linear layer+ activation layer embedding

### 如何在嵌入中使用线性层和激活层 #### 嵌入的概念及其作用嵌入是一种将离散变量转换成连续向量表示的方法。通过这种方式，可以捕捉到数据之间的语义关系并减少维度。通常情况下，在自然语言处理任务中，词会被映射为固定长度的实数向量。 #### 线性变换的作用于嵌入空间在线性层的帮助下，输入特征可以通过矩阵乘法被投影至新的潜在空间内[^1]。对于给定大小为 $d_{\text{in}}$ 的输入张量以及目标维度 $d_{\text{out}}$ ，线性层会学习一个形状为 $(d_{\text{in}}, d_{\text{out}})$ 的权重矩阵 W 和偏置 b 。当应用于嵌入时，这允许调整原始嵌入的空间结构以更好地适应下游任务的需求： ```python import torch.nn as nn class EmbeddingWithLinear(nn.Module): def __init__(self, vocab_size, embed_dim, output_dim): super(EmbeddingWithLinear, self).__init__() self.embedding = nn.Embedding(vocab_size, embed_dim) self.linear = nn.Linear(embed_dim, output_dim) def forward(self, x): embedded = self.embedding(x) # (batch_size, seq_len, embed_dim) transformed = self.linear(embedded) # (batch_size, seq_len, output_dim) return transformed ``` #### 激活函数引入非线性特性仅靠线性操作无法表达复杂的模式；因此，在实际应用中往往会在每一层之后加入激活函数来增加模型容量。ReLU 是一种常用的激活方法因为它能够有效缓解梯度消失问题并且计算效率高: ```python import torch.nn.functional as F def apply_activation(layer_output): activated = F.relu(layer_output) return activated ``` 结合上述两部分代码片段可得如下完整的实现方案： ```python class EmbeddingLayerWithActivation(nn.Module): def __init__(self, vocab_size, embed_dim, hidden_dim): super().__init__() self.embeddings = nn.Embedding(vocab_size, embed_dim) self.fc = nn.Linear(embed_dim, hidden_dim) def forward(self, text): embedded = self.embeddings(text).squeeze() # 应用嵌入层 linear_transformed = self.fc(embedded) # 进行线性变化 final_output = F.relu(linear_transformed) # 添加激活功能 return final_output ``` 此架构不仅实现了从词汇索引到低维稠密向量的有效映射，还利用了线性和非线性的组合提高了表征能力。

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ops.Cast() self.dropout = Dropout(keep_prob=(1 - keep_prob)) self.use_activation = use_activation self.convert_dtype = convert_dtype self.drop_out = drop_out def _init_activation(self, act_str): act_str = act_str.lower() if act_str == "relu": act_func = ops.ReLU() elif act_str == "sigmoid": act_func = ops.Sigmoid() elif act_str == "tanh": act_func = ops.Tanh() return act_func def construct(self, x): ''' Construct Dense layer ''' if self.training and self.drop_out: x = self.dropout(x) if self.convert_dtype: x = self.cast(x, mstype.float16) weight = self.cast(self.weight, mstype.float16) bias = self.cast(self.bias, mstype.float16) wx = self.matmul(x, weight) wx = self.bias_add(wx, bias) if self.use_activation: wx = self.act_func(wx) wx = self.cast(wx, mstype.float32) else: wx = self.matmul(x, self.weight) wx = self.bias_add(wx, self.bias) if self.use_activation: wx = self.act_func(wx) return wx class WideDeepModel(nn.Cell): """ From paper: " Wide & Deep Learning for Recommender Systems" Args: config (Class): The default config of Wide&Deep """ def init(self, config): super(WideDeepModel, self).init() self.batch_size = config.batch_size host_device_mix = bool(config.host_device_mix) parameter_server = bool(config.parameter_server) parallel_mode = context.get_auto_parallel_context("parallel_mode") is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL) if is_auto_parallel: self.batch_size = self.batch_size * get_group_size() sparse = config.sparse self.field_size = config.field_size self.emb_dim = config.emb_dim self.weight_init, self.bias_init = config.weight_bias_init self.deep_input_dims = self.field_size * self.emb_dim self.all_dim_list = [self.deep_input_dims] + config.deep_layer_dim + [1] init_acts = [('Wide_b', [1], config.emb_init)] var_map = init_var_dict(config.init_args, init_acts) self.wide_b = var_map["Wide_b"] self.dense_layer_1 = DenseLayer(self.all_dim_list[0], self.all_dim_list[1], config.weight_bias_init, config.deep_layer_act, convert_dtype=True, drop_out=config.dropout_flag) self.dense_layer_2 = DenseLayer(self.all_dim_list[1], self.all_dim_list[2], config.weight_bias_init, config.deep_layer_act, convert_dtype=True, drop_out=config.dropout_flag) self.dense_layer_3 = DenseLayer(self.all_dim_list[2], self.all_dim_list[3], config.weight_bias_init, config.deep_layer_act, convert_dtype=True, drop_out=config.dropout_flag) self.dense_layer_4 = DenseLayer(self.all_dim_list[3], self.all_dim_list[4], config.weight_bias_init, config.deep_layer_act, convert_dtype=True, drop_out=config.dropout_flag) self.dense_layer_5 = DenseLayer(self.all_dim_list[4], self.all_dim_list[5], config.weight_bias_init, config.deep_layer_act, use_activation=False, convert_dtype=True, drop_out=config.dropout_flag) self.wide_mul = ops.Mul() self.deep_mul = ops.Mul() self.reduce_sum = ops.ReduceSum(keep_dims=False) self.reshape = ops.Reshape() self.deep_reshape = ops.Reshape() self.square = ops.Square() self.concat = ops.Concat(axis=1) self.unique = ops.Unique().shard(((1,),)) self.wide_gatherv2 = ops.Gather() self.deep_gatherv2 = ops.Gather() if is_auto_parallel and sparse and not config.field_slice and not parameter_server: target = 'CPU' if host_device_mix else 'DEVICE' self.wide_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, 1, target=target, slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE) if config.deep_table_slice_mode == "column_slice": self.deep_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, self.emb_dim, target=target, slice_mode=nn.EmbeddingLookup.TABLE_COLUMN_SLICE) if config.use_sp: self.dense_layer_1.matmul.shard(((1, get_group_size()), (get_group_size(), 1))) self.dense_layer_1.bias_add.shard(((get_group_size(), 1), (1,))) self.deep_mul.shard(((1, 1, get_group_size()), (1, 1, 1))) else: self.dense_layer_1.dropout.dropout.shard(((1, get_group_size()),)) self.dense_layer_1.matmul.shard(((1, get_group_size()), (get_group_size(), 1))) self.deep_mul.shard(((1, 1, get_group_size()), (1, 1, 1))) self.dense_layer_1.matmul.add_prim_attr("field_size", self.field_size) self.deep_reshape.add_prim_attr("skip_redistribution", True) else: self.deep_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, self.emb_dim, target=target, slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE) self.reduce_sum.add_prim_attr("cross_batch", True) self.embedding_table = self.deep_embeddinglookup.embedding_table elif is_auto_parallel and host_device_mix and config.field_slice and config.full_batch and config.manual_shape: manual_shapes = tuple((s[0] for s in config.manual_shape)) self.deep_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, self.emb_dim, slice_mode=nn.EmbeddingLookup.FIELD_SLICE, manual_shapes=manual_shapes) self.wide_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, 1, slice_mode=nn.EmbeddingLookup.FIELD_SLICE, manual_shapes=manual_shapes) self.deep_mul.shard(((1, get_group_size(), 1), (1, get_group_size(), 1))) self.wide_mul.shard(((1, get_group_size(), 1), (1, get_group_size(), 1))) self.reduce_sum.shard(((1, get_group_size(), 1),)) self.dense_layer_1.dropout.dropout.shard(((1, get_group_size()),)) self.dense_layer_1.matmul.shard(((1, get_group_size()), (get_group_size(), 1))) self.embedding_table = self.deep_embeddinglookup.embedding_table elif parameter_server: cache_enable = config.vocab_cache_size > 0 target = 'DEVICE' if cache_enable else 'CPU' if not cache_enable: sparse = True if is_auto_parallel and config.full_batch and cache_enable: self.deep_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, self.emb_dim, target=target, slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE, sparse=sparse, vocab_cache_size=config.vocab_cache_size) self.wide_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, 1, target=target, slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE, sparse=sparse, vocab_cache_size=config.vocab_cache_size) else: self.deep_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, self.emb_dim, target=target, sparse=sparse, vocab_cache_size=config.vocab_cache_size) self.wide_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, 1, target=target, sparse=sparse, vocab_cache_size=config.vocab_cache_size) self.embedding_table = self.deep_embeddinglookup.embedding_table self.deep_embeddinglookup.embedding_table.set_param_ps() self.wide_embeddinglookup.embedding_table.set_param_ps() else: self.deep_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, self.emb_dim, target='DEVICE', sparse=sparse, vocab_cache_size=config.vocab_cache_size) self.wide_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, 1, target='DEVICE', sparse=sparse, vocab_cache_size=config.vocab_cache_size) self.embedding_table = self.deep_embeddinglookup.embedding_table def construct(self, id_hldr, wt_hldr): """ Args: id_hldr: batch ids; wt_hldr: batch weights; """ # Wide layer wide_id_weight = self.wide_embeddinglookup(id_hldr) # Deep layer deep_id_embs = self.deep_embeddinglookup(id_hldr) mask = self.reshape(wt_hldr, (self.batch_size, self.field_size, 1)) # Wide layer wx = self.wide_mul(wide_id_weight, mask) wide_out = self.reshape(self.reduce_sum(wx, 1) + self.wide_b, (-1, 1)) # Deep layer vx = self.deep_mul(deep_id_embs, mask) deep_in = self.deep_reshape(vx, (-1, self.field_size * self.emb_dim)) deep_in = self.dense_layer_1(deep_in) deep_in = self.dense_layer_2(deep_in) deep_in = self.dense_layer_3(deep_in) deep_in = self.dense_layer_4(deep_in) deep_out = self.dense_layer_5(deep_in) out = wide_out + deep_out return out, self.embedding_table class NetWithLossClass(nn.Cell): """" Provide WideDeep training loss through network. Args: network (Cell): The training network config (Class): WideDeep config """ def init(self, network, config): super(NetWithLossClass, self).init(auto_prefix=False) host_device_mix = bool(config.host_device_mix) parameter_server = bool(config.parameter_server) sparse = config.sparse parallel_mode = context.get_auto_parallel_context("parallel_mode") is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL) self.no_l2loss = (is_auto_parallel if (host_device_mix or config.field_slice) else parameter_server) if sparse: self.no_l2loss = True self.network = network self.l2_coef = config.l2_coef self.loss = ops.SigmoidCrossEntropyWithLogits() self.square = ops.Square() self.reduceMean_false = ops.ReduceMean(keep_dims=False) if is_auto_parallel: self.reduceMean_false.add_prim_attr("cross_batch", True) self.reduceSum_false = ops.ReduceSum(keep_dims=False) def construct(self, batch_ids, batch_wts, label): ''' Construct NetWithLossClass ''' predict, embedding_table = self.network(batch_ids, batch_wts) log_loss = self.loss(predict, label) wide_loss = self.reduceMean_false(log_loss) if self.no_l2loss: deep_loss = wide_loss else: l2_loss_v = self.reduceSum_false(self.square(embedding_table)) / 2 deep_loss = self.reduceMean_false(log_loss) + self.l2_coef * l2_loss_v return wide_loss, deep_loss class IthOutputCell(nn.Cell): def init(self, network, output_index): super(IthOutputCell, self).init() self.network = network self.output_index = output_index def construct(self, x1, x2, x3): predict = self.network(x1, x2, x3)[self.output_index] return predict class TrainStepWrap(nn.Cell): """ Encapsulation class of WideDeep network training. Append Adam and FTRL optimizers to the training network after that construct function can be called to create the backward graph. Args: network (Cell): The training network. Note that loss function should have been added. sens (Number): The adjust parameter. Default: 1024.0 host_device_mix (Bool): Whether run in host and device mix mode. Default: False parameter_server (Bool): Whether run in parameter server mode. Default: False """ def init(self, network, sens=1024.0, host_device_mix=False, parameter_server=False, sparse=False, cache_enable=False): super(TrainStepWrap, self).init() parallel_mode = context.get_auto_parallel_context("parallel_mode") is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL) self.network = network self.network.set_train() self.trainable_params = network.trainable_params() weights_w = [] weights_d = [] for params in self.trainable_params: if 'wide' in params.name: weights_w.append(params) else: weights_d.append(params) self.weights_w = ParameterTuple(weights_w) self.weights_d = ParameterTuple(weights_d) if (sparse and is_auto_parallel) or (sparse and parameter_server): self.optimizer_d = Adam( self.weights_d, learning_rate=5e-4, eps=1e-8, loss_scale=sens, use_lazy=True) self.optimizer_w = FTRL(learning_rate=1e-3, params=self.weights_w, l1=1e-8, l2=1e-8, initial_accum=1.0, loss_scale=sens) if host_device_mix or (parameter_server and not cache_enable): self.optimizer_w.target = "CPU" self.optimizer_d.target = "CPU" else: self.optimizer_d = Adam( self.weights_d, learning_rate=5e-4, eps=1e-8, loss_scale=sens) self.optimizer_w = FTRL(learning_rate=1e-3, params=self.weights_w, l1=1e-8, l2=1e-8, initial_accum=1.0, loss_scale=sens) self.hyper_map = ops.HyperMap() self.grad_w = ops.GradOperation(get_by_list=True, sens_param=True) self.grad_d = ops.GradOperation(get_by_list=True, sens_param=True) self.sens = sens self.loss_net_w = IthOutputCell(network, output_index=0) self.loss_net_d = IthOutputCell(network, output_index=1) self.loss_net_w.set_grad() self.loss_net_d.set_grad() self.reducer_flag = False self.grad_reducer_w = None self.grad_reducer_d = None self.reducer_flag = parallel_mode in (ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL) if self.reducer_flag: mean = context.get_auto_parallel_context("gradients_mean") degree = context.get_auto_parallel_context("device_num") self.grad_reducer_w = DistributedGradReducer(self.optimizer_w.parameters, mean, degree) self.grad_reducer_d = DistributedGradReducer(self.optimizer_d.parameters, mean, degree) def construct(self, batch_ids, batch_wts, label): ''' Construct wide and deep model ''' weights_w = self.weights_w weights_d = self.weights_d loss_w, loss_d = self.network(batch_ids, batch_wts, label) sens_w = ops.Fill()(ops.DType()(loss_w), ops.Shape()(loss_w), self.sens) sens_d = ops.Fill()(ops.DType()(loss_d), ops.Shape()(loss_d), self.sens) grads_w = self.grad_w(self.loss_net_w, weights_w)(batch_ids, batch_wts, label, sens_w) grads_d = self.grad_d(self.loss_net_d, weights_d)(batch_ids, batch_wts, label, sens_d) if self.reducer_flag: grads_w = self.grad_reducer_w(grads_w) grads_d = self.grad_reducer_d(grads_d) return ops.depend(loss_w, self.optimizer_w(grads_w)), ops.depend(loss_d, self.optimizer_d(grads_d)) class PredictWithSigmoid(nn.Cell): """ Predict definition """ def init(self, network): super(PredictWithSigmoid, self).init() self.network = network self.sigmoid = ops.Sigmoid() parallel_mode = context.get_auto_parallel_context("parallel_mode") full_batch = context.get_auto_parallel_context("full_batch") is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL) if is_auto_parallel and full_batch: self.sigmoid.shard(((1, 1),)) def construct(self, batch_ids, batch_wts, labels): logits, _, = self.network(batch_ids, batch_wts) pred_probs = self.sigmoid(logits) return logits, pred_probs, labels # Pre processing def pre_process_criteo_wide_deep(x): return x class WideDeepPostProcess: def init(self): self.good = 0 self.total = 0 self.roc_auc = 0 self.results = [] self.labels = [] def call(self, results, expected=None, result_dict=None): processed_results = [] n = len(results) for idx in range(0, n): result = results['auc'] processed_results.append(result) self.good += 1 self.total += 1 return processed_results def add_results(self, labels, results): self.results.append(results) self.labels.append(labels) def start(self): self.good = 0 self.total = 0 self.roc_auc = 0 self.results = [] def finalize(self, result_dict, ds=False, output_dir=None): result_dict["good"] = self.good result_dict["total"] = self.total 我在将模型和权重转化成onnx文件时需要将模型代码中的训练部分去除么？

super().__init__()self.linear=base_model.linear#仅复制推理所需组件defforward(self,x):#仅保留必需输入returnself.linear(x)###验证方法1.使用torch.onnx.export转换后：pythontorch.onnx.export(model_...

import os os.system(“python ./GPPT.py”)运行结果是什么？#GPPT.py为上面的第二个代码import argparse import get_args import time import numpy as np import networkx as nx import torch import torch.nn as nn from torch.nn import init import torch.nn.functional as F import dgl.function as fn import dgl from dgl import DGLGraph from dgl.data import register_data_args, load_data from dgl.nn.pytorch.conv import SAGEConv import dgl.nn.pytorch as dglnn from model import SAGE import matplotlib.pyplot as plt import pandas as pd import random import os import sklearn.linear_model as lm import sklearn.metrics as skm import utils import warnings warnings.filterwarnings("ignore") from sklearn.cluster import KMeans class GraphSAGE(nn.Module): def init(self, in_feats, n_hidden, n_classes, n_layers, activation, dropout, aggregator_type,center_num): super(GraphSAGE, self).init() self.layers = nn.ModuleList() self.dropout = nn.Dropout(dropout) self.activation = activation self.n_classes=n_classes self.center_num=center_num # input layer self.layers.append(SAGEConv(in_feats, n_hidden, aggregator_type)) # hidden layers for i in range(n_layers - 1): self.layers.append(SAGEConv(n_hidden, n_hidden, aggregator_type)) self.prompt=nn.Linear(n_hidden,self.center_num,bias=False) self.pp = nn.ModuleList() for i in range(self.center_num): self.pp.append(nn.Linear(2n_hidden,n_classes,bias=False)) def model_to_array(self,args): s_dict = torch.load('./data_smc/'+args.dataset+'_model_'+args.file_id+'.pt')#,map_location='cuda:0') keys = list(s_dict.keys()) res = s_dict[keys[0]].view(-1) for i in np.arange(1, len(keys), 1): res = torch.cat((res, s_dict[keys[i]].view(-1))) return res def array_to_model(self, args): arr=self.model_to_array(args) m_m=torch.load('./data_smc/'+args.dataset+'_model_'+args.file_id+'.pt')#,map_location='cuda:0')#+str(args.gpu)) indice = 0 s_dict = self.state_dict() for name, param in m_m.items(): length = torch.prod(torch.tensor(param.shape)) s_dict[name] = arr[indice:indice + length].view(param.shape) indice = indice + length self.load_state_dict(s_dict) def load_parameters(self, args): self.args=args self.array_to_model(args) def weigth_init(self,graph,inputs,label,index): h = self.dropout(inputs) for l, layer in enumerate(self.layers): h = layer(graph, h) if l != len(self.layers) - 1: h = self.activation(h) h = self.dropout(h) h = self.activation(h) graph.ndata['h']=h graph.update_all(fn.copy_u('h', 'm'), fn.mean('m', 'neighbor')) neighbor=graph.ndata['neighbor'] h=torch.cat((h,neighbor),dim=1) features=h[index] labels=label[index.long()] cluster = KMeans(n_clusters=self.center_num,random_state=0).fit(features.detach().cpu()) temp=torch.FloatTensor(cluster.cluster_centers_).cuda() self.prompt.weight.data.copy(temp) p=[] for i in range(self.n_classes): p.append(features[labels==i].mean(dim=0).view(1,-1)) temp=torch.cat(p,dim=0) for i in range(self.center_num): self.pp[i].weight.data.copy(temp) def update_prompt_weight(self,h): cluster = KMeans(n_clusters=self.center_num,random_state=0).fit(h.detach().cpu()) temp=torch.FloatTensor(cluster.cluster_centers_).cuda() self.prompt.weight.data.copy(temp) def get_mul_prompt(self): pros=[] for name,param in self.named_parameters(): if name.startswith('pp.'): pros.append(param) return pros def get_prompt(self): for name,param in self.named_parameters(): if name.startswith('prompt.weight'): pro=param return pro def get_mid_h(self): return self.fea def forward(self, graph, inputs): if self.dropout==False: h=inputs else: h = self.dropout(inputs) for l, layer in enumerate(self.layers): h_dst = h[:graph[l].num_dst_nodes()] # <--- h = layer(graph[l], (h, h_dst)) if l != len(self.layers) - 1: h = self.activation(h) if self.dropout!=False: h = self.dropout(h) h = self.activation(h) h_dst = self.activation(h_dst) neighbor=h_dst h=torch.cat((h,neighbor),dim=1) self.fea=h out=self.prompt(h) index=torch.argmax(out, dim=1) out=torch.FloatTensor(h.shape[0],self.n_classes).cuda() for i in range(self.center_num): out[index==i]=self.pp[i](h[index==i]) return out def main(args): utils.seed_torch(args.seed) g,features,labels,in_feats,n_classes,n_edges,train_nid,val_nid,test_nid,device=utils.get_init_info(args) sampler = dgl.dataloading.MultiLayerNeighborSampler(args.sample_list) train_dataloader = dgl.dataloading.NodeDataLoader(g,train_nid.int(),sampler,device=device,batch_size=args.batch_size,shuffle=True,drop_last=False,num_workers=0) model = GraphSAGE(in_feats,args.n_hidden,n_classes,args.n_layers,F.relu,args.dropout,args.aggregator_type,args.center_num) model.to(device) model.load_parameters(args) model.weigth_init(g,features,labels,train_nid) optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) acc_all=[] loss_all=[] for epoch in range(args.n_epochs): model.train() acc = utils.evaluate(model, g, test_nid, args.batch_size, device, args.sample_list) acc_all.append(acc) t0 = time.time() for step, (input_nodes, output_nodes, mfgs) in enumerate(train_dataloader): inputs = mfgs[0].srcdata['feat'] lab = mfgs[-1].dstdata['label'] logits = model(mfgs, inputs) loss = F.cross_entropy(logits, lab) loss_all.append(loss.cpu().data) loss=loss+args.lr_cutils.constraint(device,model.get_mul_prompt()) optimizer.zero_grad() loss.backward() optimizer.step() embedding_save=model.get_mid_h().detach().clone().cpu().numpy() data=pd.DataFrame(embedding_save) label=pd.DataFrame(lab.detach().clone().cpu().numpy()) data.to_csv("./data.csv",index=None,header=None) label.to_csv("./label.csv",index=None,header=None) pd.DataFrame(torch.cat(model.get_mul_prompt(),axis=1).detach().clone().cpu().numpy()).to_csv("./data_p.csv",index=None,header=None) model.update_prompt_weight(model.get_mid_h()) print("Epoch {:03d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} ".format(epoch, time.time() - t0, loss.item(),acc)) pd.DataFrame(acc_all).to_csv('./res/gs_pre_pro_mul_pro_center_c_nei_'+args.dataset+'.csv',index=None,header=None) pd.DataFrame(loss_all).to_csv('./res/gs_pre_pro_mul_pro_center_c_nei_'+args.dataset+'_loss.csv',index=None,header=None) acc = utils.evaluate(model, g, test_nid, args.batch_size, device, args.sample_list) print("Test Accuracy {:.4f}".format(np.mean(acc_all[-10:]))) if name == 'main': args=get_args.get_my_args() main(args)

def __init__(self, in_feats, n_hidden, n_classes, n_layers, activation, dropout, aggregator_type, center_num): super().__init__() self.layers = nn.ModuleList() # GraphSAGE卷积层 self.layers....

# 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

1. **嵌入层（Embedding Layer）**: - 将每个氨基酸字符映射为稠密向量表示 - 公式：$$ \mathbf{E} = \text{Embedding}(x_i) \in \mathbb{R}^{d_{\text{model}}} $$ - 其中 $x_i$ 是第 $i$ 个位置的氨基酸类别，$...

import json import torch from typing import Dict, List, Optional, Tuple from torch.utils.data import Dataset from collections import defaultdict import transformers from peft import LoraConfig, TaskType, get_peft_model from torch.utils.data import DataLoader from transformers import Trainer, TrainingArguments from lora_plus import LoraPlusTrainer from swanlab.integration.transformers import SwanLabCallback import swanlab import numpy as np import pandas as pd import re from tqdm import tqdm from transformers import PreTrainedTokenizer, AutoTokenizer import torch.nn as nn from transformers import PreTrainedModel from torch.nn import CrossEntropyLoss, MSELoss # 分子公式解析函数 def parse_chem_formula(formula): pattern = r'([A-Z][a-z]?)(\d)' matches = re.findall(pattern, formula) element_counts = defaultdict(int) for (element, count) in matches: count = int(count) if count else 1 element_counts[element] += count return element_counts def generate_element_list(formula): element_counts = parse_chem_formula(formula) elements = [] for element, count in element_counts.items(): if element != "H": elements.extend([element] count) return ''.join(elements) # 初始化SwanLab swanlab.init("Finetune-Llama3.2-with-Encoder") swanlab_callback = SwanLabCallback( project="Finetune-Llama3.2-with-Encoder", experiment_name="Finetune-Llama3.2-with-Encoder" ) # 常量定义 CHEM_FORMULA_SIZE = r"([A-Z][a-z])([0-9])" VALID_ELEMENTS = ["C", "N", "P", "O", "S", "Si", "I", "H", "Cl", "F", "Br", "B", "Se", "Fe", "Co", "As", "K", "Na"] element_to_idx = {elem: idx for idx, elem in enumerate(VALID_ELEMENTS)} # 化学式转密集向量 def formula_to_dense(chem_formula: str) -> torch.Tensor: dense_vec = torch.zeros(len(VALID_ELEMENTS), dtype=torch.float32) matches = re.findall(CHEM_FORMULA_SIZE, chem_formula) for chem_symbol, num_str in matches: num = 1 if num_str == "" else int(num_str) if chem_symbol in element_to_idx: idx = element_to_idx[chem_symbol] dense_vec[idx] += num return dense_vec # 位置编码生成 def positional_encoding(max_position: int, d_model: int, min_freq: float = 1e-4) -> torch.Tensor: position = torch.arange(max_position).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2) * (-torch.log(torch.tensor(min_freq)) / d_model)) pos_enc = torch.zeros(max_position, d_model) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] = torch.cos(position * div_term) return pos_enc # 初始化位置编码矩阵 P = positional_encoding(2000000, 254) dimn = 254 # 与位置编码维度一致 # 质谱数据编码 def encode_spectra(rag_tensor: list, P: torch.Tensor, dimn: int) -> torch.Tensor: encoded_list = [] for sample in rag_tensor: mz_list, intensity_list = sample base_features = torch.tensor([mz_list, intensity_list], dtype=torch.float32).T pos_enc = torch.stack([P[min(int(mz), P.size(0)-1)] for mz in mz_list]) features = torch.cat([base_features, pos_enc], dim=1) if features.size(0) < 501: padding = torch.zeros(501 - features.size(0), features.size(1)) features = torch.cat([features, padding], dim=0) else: features = features[:501] encoded_list.append(features) return torch.stack(encoded_list) # 质谱数据预处理 def preprocess_spectra(df: pd.DataFrame) -> list: spectra_list = [] for idx, row in tqdm(df.iterrows(), total=len(df)): spectrum_str = row['Spectrum'] total_mass = row['Total Exact Mass'] pairs = spectrum_str.split() mz_list, intensity_list = [], [] for pair in pairs: mz, intensity = pair.split(':') mz_list.append(float(mz)) intensity_list.append(float(intensity)) mz_list.append(total_mass) intensity_list.append(0.0) mz_list = [round(mz, 2) for mz in mz_list] intensity_list = [round(intensity, 2) for intensity in intensity_list] spectra_list.append([mz_list, intensity_list]) return spectra_list class MolecularDataset(Dataset): def init(self, csv_path: str, tokenizer: AutoTokenizer, max_seq_len: int = 512): self.df = pd.read_csv(csv_path) self.tokenizer = tokenizer self.max_seq_len = max_seq_len self.pad_token_id = tokenizer.pad_token_id self.mask_token_id = tokenizer.mask_token_id if tokenizer.mask_token_id is not None else tokenizer.convert_tokens_to_ids("<mask>") spectra_data = preprocess_spectra(self.df) self.spec_encoded = encode_spectra(spectra_data, P, dimn) self.element_lists = [generate_element_list(formula) for formula in self.df['Molecular Formula']] self.element_lengths = [] for elem_list in self.element_lists: elem_tokens = self.tokenizer(elem_list, add_special_tokens=False)['input_ids'] self.element_lengths.append(len(elem_tokens)) def len(self): return len(self.df) def getitem(self, idx) -> dict: formula = self.df.iloc[idx]['Molecular Formula'] formula_vec = formula_to_dense(formula).squeeze(0) # 压缩为1D向量 spec_matrix = self.spec_encoded[idx] element_list = self.element_lists[idx] element_text = f"<|Spectrum|>{element_list}" selfies_str = self.df.iloc[idx]['SELFIES'] selfies_text = f"{selfies_str}" input_text = f"{element_text}{selfies_text}" encoding = self.tokenizer( input_text, add_special_tokens=False, padding='max_length', truncation=True, max_length=self.max_seq_len, return_tensors='pt' ) input_ids = encoding['input_ids'].squeeze(0) attention_mask = encoding['attention_mask'].squeeze(0) labels = input_ids.clone() labels[labels == self.pad_token_id] = -100 element_len = self.element_lengths[idx] element_end = 3 + element_len # , <|Spectrum|>, 元素列表 if element_end < len(labels): labels[:element_end] = -100 return { 'encoder1_inputs': formula_vec, # 注意：现在是1D向量 'encoder2_inputs': spec_matrix, 'input_ids': input_ids, 'attention_mask': attention_mask, 'labels': labels, 'formula_labels': formula_vec, # 添加元素计数标签 } # 加载tokenizer tokenizer = AutoTokenizer.from_pretrained('/root/workspace/d21lv5s7v38s73b4ddlg/checkpoint-2500') if tokenizer.mask_token is None: tokenizer.add_special_tokens({"mask_token": "<mask>"}) # 创建数据集 dataset = MolecularDataset('/root/workspace/d21lv5s7v38s73b4ddlg/SELFIES-SFT.csv', tokenizer) def custom_collator(features: List[Dict]) -> Dict: batch = { 'encoder1_inputs': torch.stack([f['encoder1_inputs'] for f in features]), # 形状: (batch_size, 18) 'encoder2_inputs': torch.stack([f['encoder2_inputs'] for f in features]), 'input_ids': torch.stack([f['input_ids'] for f in features]), 'attention_mask': torch.stack([f['attention_mask'] for f in features]), 'labels': torch.stack([f['labels'] for f in features]), 'formula_labels': torch.stack([f['formula_labels'] for f in features]), # 形状: (batch_size, 18) } return batch class ElementPredictionHead(nn.Module): """化学元素计数预测头部""" def init(self, hidden_size, output_size=18): super().init() self.dense = nn.Linear(hidden_size, hidden_size) self.activation = nn.ReLU() self.layer_norm = nn.LayerNorm(hidden_size) self.out_proj = nn.Linear(hidden_size, output_size) def forward(self, hidden_states): x = self.dense(hidden_states) x = self.activation(x) x = self.layer_norm(x) x = self.out_proj(x) return x class LlamaWithEncoder(PreTrainedModel): def init(self, base_model, encoder1_dim=18, encoder2_dim=256, hidden_dim=512): self.config = base_model.config super().init(self.config) self.model = base_model # 分子式编码器 encoder1_layer = nn.TransformerEncoderLayer( d_model=encoder1_dim, nhead=3, dim_feedforward=hidden_dim, batch_first=True ) self.encoder1 = nn.TransformerEncoder(encoder1_layer, num_layers=2) # 质谱编码器 encoder2_layer = nn.TransformerEncoderLayer( d_model=encoder2_dim, nhead=8, dim_feedforward=hidden_dim, batch_first=True ) self.encoder2 = nn.TransformerEncoder(encoder2_layer, num_layers=2) # 投影层 self.proj1 = nn.Linear(encoder1_dim, base_model.config.hidden_size) self.proj2 = nn.Linear(encoder2_dim, base_model.config.hidden_size) # 嵌入层 self.embed_tokens = nn.Embedding( num_embeddings=base_model.config.vocab_size, embedding_dim=base_model.config.hidden_size, padding_idx=base_model.config.pad_token_id ) self.embed_tokens.weight.data = base_model.get_input_embeddings().weight.data.clone() # 添加元素计数预测头 self.element_head = ElementPredictionHead(base_model.config.hidden_size) # PEFT所需方法 def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def get_output_embeddings(self): return self.model.get_output_embeddings() def set_output_embeddings(self, new_embeddings): self.model.set_output_embeddings(new_embeddings) def get_base_model(self): return self.model def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, encoder1_inputs: Optional[torch.FloatTensor] = None, encoder2_inputs: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, formula_labels: Optional[torch.FloatTensor] = None, # 新增：元素计数标签 past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, kwargs ): return_dict = return_dict if return_dict is not None else self.config.use_return_dict # 1. 编码器处理 enc1_out = self.encoder1(encoder1_inputs.unsqueeze(1)) # 添加序列维度 enc1_out = enc1_out.mean(dim=1) # (batch_size, encoder1_dim) enc1_proj = self.proj1(enc1_out) # (batch_size, hidden_size) enc2_out = self.encoder2(encoder2_inputs) # (batch_size, 501, encoder2_dim) enc2_out = enc2_out.mean(dim=1) # (batch_size, encoder2_dim) enc2_proj = self.proj2(enc2_out) # (batch_size, hidden_size) # 合并编码器输出 mask_replacement = (enc1_proj + enc2_proj) / 2 # (batch_size, hidden_size) # 2. 获取原始嵌入 embeddings = self.embed_tokens(input_ids) # (batch_size, seq_len, hidden_size) batch_size, seq_len, hidden_size = embeddings.size() # 3. 替换<mask> token if seq_len > 2: mask_embed = mask_replacement.unsqueeze(1) # (batch_size, 1, hidden_size) part1 = embeddings[:, :2, :] # (batch_size, 2, hidden_size) part2 = mask_embed # (batch_size, 1, hidden_size) part3 = embeddings[:, 3:, :] # (batch_size, seq_len-3, hidden_size) new_embeddings = torch.cat([part1, part2, part3], dim=1) # (batch_size, seq_len, hidden_size) else: new_embeddings = embeddings # 4. 调用基础模型 model_output = self.model( inputs_embeds=new_embeddings, attention_mask=attention_mask, labels=labels, past_key_values=past_key_values, output_attentions=output_attentions, output_hidden_states=True, # 必须返回隐藏状态用于元素预测 return_dict=return_dict, kwargs ) # 5. 元素计数预测 element_pred = None element_loss = None if formula_labels is not None: # 获取最后一个非填充token的隐藏状态 seq_lengths = attention_mask.sum(dim=1) - 1 # 最后一个有效token的索引 batch_indices = torch.arange(batch_size, device=model_output.hidden_states[-1].device) last_token_hidden = model_output.hidden_states[-1][batch_indices, seq_lengths] # (batch_size, hidden_size) # 预测元素计数 element_pred = self.element_head(last_token_hidden) # (batch_size, 18) # 计算元素计数损失（MSE损失） element_loss = MSELoss()(element_pred, formula_labels) # 组合总损失：语言模型损失 + 元素计数损失 total_loss = model_output.loss + 0.5 * element_loss else: total_loss = model_output.loss # 返回结果 if not return_dict: output = (model_output.logits,) if element_pred is not None: output += (element_pred,) return (total_loss,) + output if total_loss is not None else output return { 'loss': total_loss, 'logits': model_output.logits, 'element_pred': element_pred, 'element_loss': element_loss, 'hidden_states': model_output.hidden_states, 'past_key_values': model_output.past_key_values, 'attentions': model_output.attentions } # 加载预训练模型 base_model = transformers.AutoModelForCausalLM.from_pretrained( "/root/workspace/d21lv5s7v38s73b4ddlg/checkpoint-2500", trust_remote_code=True, torch_dtype=torch.bfloat16, ) model = LlamaWithEncoder(base_model) # 配置LoRA lora_config = LoraConfig( r=8, lora_alpha=16, target_modules="all-linear", lora_dropout=0.0, bias="none", task_type="CAUSAL_LM" ) model = get_peft_model(model, lora_config) model.print_trainable_parameters() # 训练参数 training_args = TrainingArguments( output_dir="./llama3.2-SELFIES-SFT", per_device_train_batch_size=24, gradient_accumulation_steps=24, num_train_epochs=12, learning_rate=5.0e-05, optim="adamw_torch", logging_steps=10, bf16=True, save_strategy="steps", lr_scheduler_type='cosine', max_grad_norm=1.0, save_steps=2000, warmup_steps=0 ) class CustomTrainer(LoraPlusTrainer): def get_train_dataloader(self) -> DataLoader: return DataLoader( self.train_dataset, batch_size=self.args.train_batch_size, shuffle=True, collate_fn=self.data_collator, drop_last=False, ) # 训练模型 lp_trainer = CustomTrainer( model, training_args, train_dataset=dataset, tokenizer=tokenizer, data_collator=custom_collator, callbacks=[swanlab_callback], ) lp_trainer.train() lp_trainer.save_model(output_dir='./llama3.2-SELFIES-SFT') # 合并LoRA权重并移除元素预测头 model = model.merge_and_unload() model.element_head = None # 移除元素预测头 # 保存模型（不包括元素预测头） save_directory = './llama3.2-SELFIES' model.save_pretrained(save_directory, safe_serialization=True) tokenizer.save_pretrained(save_directory)不对，要对应修改为 element_text = f"<｜User｜><mask>{element_list}" # SELFIES目标序列并添加标记 selfies_str = self.df.iloc[idx]['SELFIES'] selfies_text = f"<｜Assistant｜>{selfies_str}"，同时化学元素计数预测模型的输入token取<｜Assistant｜>token之后的，写出完整的修改代码

我们需要修改MolecularDataset类中的__getitem__方法，将原来的元素部分用标记替换，并调整输入格式。具体修改如下： 1. 将元素列表的输入格式改为：以"<s><mask>"开头，后接元素列表字符串，然后以"</s>"结束。...

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2. **解决尺度问题**：约束多词条embedding累加带来的特征尺度漂移[^3] 3. **提升泛化能力**：实验证明可使LSTM训练速度提升30-50%，最终准确率提高2-5% 4. **任务适用性**： - 文本生成/机器翻译：必需 - 时间...

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另外，在位置编码部分，build_2d_sincos_position_embedding生成位置编码，这部分是静态的，没有可学习参数，所以不影响参数量。现在，逐步思考优化步骤： 1. 输入投影层优化：原来的input_proj对每个输入通道...

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车牌检测是计算机视觉领域的一个重要应用，尤其在智能交通系统、监控视频分析以及车辆管理等方面具有广泛的应用。CCPD2019车牌数据集是专为车牌检测任务设计的数据集，包含了大量的实际场景中拍摄的车辆图片及其对应的标注信息，这些标注信息以YOLO（You Only Look Once）格式提供。 YOLO是一种流行的目标检测算法，因其速度和准确性相结合而受到广泛欢迎。在YOLO算法中，整个图像被一次性通过网络进行处理，同时预测出多个边界框和这些框所属的类别。YOLO将目标检测任务视为一个回归问题，直接从图像像素到边界框坐标和类别概率的映射，与其他基于区域的方法相比，YOLO在速度上有很大的优势，可以实现实时检测。 YOLO格式标签是一种特殊的标注格式，它提供了用于训练和验证模型的数据。这些标签通常包含每个目标的类别以及它的位置信息，通常在一张图片的标注文件中，对于每一个检测到的车辆，都会有一个对应的标注行，标注行中包含了该车辆车牌的位置、大小和类别信息。通常这些信息包括：标注物体在原图中的中心点坐标（x,y）、宽度、高度以及类别ID。使用CCPD2019车牌数据集，研究人员和工程师可以进行深度学习模型的训练，特别是基于YOLO算法的车牌检测模型。数据集中的图片是精心挑选的，包含了各种光照条件、不同角度和遮挡情况下的车牌图像，这对于提高模型在现实世界中检测的准确性和鲁棒性至关重要。在深度学习中，训练模型需要大量的标注数据。一个高质量的数据集对于模型能否成功学习到目标检测中的关键特征是至关重要的。CCPD2019车牌数据集提供的10000张图片以及它们的YOLO格式标签，能够为模型提供充足的训练材料。这些标签通常都是由人工仔细标注，确保了标注的质量和准确性。深度学习在车牌识别系统中的应用是多方面的，不仅包括检测车牌的位置，还包括车牌字符的分割和识别。在实际应用中，经过训练的YOLO模型首先对输入图像中的车牌区域进行定位，然后可以通过其他专门设计的深度学习模型对车牌上的文字进行识别。 CCPD2019车牌数据集对于研究和开发车牌识别技术具有重要价值，它有助于推动计算机视觉领域特别是车牌检测和识别技术的发展。通过深度学习，特别是使用YOLO这样的先进算法，可以实现车牌的快速准确识别，这对于智能交通管理、车辆安全监控和交通流量分析等应用具有十分重要的意义。

【精准温度测量与HP303B校准】：掌握这些高级技巧，提升测量准确性

# 摘要本文系统地探讨了精准温度测量的科学原理，并对HP303B温度传感器的特性、技术参数、校准准备和校准步骤进行了详细介绍。文章进一步讨论了提


那如果我加上turbulenceProperties，是不是这么写FoamFile { version 2.0; format ascii; class dictionary; object turbulenceProperties; } // * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // simulationType laminar;

<think>好的，用户现在的问题是询问如果在cavity算例中添加turbulenceProperties文件，是否可以按照他提供的格式编写，特别是simulationType设置为laminar的情况。我需要确认这种写法是否正确，并解释相关细节。首先，用户提供的FoamFile部分是标准的OpenFOAM字典文件头，版本、格式、类和对象名都是正确的。然后，simulationType设置为laminar，这在层流模拟中是正确的配置。需要说明这样写是可行的，并且可能补充一些注意事项，比如是否必要，或者是否有其他参数需要考虑。接下来，用户可能想知道这样做的影响，比如是否会影响求解器的行

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那如果我加上turbulenceProperties，是不是这么写FoamFile { version 2.0; format ascii; class dictionary; object turbulenceProperties; } // * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // simulationType laminar;

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