有注册的组件权重 for comp_type in self.ALLOWED_COMPONENTS.keys(): if hasattr(self, comp_type): state_dict[comp_type] = getattr(self, comp_type).state_dict() torch.save(state_dict, snapshot_path) # 写入进化日志 with open(os.path.join(self.GENE_SNAPSHOT_DIR, "evolution_log.md"), "a") as f: f.write(f"## {snapshot_name}\n") f.write(f"变更原因：{evolution_note}\n") f.write(f"时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n") def load_gene_snapshot(self, snapshot_path): """加载基因快照（复活/回退用）""" state_dict = torch.load(snapshot_path) # 更新配置 self.model_config = state_dict["model_config"] # 加载主要组件权重 for attr in ["embedding", "attention", "norm", "cnn", "rnn", "transformer", "fusion"]: if attr in state_dict and hasattr(self, attr): getattr(self, attr).load_state_dict(state_dict[attr]) # 加载所有注册的组件权重 for comp_type in self.ALLOWED_COMPONENTS.keys(): if comp_type in state_dict and hasattr(self, comp_type): getattr(self, comp_type).load_state_dict(state_dict[comp_type]) print(f"[萧默芯] 成功加载基因快照: {snapshot_path}") 帮我分析一下这个代码的可靠性和能力！

class BaseMultiArchModel(nn.Module): def __init__(self, arch_config: Dict[str, bool]): self.use_cnn = arch_config["cnn"] # 动态启用模块 if self.use_cnn: self.cnn = CNNBackbone() 2. **...

这是我的代码：import os import time import torch import torch.nn as nn import torch.optim as optim import pandas as pd import numpy as np from torch.utils.data import Dataset, DataLoader, random_split from torchvision import transforms from PIL import Image from tqdm import tqdm import matplotlib.pyplot as plt os.system("") # 激活ANSI支持 torch.manual_seed(42) np.random.seed(42) # -------------------------- # 1. 数据集定义（保持不变） # -------------------------- class FluidImageDataset(Dataset): def __init__(self, blade_params, pressure_dir, velocity_u_dir, velocity_v_dir, velocity_w_dir, transform=None): self.blade_params = blade_params self.pressure_dir = pressure_dir self.velocity_u_dir = velocity_u_dir self.velocity_v_dir = velocity_v_dir self.velocity_w_dir = velocity_w_dir self.transform = transform or transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor(), ]) def __len__(self): return len(self.blade_params) def __getitem__(self, idx): params = torch.tensor(self.blade_params.iloc[idx].values, dtype=torch.float32) img_id = idx + 1 # 简化图像加载 def load_img(path): try: return self.transform(Image.open(path).convert('L')) except: return torch.zeros(1, 256, 256) return {'params': params, 'outputs': { 'pressure': load_img(os.path.join(self.pressure_dir, f"Pressure_{img_id}.jpg")), 'velocity_u': load_img(os.path.join(self.velocity_u_dir, f"Velocity_u_{img_id}.jpg")), 'velocity_v': load_img(os.path.join(self.velocity_v_dir, f"Velocity_v_{img_id}.jpg")), 'velocity_w': load_img(os.path.join(self.velocity_w_dir, f"Velocity_w_{img_id}.jpg")), }} # -------------------------- # 2. 傅里叶KAN层（核心修改：使用傅里叶基函数替代原有非线性函数） # -------------------------- class FourierKANLayer(nn.Module): def __init__(self, input_dim, output_dim, grid_size=16, add_bias=True, smooth_initialization=True): super().__init__() self.input_dim = input_dim self.output_dim = output_dim self.grid_size = grid_size # 傅里叶频率数量 self.add_bias = add_bias # 傅里叶系数初始化（正弦和余弦） # 平滑初始化：高频系数衰减，保证初始函数平滑 grid_norm = (torch.arange(grid_size) + 1) **2 if smooth_initialization else np.sqrt(grid_size) self.fourier_coeffs = nn.Parameter( torch.randn(2, output_dim, input_dim, grid_size) / (np.sqrt(input_dim) * grid_norm) ) # 偏置项 if self.add_bias: self.bias = nn.Parameter(torch.zeros(1, output_dim)) def forward(self, x): # 处理向量数据 (B, input_dim) 或图像数据 (B, C, H, W) original_shape = x.shape if x.dim() == 4: # 图像数据展平为 (B*H*W, C) 便于处理 batch_size, channels, height, width = x.shape x = x.permute(0, 2, 3, 1).reshape(-1, channels) # (B*H*W, C) # 傅里叶基函数计算：cos(kx) 和 sin(kx) k = torch.arange(1, self.grid_size + 1, device=x.device).reshape(1, 1, 1, self.grid_size) # 频率 x_expanded = x.unsqueeze(1).unsqueeze(3) # (B, 1, input_dim, 1) cos_terms = torch.cos(k * x_expanded) # (B, 1, input_dim, grid_size) sin_terms = torch.sin(k * x_expanded) # (B, 1, input_dim, grid_size) # 傅里叶系数加权求和 y = torch.sum(cos_terms * self.fourier_coeffs[0:1], dim=(-2, -1)) # 余弦项贡献 y += torch.sum(sin_terms * self.fourier_coeffs[1:2], dim=(-2, -1)) # 正弦项贡献 # 添加偏置 if self.add_bias: y += self.bias # 恢复图像数据形状 if len(original_shape) == 4: y = y.reshape(batch_size, height, width, self.output_dim).permute(0, 3, 1, 2) # (B, output_dim, H, W) else: y = y.reshape(original_shape[:-1] + (self.output_dim,)) # 向量数据恢复形状 return y class KANFeatureExtractor(nn.Module): def __init__(self, input_dim): super().__init__() self.layers = nn.Sequential( FourierKANLayer(input_dim, 128, grid_size=16), # 傅里叶KAN层 nn.LayerNorm(128), FourierKANLayer(128, 128, grid_size=16), nn.LayerNorm(128), FourierKANLayer(128, 256, grid_size=16), nn.LayerNorm(256), FourierKANLayer(256, 256, grid_size=16) ) def forward(self, x): return self.layers(x) # -------------------------- # 3. U-Net结构（使用傅里叶KAN层替代原有激活函数） # -------------------------- class MiniUNetEncoder(nn.Module): def __init__(self, in_channels=8): super().__init__() self.conv1 = nn.Sequential( nn.Conv2d(in_channels, 16, kernel_size=3, padding=1), nn.GroupNorm(4, 16), FourierKANLayer(16, 16, grid_size=16), # 傅里叶KAN替代ReLU nn.Conv2d(16, 16, kernel_size=3, padding=1), nn.GroupNorm(4, 16), FourierKANLayer(16, 16, grid_size=16) ) self.pool1 = nn.MaxPool2d(2, 2) self.conv2 = nn.Sequential( nn.Conv2d(16, 32, kernel_size=3, padding=1), nn.GroupNorm(4, 32), FourierKANLayer(32, 32, grid_size=16), nn.Conv2d(32, 32, kernel_size=3, padding=1), nn.GroupNorm(4, 32), FourierKANLayer(32, 32, grid_size=16) ) self.pool2 = nn.MaxPool2d(2, 2) def forward(self, x): c1 = self.conv1(x) p1 = self.pool1(c1) c2 = self.conv2(p1) p2 = self.pool2(c2) return c1, c2, p2 class MiniUNetDecoder(nn.Module): def __init__(self, out_channels=1): super().__init__() self.up1 = nn.ConvTranspose2d(32, 16, kernel_size=2, stride=2) self.conv3 = nn.Sequential( nn.Conv2d(48, 16, kernel_size=3, padding=1), nn.GroupNorm(4, 16), FourierKANLayer(16, 16, grid_size=16) # 傅里叶KAN替代ReLU ) self.up2 = nn.ConvTranspose2d(16, 8, kernel_size=2, stride=2) self.final_conv = nn.Conv2d(24, out_channels, kernel_size=1) self.sigmoid = nn.Sigmoid() def forward(self, c1, c2, p2): u1 = self.up1(p2) cat1 = torch.cat([u1, c2], dim=1) d1 = self.conv3(cat1) u2 = self.up2(d1) cat2 = torch.cat([u2, c1], dim=1) out = self.final_conv(cat2) return self.sigmoid(out) class MiniUNetPredictor(nn.Module): def __init__(self): super().__init__() self.encoder = MiniUNetEncoder() self.decoder = MiniUNetDecoder() def forward(self, x): c1, c2, p2 = self.encoder(x) return self.decoder(c1, c2, p2) # -------------------------- # 4. 模型组装（保持结构不变，使用傅里叶KAN层） # -------------------------- class LightKANUNetModel(nn.Module): def __init__(self, input_dim): super().__init__() self.kan_feature = KANFeatureExtractor(input_dim) # 特征投影模块（使用傅里叶KAN） self.feature_proj = nn.Sequential( nn.Linear(256, 128), nn.LayerNorm(128), FourierKANLayer(128, 128, grid_size=16), # 傅里叶KAN替代ReLU # 生成初始特征图 nn.Linear(128, 8 * 16 * 16), nn.Unflatten(1, (8, 16, 16)), # 上采样卷积块 nn.Conv2d(8, 16, kernel_size=3, padding=1), nn.GroupNorm(4, 16), FourierKANLayer(16, 16, grid_size=16), nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True), nn.Conv2d(16, 32, kernel_size=3, padding=1), nn.GroupNorm(4, 32), FourierKANLayer(32, 32, grid_size=16), nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True), nn.Conv2d(32, 64, kernel_size=3, padding=1), nn.GroupNorm(4, 64), FourierKANLayer(64, 64, grid_size=16), nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True), nn.Conv2d(64, 32, kernel_size=3, padding=1), nn.GroupNorm(4, 32), FourierKANLayer(32, 32, grid_size=16), nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True), nn.Conv2d(32, 8, kernel_size=3, padding=1), nn.GroupNorm(4, 8), FourierKANLayer(8, 8, grid_size=16) ) self.predictors = nn.ModuleDict({ 'pressure': MiniUNetPredictor(), 'velocity_u': MiniUNetPredictor(), 'velocity_v': MiniUNetPredictor(), 'velocity_w': MiniUNetPredictor() }) def forward(self, x): features = self.kan_feature(x) feat_map = self.feature_proj(features) return {k: self.predictors[k](feat_map) for k in self.predictors} # -------------------------- # 5. 训练与工具函数（保持不变） # -------------------------- def load_data(blade_params_path, data_dir): blade_params = pd.read_excel(blade_params_path).iloc[:, 1:] print(f"叶片参数形状：{blade_params.shape}（样本数×参数数）") dataset = FluidImageDataset( blade_params, os.path.join(data_dir, "Pressure"), os.path.join(data_dir, "Velocity_u"), os.path.join(data_dir, "Velocity_v"), os.path.join(data_dir, "Velocity_w") ) total_size = len(dataset) train_size = int(0.8 * total_size) val_size = int(0.1 * total_size) test_size = total_size - train_size - val_size generator = torch.Generator().manual_seed(42) train_dataset, val_dataset, test_dataset = random_split( dataset, [train_size, val_size, test_size], generator=generator ) return ( DataLoader(train_dataset, batch_size=1, shuffle=True, num_workers=0), DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=0), DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=0), blade_params.shape[1] ) def train_model(model, train_loader, criterion, optimizer, device, epochs=3): model.train() if torch.cuda.is_available(): torch.cuda.reset_peak_memory_stats() print(f"初始GPU内存：{torch.cuda.memory_allocated() / 1e6:.1f} MB") for epoch in range(epochs): running_loss = 0.0 pbar = tqdm(train_loader, total=len(train_loader), desc=f"Epoch {epoch + 1}/{epochs}") for batch_idx, data in enumerate(pbar): start_time = time.time() params = data['params'].to(device) true_outputs = {k: v.to(device) for k, v in data['outputs'].items()} optimizer.zero_grad() with torch.amp.autocast('cuda', enabled=torch.cuda.is_available()): pred_outputs = model(params) loss = sum(criterion(pred_outputs[k], true_outputs[k]) for k in pred_outputs) loss.backward() optimizer.step() running_loss += loss.item() batch_time = time.time() - start_time if (batch_idx + 1) % 10 == 0 or batch_idx == len(train_loader) - 1: avg_loss = running_loss / (batch_idx + 1) gpu_mem = torch.cuda.memory_allocated() / 1e6 if torch.cuda.is_available() else 0 pbar.set_postfix({ "批损失": f"{loss.item():.4f}", "平均损失": f"{avg_loss:.4f}", "耗时": f"{batch_time:.2f}s", "GPU内存": f"{gpu_mem:.1f} MB" }) print(f"Epoch {epoch + 1} 完成，平均损失：{running_loss / len(train_loader):.4f}") return model def evaluate_model(model, test_loader, criterion, device): model.eval() metrics = {k: [] for k in ['pressure', 'velocity_u', 'velocity_v', 'velocity_w']} with torch.no_grad(): for data in test_loader: params = data['params'].to(device) true_outputs = {k: v.to(device) for k, v in data['outputs'].items()} pred_outputs = model(params) for k in metrics: metrics[k].append(criterion(pred_outputs[k], true_outputs[k]).item()) avg = {k: sum(v) / len(v) for k, v in metrics.items()} print("\n测试集结果：") print(f"总平均MSE：{sum(avg.values()) / 4:.4f}") for k, v in avg.items(): print(f" {k}：{v:.6f}") return avg def visualize_predictions(model, test_loader, device, num_samples=3): model.eval() save_dir = "KAN_UNet_Results" os.makedirs(save_dir, exist_ok=True) with torch.no_grad(): for i, data in enumerate(test_loader): if i >= num_samples: break params = data['params'].to(device) true = data['outputs'] pred = {k: v.cpu() for k, v in model(params).items()} fig, axes = plt.subplots(1, 9, figsize=(24, 5)) axes[0].text(0.1, 0.5, "参数:\n" + "\n".join( [f"p{j}: {v:.2f}" for j, v in enumerate(params[0].cpu().numpy().round(2))]), fontsize=9, va='center') axes[0].axis('off') for col, k in enumerate(['pressure', 'velocity_u', 'velocity_v', 'velocity_w']): axes[2 * col + 1].imshow(true[k][0].squeeze(), cmap='viridis') axes[2 * col + 1].set_title(f'真实{k}') axes[2 * col + 1].axis('off') axes[2 * col + 2].imshow(pred[k][0].squeeze(), cmap='viridis') axes[2 * col + 2].set_title(f'预测{k}') axes[2 * col + 2].axis('off') plt.tight_layout() plt.savefig(f"{save_dir}/样本_{i + 1}.png", dpi=200) plt.close() print(f"已保存样本 {i + 1} 可视化结果") def main(): save_dir = "KAN_UNet_Results" blade_params_path = r"C:\Users\ZYQ\PycharmProjects\2025.07.09\Data\Blade parameters.xlsx" data_dir = r"C:\Users\ZYQ\PycharmProjects\2025.07.09\Data" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"使用设备：{device}") train_loader, val_loader, test_loader, input_dim = load_data(blade_params_path, data_dir) print(f"输入维度：{input_dim}，训练批次：{len(train_loader)}") model = LightKANUNetModel(input_dim).to(device) total_params = sum(p.numel() for p in model.parameters()) print(f"模型参数量：{total_params / 1e6:.2f} M") criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-5) print("\n开始训练...") model = train_model(model, train_loader, criterion, optimizer, device, epochs=3) evaluate_model(model, test_loader, criterion, device) visualize_predictions(model, test_loader, device) torch.save(model.state_dict(), f"{save_dir}/fourier_kan_unet.pth") print(f"模型保存至：{save_dir}/fourier_kan_unet.pth") if __name__ == "__main__": main()

| 主干架构 | 纯卷积神经网络(CNN) | CNN+Transformer混合架构 | | 结构对称性 | 严格的U形对称设计[^1] | 非对称设计(Transformer模块打破对称性) | | 模块组成 | 编码器+瓶颈层+解码器 | 增加Transformer编码器/...

class ConvNeXtBlock(nn.Module): def __init__(self, in_channels, out_channels): super(ConvNeXtBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) self.bn1 = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU() self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) self.bn2 = nn.BatchNorm2d(out_channels) def forward(self, x): x = self.relu(self.bn1(self.conv1(x))) x = self.bn2(self.conv2(x)) return x ———————————————— 这个代码什么走呀

self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # 深度卷积，groups=dim即深度可分离卷积 self.norm = LayerNorm(dim, eps=1e-6) self.pwconv1 = nn.Linear(dim, 4 * dim) # 使用线性...

class MRCNN(nn.Module):# 特征提取模块 def __init__(self, afr_reduced_cnn_size): super(MRCNN, self).__init__()#多分辨率卷积分支 drate = 0.5 self.GELU = GELU() # for older versions of PyTorch. For new versions use nn.GELU() instead. self.features1 = nn.Sequential( nn.Conv1d(1, 64, kernel_size=50, stride=6, bias=False, padding=24), nn.BatchNorm1d(64),# 大卷积核捕获低频特征 self.GELU, nn.MaxPool1d(kernel_size=8, stride=2, padding=4), nn.Dropout(drate), nn.Conv1d(64, 128, kernel_size=8, stride=1, bias=False, padding=4), nn.BatchNorm1d(128), self.GELU, nn.Conv1d(128, 128, kernel_size=8, stride=1, bias=False, padding=4), nn.BatchNorm1d(128), self.GELU, nn.MaxPool1d(kernel_size=4, stride=4, padding=2) ) self.features2 = nn.Sequential( nn.Conv1d(1, 64, kernel_size=400, stride=50, bias=False, padding=200), nn.BatchNorm1d(64), self.GELU, nn.MaxPool1d(kernel_size=4, stride=2, padding=2), nn.Dropout(drate), nn.Conv1d(64, 128, kernel_size=7, stride=1, bias=False, padding=3), nn.BatchNorm1d(128),# 小卷积核捕获高频特征 self.GELU, nn.Conv1d(128, 128, kernel_size=7, stride=1, bias=False, padding=3), nn.BatchNorm1d(128), self.GELU, nn.MaxPool1d(kernel_size=2, stride=2, padding=1) ) self.dropout = nn.Dropout(drate) self.inplanes = 128 self.AFR = self._make_layer(SEBasicBlock, afr_reduced_cnn_size, 1) def _make_layer(self, block, planes, blocks, stride=1): # makes residual SE block downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv1d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm1d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): x1 = self.features1(x) x2 = self.features2(x) x_concat = torch.cat((x1, x2), dim=2) x_concat = self.dropout(x_concat) x_concat = self.AFR(x_concat) return x_concat ########################################################################################## def attention(query, key, value, dropout=None): "Implementation of Scaled dot product attention" d_k = query.size(-1) scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k) p_attn = F.softmax(scores, dim=-1) if dropout is not None: p_attn = dropout(p_attn) return torch.matmul(p_attn, value), p_attn class CausalConv1d(torch.nn.Conv1d): def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True): self.__padding = (kernel_size - 1) * dilation super(CausalConv1d, self).__init__( in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=self.__padding, dilation=dilation, groups=groups, bias=bias)请帮我理解该部分代码 并逐行讲解

def __init__(self, afr_reduced_cnn_size): super(MRCNN, self).__init__() drate = 0.5 self.GELU = GELU() # 使用GELU激活函数（旧版本兼容） 2. 多分辨率分支结构 python # 第一分支（低频特征提取...

Traceback (most recent call last): File "tools/train.py", line 121, in <module> main() File "tools/train.py", line 117, in main runner.train() File "D:\anaconda3\envs\openmmlab2\lib\site-packages\mmengine\runner\runner.py", line 1777, in train model = self.train_loop.run() # type: ignore File "D:\anaconda3\envs\openmmlab2\lib\site-packages\mmengine\runner\loops.py", line 289, in run self.run_iter(data_batch) File "D:\anaconda3\envs\openmmlab2\lib\site-packages\mmengine\runner\loops.py", line 313, in run_iter outputs = self.runner.model.train_step( File "D:\anaconda3\envs\openmmlab2\lib\site-packages\mmengine\model\base_model\base_model.py", line 114, in train_step losses = self._run_forward(data, mode='loss') # type: ignore File "D:\anaconda3\envs\openmmlab2\lib\site-packages\mmengine\model\base_model\base_model.py", line 361, in _run_forward results = self(**data, mode=mode) File "D:\anaconda3\envs\openmmlab2\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\anaconda3\envs\openmmlab2\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "e:\tuxiangfenge\mmdetection\mmdet\models\detectors\base.py", line 92, in forward return self.loss(inputs, data_samples) File "e:\tuxiangfenge\mmdetection\mmdet\models\detectors\maskformer.py", line 63, in loss losses = self.panoptic_head.loss(x, batch_data_samples) File "e:\tuxiangfenge\mmdetection\mmdet\models\dense_heads\maskformer_head.py", line 554, in loss all_cls_scores, all_mask_preds = self(x, batch_data_samples) File "D:\anaconda3\envs\openmmlab2\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\anaconda3\envs\openmmlab2\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "e:\tuxiangfenge\mmdetection\mmdet\models\dense_heads\mask2former_AdativeQuery_head.py", line 115, in forward mask_features, multi_scale_memorys = self.pixel_decoder(x) File "D:\anaconda3\envs\openmmlab2\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\anaconda3\envs\openmmlab2\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "e:\tuxiangfenge\mmdetection\mmdet\models\layers\hierarchicaldeformablepixeldecoder.py", line 262, in forward decoder_query = layer( File "D:\anaconda3\envs\openmmlab2\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\anaconda3\envs\openmmlab2\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "e:\tuxiangfenge\mmdetection\mmdet\models\layers\hierarchicaldeformablepixeldecoder.py", line 51, in forward query = self.deform_attn( File "D:\anaconda3\envs\openmmlab2\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\anaconda3\envs\openmmlab2\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "D:\anaconda3\envs\openmmlab2\lib\site-packages\mmcv\utils\misc.py", line 340, in new_func output = old_func(*args, **kwargs) File "D:\anaconda3\envs\openmmlab2\lib\site-packages\mmcv\ops\multi_scale_deform_attn.py", line 330, in forward bs, num_query, _ = query.shape ValueError: not enough values to unpack (expected 3, got 2) # Copyright (c) OpenMMLab. All rights reserved. from typing import List, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import Conv2d, ConvModule from mmcv.cnn.bricks.transformer import (MultiScaleDeformableAttention, MultiheadAttention, FFN) from mmengine.model import (BaseModule, ModuleList, caffe2_xavier_init, normal_init, xavier_init) from torch import Tensor from mmdet.registry import MODELS from mmdet.utils import ConfigType, OptMultiConfig from ..task_modules.prior_generators import MlvlPointGenerator from .positional_encoding import SinePositionalEncoding from .transformer import Mask2FormerTransformerEncoder class HierarchicalDecoderLayer(BaseModule): """分层注意力优化版本""" def __init__(self, self_attn_cfg, deform_attn_cfg, use_self_attn=False): super().__init__() self.use_self_attn = use_self_attn if self.use_self_attn: self.self_attn = MultiheadAttention(**self_attn_cfg) self.deform_attn = MultiScaleDeformableAttention(**deform_attn_cfg) self.ffn = FFN( embed_dims=256, feedforward_channels=1024, num_fcs=2, ffn_drop=0.1, act_cfg=dict(type='ReLU')) def forward(self, query, reference_points, feat_maps, is_high_level=False): # 跨查询自注意力（仅高层使用） if self.use_self_attn and is_high_level: query = query.transpose(0, 1) query = self.self_attn(query, query, query)[0] query = query.transpose(0, 1) # 维度修正（核心修改） if reference_points.dim() == 3: reference_points = reference_points.unsqueeze(2) # [bs, nq, 1, 2] # 可变形注意力处理 bs, _, h, w = feat_maps.shape spatial_shapes = torch.tensor([[h, w]], device=feat_maps.device) value = feat_maps.flatten(2).permute(0, 2, 1) query = self.deform_attn( query=query, value=value, reference_points=reference_points, spatial_shapes=spatial_shapes, level_start_index=torch.tensor([0], device=query.device), batch_first=True) return self.ffn(query) @MODELS.register_module() class HierarchicalDeformablePixelDecoder(BaseModule): def __init__(self, in_channels: Union[List[int], Tuple[int]] = [256, 512, 1024, 2048], strides: Union[List[int], Tuple[int]] = [4, 8, 16, 32], feat_channels: int = 256, out_channels: int = 256, num_outs: int = 3, norm_cfg: ConfigType = dict(type='GN', num_groups=32), act_cfg: ConfigType = dict(type='ReLU'), encoder: ConfigType = None, positional_encoding: ConfigType = dict(num_feats=128, normalize=True), init_cfg: OptMultiConfig = None, use_hierarchical_attn: bool = True, num_queries: int = 100): super().__init__(init_cfg=init_cfg) self.strides = strides self.num_input_levels = len(in_channels) self.num_encoder_levels = encoder.layer_cfg.self_attn_cfg.num_levels self.use_hierarchical_attn = use_hierarchical_attn self.num_layers = encoder.num_layers self.num_queries = num_queries self.spatial_shapes = None # 初始化核心组件 self._init_original_components(in_channels, norm_cfg, act_cfg, encoder, positional_encoding, feat_channels, out_channels) # 层次化解码器配置 self.decoder_layers = ModuleList([ HierarchicalDecoderLayer( self_attn_cfg=dict( embed_dims=feat_channels, num_heads=8, dropout=0.1, batch_first=True), deform_attn_cfg=dict( embed_dims=feat_channels, num_heads=8, num_levels=1, num_points=4, batch_first=True), use_self_attn=(i >= self.num_layers // 2) ) for i in range(self.num_layers) ]) def _prepare_encoder_inputs(self, feats: List[Tensor]) -> tuple: batch_size = feats[0].shape[0] encoder_input_list = [] padding_mask_list = [] level_pos_embed_list = [] spatial_shapes = [] reference_points_list = [] for i in range(self.num_encoder_levels): level_idx = self.num_input_levels - i - 1 feat = feats[level_idx] feat_projected = self.input_convs[i](feat) feat_h, feat_w = feat.shape[-2:] feat_hw = torch.tensor([[feat_h, feat_w]], device=feat.device) padding_mask = feat.new_zeros((batch_size, feat_h, feat_w), dtype=torch.bool) pos_embed = self.positional_encoding(padding_mask) level_embed = self.level_encoding.weight[i] level_pos_embed = level_embed.view(1, -1, 1, 1) + pos_embed reference_points = [] for lv in range(self.num_encoder_levels): stride = self.strides[level_idx] * (2 ** lv) ref_points = self.point_generator.single_level_grid_priors( (feat_h, feat_w), lv, device=feat.device) feat_wh = torch.tensor([[feat_w, feat_h]], device=feat.device) ref_points = ref_points / (feat_wh * stride) reference_points.append(ref_points) reference_points = torch.stack(reference_points, dim=1) feat_projected = feat_projected.flatten(2).permute(0, 2, 1) level_pos_embed = level_pos_embed.flatten(2).permute(0, 2, 1) padding_mask = padding_mask.flatten(1) encoder_input_list.append(feat_projected) padding_mask_list.append(padding_mask) level_pos_embed_list.append(level_pos_embed) spatial_shapes.append(feat_hw) reference_points_list.append(reference_points) return ( encoder_input_list, padding_mask_list, level_pos_embed_list, spatial_shapes, reference_points_list ) def _init_original_components(self, in_channels, norm_cfg, act_cfg, encoder, positional_encoding, feat_channels, out_channels): self.input_convs = ModuleList() for i in range(self.num_input_levels - 1, self.num_input_levels - self.num_encoder_levels - 1, -1): input_conv = ConvModule( in_channels[i], feat_channels, kernel_size=1, norm_cfg=norm_cfg, act_cfg=None) self.input_convs.append(input_conv) self.encoder = Mask2FormerTransformerEncoder(**encoder) self.positional_encoding = SinePositionalEncoding(**positional_encoding) self.level_encoding = nn.Embedding(self.num_encoder_levels, feat_channels) self.lateral_convs = ModuleList() self.output_convs = ModuleList() self.use_bias = norm_cfg is None for i in range(self.num_input_levels - self.num_encoder_levels - 1, -1, -1): lateral_conv = ConvModule( in_channels[i], feat_channels, kernel_size=1, bias=self.use_bias, norm_cfg=norm_cfg, act_cfg=None) output_conv = ConvModule( feat_channels, feat_channels, kernel_size=3, stride=1, padding=1, bias=self.use_bias, norm_cfg=norm_cfg, act_cfg=act_cfg) self.lateral_convs.append(lateral_conv) self.output_convs.append(output_conv) self.mask_feature = Conv2d( feat_channels, out_channels, kernel_size=1, stride=1, padding=0) self.point_generator = MlvlPointGenerator(self.strides) def _gen_ref_points(self, feat_level: int) -> Tensor: """重构参考点生成逻辑（核心修改）""" if self.spatial_shapes is None: raise RuntimeError("spatial_shapes not initialized. Run forward first.") # 获取实际数值 h, w = self.spatial_shapes[feat_level][0].tolist() stride = self.strides[feat_level] # 生成基础参考点 [num_points, 2] ref_points = self.point_generator.single_level_grid_priors( (h, w), feat_level, device=next(self.parameters()).device) # 归一化处理 feat_wh = torch.tensor([w, h], dtype=torch.float32, device=ref_points.device) normalized_points = ref_points / (feat_wh * stride) # 扩展维度 [batch_size, num_queries, num_levels=1, 2] return normalized_points.unsqueeze(0).repeat( self.num_queries, 1, 1, 1).permute(1, 0, 2, 3) def forward(self, feats: List[Tensor]) -> Tuple[Tensor, List[Tensor]]: # 准备编码器输入 (encoder_inputs, padding_masks, level_encodings, spatial_shapes, ref_points) = self._prepare_encoder_inputs(feats) self.spatial_shapes = spatial_shapes batch_size = feats[0].size(0) # 编码器处理 spatial_shapes_tensor = torch.cat(spatial_shapes) level_start_index = torch.cat(( spatial_shapes_tensor.new_zeros(1), spatial_shapes_tensor.prod(dim=1).cumsum(0)[:-1] )) memory = self.encoder( query=torch.cat(encoder_inputs, dim=1), query_pos=torch.cat(level_encodings, dim=1), key_padding_mask=torch.cat(padding_masks, dim=1), spatial_shapes=spatial_shapes_tensor, reference_points=torch.cat(ref_points, dim=0).unsqueeze(0).repeat(batch_size, 1, 1, 1), level_start_index=level_start_index, valid_ratios=torch.ones((batch_size, self.num_encoder_levels, 2), device=feats[0].device)) memory = memory.permute(0, 2, 1) # 初始化解码查询 decoder_query = torch.zeros( (batch_size, self.num_queries, 256), device=memory.device) # 分层解码处理（核心修改） for i, layer in enumerate(self.decoder_layers): feat_level = 0 if i < self.num_layers // 2 else -1 is_high_level = (i >= self.num_layers // 2) # 生成四维参考点 [bs, num_q, 1, 2] ref_points = self._gen_ref_points(feat_level).to(memory.device) decoder_query = layer( decoder_query, ref_points, feats[feat_level], is_high_level=is_high_level) # 特征分割与融合 num_queries_per_level = [s[0][0].item() * s[0][1].item() for s in spatial_shapes] outs = torch.split(memory, num_queries_per_level, dim=-1) outs = [ x.reshape(batch_size, -1, int(s[0][0]), int(s[0][1])) for x, s in zip(outs, spatial_shapes) ] # FPN特征融合 for i in range(self.num_input_levels - self.num_encoder_levels - 1, -1, -1): x = feats[i] cur_feat = self.lateral_convs[i](x) y = cur_feat + F.interpolate( outs[-1], size=cur_feat.shape[-2:], mode='bilinear', align_corners=False) y = self.output_convs[i](y) outs.append(y) return self.mask_feature(outs[-1]), outs[:self.num_outs] def init_weights(self): for conv in self.input_convs: xavier_init(conv.conv, distribution='uniform') for conv in self.lateral_convs + self.output_convs: caffe2_xavier_init(conv.conv) normal_init(self.level_encoding, std=0.01) caffe2_xavier_init(self.mask_feature) for p in self.encoder.parameters(): if p.dim() > 1: nn.init.xavier_normal_(p) for layer in self.decoder_layers: if hasattr(layer, 'self_attn'): for param in layer.self_attn.parameters(): if param.dim() > 1: nn.init.xavier_uniform_(param)

好的，我现在需要帮助用户解决在使用mmdetection中的...- 若使用自定义模型或修改过mmdetection源码，检查是否误改了相关模块的解包逻辑； - 使用虚拟环境（如conda）管理依赖，避免版本冲突。

class MultiHeadAttention(nn.Module): def __init__(self, d_model=512, heads=8): # 修正默认值匹配CNN输出通道 super().__init__() self.d_model = d_model self.heads = heads self.d_k = d_model // heads # 保持原QKV线性层定义 self.q_linear = nn.Linear(d_model, d_model) self.k_linear = nn.Linear(d_model, d_model) self.v_linear = nn.Linear(d_model, d_model) self.out = nn.Linear(d_model, d_model) self.attn_dropout = nn.Dropout(0.1) def forward(self, x): batch_size, C, H, W = x.size() L = H * W # 显式计算序列长度 # 维度转换修正 x = x.permute(0, 2, 3, 1).view(batch_size, L, C) # [B, L, C] # 保持原有QKV生成逻辑 q = self.q_linear(x).view(batch_size, L, self.heads, self.d_k).transpose(1,2) k = self.k_linear(x).view(batch_size, L, self.heads, self.d_k).transpose(1,2) v = self.v_linear(x).view(batch_size, L, self.heads, self.d_k).transpose(1,2) # 保持原注意力计算 scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) attn_weights = torch.softmax(scores, dim=-1) # 添加注意力dropout attn = self.attn_dropout(attn_weights) context = torch.matmul(attn, v).transpose(1,2) context = context.contiguous().view(batch_size, L, C) # 输出维度修正 output = self.out(context).view(batch_size, L, C) return output.permute(0, 2, 1).view(batch_size, C, H, W) # 严格恢复原始维度

attn_input = cnn_feat.view(batch_size, seq_len, embed_dim).transpose(0,1) 2. **位置编码增强**：建议为展平后的特征添加二维位置编码[^2] $$ PE_{(pos,h,w)} = PE_{h}^{row} + PE_{w}^{col} $$ #### ...

# 这是一个示例 Python 脚本。 # 按 Shift+F10 执行或将其替换为您的代码。 # 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。 import argparse import math import pickle import torch import torch.nn as nn import torch.nn.functional as F from tqdm import tqdm from omegaconf import OmegaConf from sklearn.metrics import f1_score from torch.utils.data import Dataset, DataLoader from torch.nn import TransformerEncoderLayer, TransformerEncoder restypes = [ 'A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V' ] unsure_restype = 'X' unknown_restype = 'U' def make_dataset(data_config, train_rate=0.7, valid_rate=0.2): data_path = data_config.data_path with open(data_path, 'rb') as f: data = pickle.load(f) total_number = len(data) train_sep = int(total_number * train_rate) valid_sep = int(total_number * (train_rate + valid_rate)) train_data_dicts = data[:train_sep] valid_data_dicts = data[train_sep:valid_sep] test_data_dicts = data[valid_sep:] train_dataset = DisProtDataset(train_data_dicts) valid_dataset = DisProtDataset(valid_data_dicts) test_dataset = DisProtDataset(test_data_dicts) return train_dataset, valid_dataset, test_dataset class DisProtDataset(Dataset): def __init__(self, dict_data): sequences = [d['sequence'] for d in dict_data] labels = [d['label'] for d in dict_data] assert len(sequences) == len(labels) self.sequences = sequences self.labels = labels self.residue_mapping = {'X':20} self.residue_mapping.update(dict(zip(restypes, range(len(restypes))))) def __len__(self): return len(self.sequences) def __getitem__(self, idx): sequence = torch.zeros(len(self.sequences[idx]), len(self.residue_mapping)) for i, c in enumerate(self.sequences[idx]): if c not in restypes: c = 'X' sequence[i][self.residue_mapping[c]] = 1 label = torch.tensor([int(c) for c in self.labels[idx]], dtype=torch.long) return sequence, label class PositionalEncoding(nn.Module): def __init__(self, d_model, dropout=0.0, max_len=40): super().__init__() position = torch.arange(max_len).unsqueeze(1) div_term = torch.exp( torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) ) pe = torch.zeros(1, max_len, d_model) pe[0, :, 0::2] = torch.sin(position * div_term) pe[0, :, 1::2] = torch.cos(position * div_term) self.register_buffer("pe", pe) self.dropout = nn.Dropout(p=dropout) def forward(self, x): if len(x.shape) == 3: x = x + self.pe[:, : x.size(1)] elif len(x.shape) == 4: x = x + self.pe[:, :x.size(1), None, :] return self.dropout(x) class DisProtModel(nn.Module): def __init__(self, model_config): super().__init__() self.d_model = model_config.d_model self.n_head = model_config.n_head self.n_layer = model_config.n_layer self.input_layer = nn.Linear(model_config.i_dim, self.d_model) self.position_embed = PositionalEncoding(self.d_model, max_len=20000) self.input_norm = nn.LayerNorm(self.d_model) self.dropout_in = nn.Dropout(p=0.1) encoder_layer = TransformerEncoderLayer( d_model=self.d_model, nhead=self.n_head, activation='gelu', batch_first=True) self.transformer = TransformerEncoder(encoder_layer, num_layers=self.n_layer) self.output_layer = nn.Sequential( nn.Linear(self.d_model, self.d_model), nn.GELU(), nn.Dropout(p=0.1), nn.Linear(self.d_model, model_config.o_dim) ) def forward(self, x): x = self.input_layer(x) x = self.position_embed(x) x = self.input_norm(x) x = self.dropout_in(x) x = self.transformer(x) x = self.output_layer(x) return x def metric_fn(pred, gt): pred = pred.detach().cpu() gt = gt.detach().cpu() pred_labels = torch.argmax(pred, dim=-1).view(-1) gt_labels = gt.view(-1) score = f1_score(y_true=gt_labels, y_pred=pred_labels, average='micro') return score if __name__ == '__main__': device = 'cuda' if torch.cuda.is_available() else 'cpu' parser = argparse.ArgumentParser('IDRs prediction') parser.add_argument('--config_path', default='./config.yaml') args = parser.parse_args() config = OmegaConf.load(args.config_path) train_dataset, valid_dataset, test_dataset = make_dataset(config.data) train_dataloader = DataLoader(dataset=train_dataset, **config.train.dataloader) valid_dataloader = DataLoader(dataset=valid_dataset, batch_size=1, shuffle=False) model = DisProtModel(config.model) model = model.to(device) optimizer = torch.optim.AdamW(model.parameters(), lr=config.train.optimizer.lr, weight_decay=config.train.optimizer.weight_decay) loss_fn = nn.CrossEntropyLoss() model.eval() metric = 0. with torch.no_grad(): for sequence, label in valid_dataloader: sequence = sequence.to(device) label = label.to(device) pred = model(sequence) metric += metric_fn(pred, label) print("init f1_score:", metric / len(valid_dataloader)) for epoch in range(config.train.epochs): # train loop progress_bar = tqdm( train_dataloader, initial=0, desc=f"epoch:{epoch:03d}", ) model.train() total_loss = 0. for sequence, label in progress_bar: sequence = sequence.to(device) label = label.to(device) pred = model(sequence) loss = loss_fn(pred.permute(0, 2, 1), label) progress_bar.set_postfix(loss=loss.item()) total_loss += loss.item() optimizer.zero_grad() loss.backward() optimizer.step() avg_loss = total_loss / len(train_dataloader) # valid loop model.eval() metric = 0. with torch.no_grad(): for sequence, label in valid_dataloader: sequence = sequence.to(device) label = label.to(device) pred = model(sequence) metric += metric_fn(pred, label) print(f"avg_training_loss: {avg_loss}, f1_score: {metric / len(valid_dataloader)}") # 保存当前 epoch 的模型 save_path = f"model.pkl" torch.save(model.state_dict(), save_path) print(f"Model saved to {save_path}") 帮我分析一下这个代码是干什么的

output = self.transformer_encoder(embedded) # [seq_len, batch_size, d_model] logits = self.classifier(output) # [seq_len, batch_size, 1] return self.sigmoid(logits.squeeze(-1)) # [seq_len, batch_...

import torch import torch.nn as nn import torch.nn.functional as F import os import numpy as np import pandas as pd import pickle from torch.utils.data import Dataset, DataLoader, random_split from torch.optim import Adam # 配置文件路径 LABEL_FILE = r'E:\Python_Eswin\growing\dataset_202507_10spec\necking_ingot1_2_match_cleaned_data_above_50rows_cleaned.csv' SPEED_DIR = r'E:\Python_Eswin\growing\dataset_202507_10spec\ingot_tau_cal\Pull Speed Actual_embedded_5' ADC_DIR = r'E:\Python_Eswin\growing\dataset_202507_10spec\ingot_tau_cal\ADC Actual V_embedded_6' MELTGAP_DIR = r'E:\Python_Eswin\growing\dataset_202507_10spec\ingot_tau_cal\Melt_Gap_embedded_5' SPEC_FILE = r'E:\Python_Eswin\growing\dataset_202507_10spec\ingot_10_spec_length_cleaned_above_50rows.csv' MODEL_PATH = r"D:\anaconda\envs\pytorch\GrowingProject\lstm\for_10spec\saved_model\best_model.pth" TEST_SET_PATH = r"D:\anaconda\envs\pytorch\GrowingProject\lstm\for_10spec\saved_model\test_set.pkl" # 确保目录存在 os.makedirs(os.path.dirname(MODEL_PATH), exist_ok=True) class PullingDataset(Dataset): def __init__(self, label_df, speed_dir, adc_dir, meltgap_dir, spec_file): # 保存原始标签DataFrame self.labels = label_df self.speed_dir = speed_dir self.adc_dir = adc_dir self.meltgap_dir = meltgap_dir self.spec_data = self._load_spec_data(spec_file) # 加载全局参数数据 # 预计算所有序列长度并过滤有效样本 self.lengths = [] self.valid_indices = [] # 调试信息：打印全局参数文件中的ID数量 print(f"全局参数文件中找到的铸锭ID数量: {len(self.spec_data)}") print(f"前5个铸锭ID: {list(self.spec_data.keys())[:5]}") for idx, ingot_id in enumerate(self.labels.iloc[:, 0]): # 确保ID为字符串并去除可能的前后空格 ingot_id_str = str(ingot_id).strip() # 调试信息：打印当前处理的ID if idx < 5: # 只打印前5个用于调试 print(f"处理标签文件中的铸锭ID: '{ingot_id_str}' (索引: {idx})") # 检查所有必需文件是否存在 speed_path = os.path.join(speed_dir, f"{ingot_id_str}_embedded_output.csv") adc_path = os.path.join(adc_dir, f"{ingot_id_str}_embedded_output.csv") meltgap_path = os.path.join(meltgap_dir, f"{ingot_id_str}_embedded_output.csv") file_exists = ( os.path.exists(speed_path) and os.path.exists(adc_path) and os.path.exists(meltgap_path) and ingot_id_str in self.spec_data ) if file_exists: try: # 获取各序列长度（跳过第一行索引） speed_df = pd.read_csv(speed_path, header=0) adc_df = pd.read_csv(adc_path, header=0) meltgap_df = pd.read_csv(meltgap_path, header=0) speed_len = len(speed_df) adc_len = len(adc_df) meltgap_len = len(meltgap_df) # 取最小长度 min_len = min(speed_len, adc_len, meltgap_len) if min_len > 0: self.lengths.append(min_len) self.valid_indices.append(idx) else: print(f"警告: {ingot_id_str} 文件有空数据") except Exception as e: print(f"读取文件错误: {ingot_id_str} - {e}") else: missing_files = [] if not os.path.exists(speed_path): missing_files.append("speed") if not os.path.exists(adc_path): missing_files.append("ADC") if not os.path.exists(meltgap_path): missing_files.append("MeltGap") if ingot_id_str not in self.spec_data: missing_files.append("spec_params") # 调试信息：检查为什么找不到spec_params print(f"在全局参数文件中找不到ID: '{ingot_id_str}'") if len(self.spec_data) > 0: print(f"全局参数文件中的ID示例: {list(self.spec_data.keys())[:5]}") print(f"文件缺失: {ingot_id_str} ({', '.join(missing_files)})") # 过滤有效样本 if len(self.valid_indices) > 0: self.labels = self.labels.iloc[self.valid_indices] print(f"有效样本数量: {len(self.labels)}") # 如果没有有效样本，打印更多信息 if len(self.labels) == 0: print("错误: 没有找到有效样本!") print(f"标签文件中的ID示例: {self.labels.iloc[:5, 0].tolist()}") print(f"全局参数文件中的ID示例: {list(self.spec_data.keys())[:5]}") print("请检查ID是否匹配（包括类型和格式）") def _load_spec_data(self, file_path): """加载全局参数文件，返回{ingot_id: 特征向量}""" try: df = pd.read_csv(file_path, header=0) print(f"成功加载全局参数文件: {file_path}") print(f"文件包含 {len(df)} 行数据") except Exception as e: print(f"加载全局参数文件错误: {e}") return {} spec_dict = {} for _, row in df.iterrows(): # 处理ID格式：先转换为浮点数再转为整数，最后转为字符串（去除小数部分） try: ingot_id = str(int(float(row.iloc[0]))) # 关键修改：处理浮点型ID spec_dict[ingot_id] = row.iloc[1:10].values.astype(np.float32) except ValueError as e: print(f"转换ID错误: {row.iloc[0]} - {e}") continue print(f"全局参数文件中找到的铸锭ID数量: {len(spec_dict)}") print(f"处理后ID示例: {list(spec_dict.keys())[:5]}") return spec_dict def __len__(self): return len(self.labels) def __getitem__(self, idx): row = self.labels.iloc[idx] ingot_id = str(row.iloc[0]).strip() # 确保ingot_id是字符串并去除空格 label = row.iloc[11] # 第12列: 标签 (0或1) try: # 加载所有序列数据（跳过第一行索引） speed_data = pd.read_csv( os.path.join(self.speed_dir, f"{ingot_id}_embedded_output.csv"), header=0 ).values.astype(np.float32) adc_data = pd.read_csv( os.path.join(self.adc_dir, f"{ingot_id}_embedded_output.csv"), header=0 ).values.astype(np.float32) meltgap_data = pd.read_csv( os.path.join(self.meltgap_dir, f"{ingot_id}_embedded_output.csv"), header=0 ).values.astype(np.float32) # 获取全局参数向量 spec_vector = self.spec_data[ingot_id] # 取最小长度 min_len = min(len(speed_data), len(adc_data), len(meltgap_data)) # 截取相同长度的序列 speed_data = speed_data[:min_len, :] adc_data = adc_data[:min_len, :] meltgap_data = meltgap_data[:min_len, :] # 创建全局参数矩阵（复制min_len次） spec_matrix = np.tile(spec_vector, (min_len, 1)) # 合并所有特征 combined_data = np.concatenate([ speed_data, # 5维 adc_data, # 6维 meltgap_data, # 5维 spec_matrix # 9维 ], axis=1) # 转换为tensor seq = torch.tensor(combined_data, dtype=torch.float32) label = float(label) label = torch.tensor(label, dtype=torch.float32) return seq, label, min_len except Exception as e: print(f"错误: 无法处理铸锭 {ingot_id}: {e}") return torch.zeros(0), torch.tensor(0.0), 0 def collate_fn(batch): sequences, labels, lengths = zip(*batch) max_len = max(lengths) if lengths else 0 # 过滤掉空序列 valid_indices = [i for i, seq in enumerate(sequences) if len(seq) > 0] if not valid_indices: return torch.zeros(0), torch.zeros(0), torch.zeros(0) sequences = [sequences[i] for i in valid_indices] labels = [labels[i] for i in valid_indices] lengths = [lengths[i] for i in valid_indices] # 获取特征维度 feat_dim = sequences[0].shape[1] if sequences[0].nelement() > 0 else 0 # 填充序列 padded_seqs = torch.zeros(len(sequences), max_len, feat_dim) for i, seq in enumerate(sequences): padded_seqs[i, :lengths[i]] = seq lengths = torch.tensor(lengths, dtype=torch.long) labels = torch.stack(labels) return padded_seqs, labels, lengths def save_test_set(test_dataset, path): test_data = [] for i in range(len(test_dataset)): test_data.append(test_dataset[i]) with open(path, 'wb') as f: pickle.dump(test_data, f) class SpeedLSTM(nn.Module): def __init__(self, input_dim=25, hidden_dim=128, num_layers=2, dropout=0.2): super().__init__() # 添加LayerNorm稳定训练 self.input_norm = nn.LayerNorm(input_dim) self.lstm = nn.LSTM( input_size=input_dim, hidden_size=hidden_dim, num_layers=num_layers, bidirectional=True, batch_first=True, dropout=dropout if num_layers > 1 else 0 ) # 添加梯度裁剪和更稳定的分类器 self.classifier = nn.Sequential( nn.LayerNorm(2 * hidden_dim), nn.Linear(2 * hidden_dim, 64), nn.ReLU(), nn.Dropout(dropout), nn.LayerNorm(64), nn.Linear(64, 32), nn.ReLU(), nn.Dropout(dropout), nn.LayerNorm(32), nn.Linear(32, 1) ) def forward(self, x, lengths): if x.nelement() == 0: return torch.zeros(x.size(0)), None x = self.input_norm(x) # 输入标准化 packed = nn.utils.rnn.pack_padded_sequence(x, lengths.cpu(), batch_first=True, enforce_sorted=False) packed_out, _ = self.lstm(packed) out, _ = nn.utils.rnn.pad_packed_sequence(packed_out, batch_first=True) # 取最后一个有效时间步 last_output = out[torch.arange(out.size(0)), lengths - 1] # 梯度裁剪 torch.nn.utils.clip_grad_norm_(self.parameters(), max_norm=1.0) return self.classifier(last_output).squeeze(-1), None def save_model(model, MODEL_PATH): os.makedirs(os.path.dirname(MODEL_PATH), exist_ok=True) try: torch.save(model.state_dict(), MODEL_PATH) print(f"模型成功保存至: {MODEL_PATH}") except Exception as e: print(f"模型保存失败: {e}") def load_model(path, input_dim=25): model = SpeedLSTM(input_dim) model.load_state_dict(torch.load(path, map_location=torch.device('cpu'))) model.eval() print(f"从 {path} 加载模型") return model def main(): print(f"使用设备: {'GPU' if torch.cuda.is_available() else 'CPU'}") # 加载标签数据 - 跳过第一行索引 try: # 使用header=0跳过第一行索引 labels_df = pd.read_csv(LABEL_FILE, header=0) print(f"加载标签数据: {labels_df.shape[0]} 行") except Exception as e: print(f"加载数据错误: {e}") return # 创建完整数据集 full_dataset = PullingDataset( labels_df, SPEED_DIR, ADC_DIR, MELTGAP_DIR, SPEC_FILE ) if len(full_dataset) == 0: print("错误: 没有有效样本!") return # 检查第一个样本获取输入维度 sample, label, length = full_dataset[0] if len(sample) > 0: input_dim = sample.shape[1] print(f"检测到输入维度: {input_dim} (5拉速 + 6ADC + 5MeltGap + 9全局参数)") else: print("警告: 无法获取样本维度, 使用默认值25") input_dim = 25 # 划分训练/验证集 (80%/20%) train_size = int(0.8 * len(full_dataset)) test_size = len(full_dataset) - train_size train_dataset, test_dataset = random_split(full_dataset, [train_size, test_size]) # 保存测试集 save_test_set(test_dataset, TEST_SET_PATH) print(f"保存测试集到 {TEST_SET_PATH}") # 创建数据加载器 train_loader = DataLoader( train_dataset, batch_size=32, shuffle=True, collate_fn=collate_fn) # 初始化模型 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = SpeedLSTM(input_dim=input_dim).to(device) # 计算类别权重 labels = [] for i in range(len(full_dataset)): try: _, label_val, _ = full_dataset[i] labels.append(float(label_val.item())) except Exception as e: print(f"错误: 获取标签值失败: {e}") pos_count = sum(labels) neg_count = len(labels) - pos_count pos_weight = torch.tensor([neg_count / max(pos_count, 1)]).to(device) print(f"类别分布: 负样本 {neg_count}, 正样本 {pos_count}") print(f"正样本权重: {pos_weight.item():.2f}") criterion = nn.BCEWithLogitsLoss(pos_weight=pos_weight) optimizer = Adam(model.parameters(), lr=0.0001, weight_decay=1e-4) torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) # 训练参数 best_val_loss = float('inf') epochs = 100 early_stop_patience = 20 no_improve_count = 0 print("\n开始训练...") for epoch in range(epochs): model.train() train_loss = 0.0 train_correct = 0 train_total = 0 for inputs, labels, lengths in train_loader: if inputs.nelement() == 0: continue inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() outputs, _ = model(inputs, lengths) loss = criterion(outputs, labels) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) optimizer.step() preds = (torch.sigmoid(outputs) > 0.5).float() train_correct += (preds == labels).sum().item() train_total += labels.size(0) train_loss += loss.item() * inputs.size(0) # 计算平均损失和准确率 train_loss = train_loss / train_total if train_total > 0 else 0 train_acc = train_correct / train_total if train_total > 0 else 0 print(f"Epoch {epoch + 1}/{epochs}: " f"Train Loss: {train_loss:.4f}, " f"Acc: {train_acc:.4f}") # 保存最佳模型 if train_loss < best_val_loss: best_val_loss = train_loss save_model(model, MODEL_PATH) no_improve_count = 0 print(f"保存最佳模型 (loss={best_val_loss:.4f})") else: no_improve_count += 1 if no_improve_count >= early_stop_patience: print(f"早停: {early_stop_patience}个epoch无改进") break print(f"\n训练完成! 最佳模型保存至: {MODEL_PATH}") if __name__ == "__main__": main() 我想要优化这个二分类预测模型的准确性，目前准确度在50%左右

def __init__(self, hidden_size): super(Attention, self).__init__() self.hidden_size = hidden_size self.attn = nn.Linear(hidden_size * 2, hidden_size) # 双向LSTM，所以是2倍 self.v = nn.Linear...

import torch import torch.nn as nn import torch.nn.functional as F class HybridAttentionPooling(nn.Module): def __init__(self, d_model, num_heads=4, lstm_layers=2): super().__init__() # LSTM层，提取双向上下文信息 self.lstm = nn.LSTM( input_size=d_model, hidden_size=d_model, num_layers=lstm_layers, bidirectional=True, batch_first=True ) # 多头注意力池化层 self.num_heads = num_heads self.query = nn.Parameter(torch.randn(num_heads, d_model * 2)) # 使用LSTM的双向输出 self.scale = (d_model * 2) ** -0.5 # LSTM双向输出的维度 self.out_proj = nn.Linear(d_model * 2 * num_heads, d_model) self.norm = nn.LayerNorm(d_model) def forward(self, x): """ x : (B, L, d_model) 输入数据 """ B, L, _ = x.shape # 1. 使用LSTM提取双向上下文信息 lstm_out, _ = self.lstm(x) # (B, L, d_model*2) # 2. 使用查询向量与LSTM输出计算注意力 queries = self.query.unsqueeze(0) * self.scale # (1, num_heads, d_model*2) keys = lstm_out.transpose(1, 2) # (B, d_model*2, L) # 计算注意力得分 attn = torch.matmul( queries, # (1, num_heads, d_model*2) keys.unsqueeze(1) # (B, 1, d_model*2, L) ) # -> (B, num_heads, 1, L) attn = attn.squeeze(2) # (B, num_heads, L) # Softmax归一化 attn = F.softmax(attn, dim=-1) # 优化计算，避免 repeat_interleave 增加内存开销 lstm_out_heads = lstm_out.unsqueeze(1).expand(-1, self.num_heads, -1, -1) # (B, num_heads, L, d_model*2) # 聚合注意力加权和 attn_heads = attn.unsqueeze(-1) # (B, num_heads, L, 1) pooled = torch.sum(attn_heads * lstm_out_heads, dim=2) # (B, num_heads, d_model*2) # 展平为一个向量 pooled = pooled.reshape(B, -1) # (B, num_heads * d_model*2) # 线性变换到原始维度 pooled = self.out_proj(pooled) # 归一化 return self.norm(pooled)

input_size=d_model, hidden_size=d_model, num_layers=lstm_layers, bidirectional=True, batch_first=True ) - 作用：提取序列的**双向上下文信息**（前向+后向），输出维度为$d_{\text{model}} \...

class LocalBottleNeck(nn.Module): def init(self): super(LocalBottleNeck, self).init() self.conv1 = self._make_branch(1024) self.conv2 = self._make_branch(1024) self.conv3 = self._make_branch(1024) self.down1 = nn.Sequential( nn.Conv2d(3072, 1024, 1, bias=False), nn.BatchNorm2d(1024), nn.Conv2d(1024, 1024, 3, 2, 1, bias=False), nn.BatchNorm2d(1024), nn.ReLU(), nn.Conv2d(1024, 2048, 1, bias=False), nn.BatchNorm2d(2048) ) self.down2 = nn.Sequential( nn.Conv2d(3072, 2048, 3, 2, 1, bias=False), nn.BatchNorm2d(2048), ) self.final = nn.Sequential( ResidualBlock(2048, 2048), ResidualBlock(2048, 2048), ) def _make_branch(self, out_ch): return nn.Sequential( nn.Conv2d(768, out_ch, 1), Conv_LayerNorm(out_ch), nn.Conv2d(out_ch, out_ch, 3, 1, 1, bias=False), Conv_LayerNorm(out_ch), ) def forward(self, feats): feats1 = self.conv1(feats[0]) feats2 = self.conv2(feats[1]) feats3 = self.conv3(feats[2]) fused = torch.concat([feats1, feats2, feats3], dim=1) fused = F.relu(self.down1(fused) + self.down2(fused)) return self.final(fused)这段代码用于将vitbase输入的第2、6、10层[1, 768, 14, 14]特征融合输出[1, 2048, 7, 7]最终用来还原widresnet50的特征，请你改写这段代码，提升还原精度，要求给出完整的代码实现，不要自己提出模块，尽量使用新颖有效经过检验的高效模块，方便论文书写，给出使用理由

class LocalBottleNeck(nn.Module): def __init__(self): super().__init__() # 使用深度可分离卷积减少参数量 self.conv1 = self._make_branch(1024) self.conv2 = self._make_branch(1024) self.conv3 = ...

# 使用Vision Transformer处理声呐图像 sonar_encoder = ViTModel.from_pretrained('google/vit-base-patch16-224') sonar_features = sonar_encoder(sonar_image).last_hidden_state.mean(dim=1) # 使用LSTM处理时序洋流数据 current_lstm = nn.LSTM(input_size=3, hidden_size=64) current_features, _ = current_lstm(current_sequence) # 地形特征提取 terrain_cnn = nn.Sequential( nn.Conv2d(1, 16, 3), nn.ReLU(), nn.MaxPool2d(2), nn.Flatten() ) terrain_features = terrain_cnn(terrain_map)

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题目：在一个二维数组中，每一行都按照从左到右递增的顺序排序，每一列都按照从上到下的递增的顺序排序。请完成一个函数，输入这样的一个二维数组和一个整数，判断数组中是否含有该整数。 c++实现： #include&lt;iostream&gt; using namespace std; bool Find(int* numbers,int rows,int cols,int num) { boo...
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全球数字相控阵系统市场到2031年将达到2.91亿美元，2025-2031年复合增长率为12.7%，由BAE Systems和CesiumAstro推动.docx

全球数字相控阵系统市场到2031年将达到2.91亿美元，2025-2031年复合增长率为12.7%，......

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