Datasets:

code-philia
/

mtpnet_text

# coding: UTF-8
import os
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import yaml
import json
from sklearn import metrics
import time
from tqdm import tqdm
import argparse

# 添加当前目录到路径
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

from model import TextRCNN as Model, Config
from dataset_utils import build_dataset, build_iterator, get_time_dif


def init_network(model, method='xavier', exclude='embedding', seed=123):
    """权重初始化，默认xavier"""
    for name, w in model.named_parameters():
        if exclude not in name:
            if 'weight' in name:
                if method == 'xavier':
                    nn.init.xavier_normal_(w)
                elif method == 'kaiming':
                    nn.init.kaiming_normal_(w)
                else:
                    nn.init.normal_(w)
            elif 'bias' in name:
                nn.init.constant_(w, 0)


def evaluate(model, data_iter, device):
    """评估函数"""
    model.eval()
    loss_total = 0
    predict_all = np.array([], dtype=int)
    labels_all = np.array([], dtype=int)
    with torch.no_grad():
        for texts, labels in data_iter:
            outputs = model(texts)
            loss = F.cross_entropy(outputs, labels)
            loss_total += loss
            labels = labels.data.cpu().numpy()
            predic = torch.max(outputs.data, 1)[1].cpu().numpy()
            labels_all = np.append(labels_all, labels)
            predict_all = np.append(predict_all, predic)

    acc = metrics.accuracy_score(labels_all, predict_all)
    return acc, loss_total / len(data_iter)


def save_epoch_data(model, train_data, dev_data, test_data, config, epoch, save_dir, device):
    """保存每个epoch的模型、特征向量和预测值
    按顺序保存train_data、dev_data、test_data的特征向量
    
    Args:
        model: 训练好的模型
        train_data: 原始训练数据集
        dev_data: 原始验证数据集
        test_data: 原始测试数据集
        config: 配置对象
        epoch: 当前epoch数
        save_dir: 保存目录
        device: 设备
    """
    epoch_dir = os.path.join(save_dir, f'epoch_{epoch}')
    os.makedirs(epoch_dir, exist_ok=True)
    
    # 保存模型
    model_path = os.path.join(epoch_dir, 'model.pt')
    if hasattr(model, 'module'):  # 多GPU情况
        torch.save(model.module.state_dict(), model_path)
    else:
        torch.save(model.state_dict(), model_path)
    
    # 重新创建数据迭代器以保证顺序一致
    from dataset_utils import build_iterator
    
    # 按顺序处理三个数据集
    datasets = [
        ('train', train_data),
        ('dev', dev_data), 
        ('test', test_data)
    ]
    
    model.eval()
    all_features = []
    all_predictions = []
    
    if epoch == 1:
        # 保存数据集索引信息 index.json
        index_data = {}
        start_idx = 0
        for name,dataset in datasets:
            # 生成该数据集在embedding中的索引列表
            indices = list(range(start_idx, start_idx + len(dataset)))
            index_data[name] = indices
            start_idx += len(dataset)
        
        with open(os.path.join("../dataset", "index.json"), "w", encoding="utf-8") as f:
            json.dump(index_data, f, ensure_ascii=False, indent=2)
        
        # 保存模型信息 info.json
        info_data = {
            "model": "TextRCNN",
            "classes": config.class_list
        }
        
        with open(os.path.join("../dataset", 'info.json'), 'w', encoding='utf-8') as f:
            json.dump(info_data, f, ensure_ascii=False, indent=2)
        
        print("  - ✓ 已保存 index.json 和 info.json")
        print("    index.json: 包含各数据集的索引映射")
        print(f"    info.json: 模型={info_data['model']}, 类别数={len(info_data['classes'])}")

    print(f"正在提取epoch {epoch}的特征向量（按train/dev/test顺序）...")
    
    with torch.no_grad():
        for dataset_name, dataset in datasets:
            print(f"  正在处理 {dataset_name} 数据集 ({len(dataset)} 个样本)...")
            
            # 为每个数据集创建新的迭代器
            data_iter = build_iterator(dataset, config)
            
            dataset_features = []
            dataset_predictions = []
            
            for batch_idx, (texts, labels) in enumerate(tqdm(data_iter, desc=f"提取{dataset_name}特征")):
                # 获取特征向量
                if hasattr(model, 'module'):
                    features = model.module.feature(texts)
                    predictions = model.module.get_prediction(texts)
                else:
                    features = model.feature(texts)
                    predictions = model.get_prediction(texts)
                
                dataset_features.append(features)
                dataset_predictions.append(predictions)
            
            # 合并当前数据集的特征
            if dataset_features:  # 检查是否为空
                dataset_embeddings = np.vstack(dataset_features)
                dataset_preds = np.vstack(dataset_predictions)
                
                all_features.append(dataset_embeddings)
                all_predictions.append(dataset_preds)
                
                print(f"    {dataset_name} 特征形状: {dataset_embeddings.shape}")
    
    # 合并所有数据集的特征
    if all_features:
        embeddings = np.vstack(all_features)
        predictions = np.vstack(all_predictions)
        
        # 保存特征向量和预测值
        np.save(os.path.join(epoch_dir, 'embeddings.npy'), embeddings)
        np.save(os.path.join(epoch_dir, 'predictions.npy'), predictions)
        
        print(f"Epoch {epoch} 数据已保存到 {epoch_dir}")
        print(f"  - 特征向量形状: {embeddings.shape}")
        print(f"  - 输出向量形状: {predictions.shape}")
    else:
        print("警告：没有提取到任何特征数据")


def train(config_path):
    """训练主函数"""
    # 加载配置
    with open(config_path, 'r', encoding='utf-8') as f:
        train_config = yaml.safe_load(f)
    
    # 设置GPU设备
    gpu_ids = train_config.get('gpu_ids', [0])
    if not isinstance(gpu_ids, list):
        gpu_ids = [gpu_ids]
    
    # 检查GPU可用性
    if not torch.cuda.is_available():
        print("CUDA不可用，使用CPU")
        device = torch.device('cpu')
        gpu_ids = []
    else:
        available_gpus = torch.cuda.device_count()
        print(f"可用GPU数量: {available_gpus}")
        
        # 验证指定的GPU ID是否有效
        valid_gpu_ids = [gpu_id for gpu_id in gpu_ids if 0 <= gpu_id < available_gpus]
        if not valid_gpu_ids:
            print(f"警告：指定的GPU ID {gpu_ids} 无效，使用GPU 0")
            valid_gpu_ids = [0]
        
        gpu_ids = valid_gpu_ids
        device = torch.device(f'cuda:{gpu_ids[0]}')
        
    print(f"使用设备: {device}")
    print(f"指定GPU ID: {gpu_ids}")
    
    # 初始化配置
    config = Config(train_config['dataset_path'], train_config.get('embedding', 'random'))
    config.num_epochs = train_config.get('num_epochs', 20)
    config.batch_size = train_config.get('batch_size', 128)
    config.learning_rate = train_config.get('learning_rate', 1e-3)
    
    # 构建数据集
    print("构建数据集...")
    vocab, train_data, dev_data, test_data = build_dataset(config, train_config.get('use_word', False))
    config.n_vocab = len(vocab)
    
    # 更新设备配置
    config.device = device
    
    # 构建数据迭代器
    train_iter = build_iterator(train_data, config)
    dev_iter = build_iterator(dev_data, config)
    test_iter = build_iterator(test_data, config)
    
    # 初始化模型
    model = Model(config)
    
    # GPU训练设置
    if len(gpu_ids) > 1 and torch.cuda.is_available():
        try:
            print(f"尝试使用多GPU训练: {gpu_ids}")
            model = nn.DataParallel(model, device_ids=gpu_ids)
            print("✓ 多GPU训练模式已启用")
        except Exception as e:
            print(f"⚠️  多GPU初始化失败: {e}")
            print("回退到单GPU训练模式")
            # 回退到单GPU
            model = model.to(device)
    elif len(gpu_ids) == 1 and torch.cuda.is_available():
        print(f"使用单GPU训练模式: GPU {gpu_ids[0]}")
        model = model.to(device)
    else:
        print("使用CPU训练模式")
        model = model.to(device)
    
    # 初始化网络权重
    init_network(model)
    
    # 优化器
    optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
    
    # 训练
    print("开始训练...")
    start_time = time.time()
    total_batch = 0
    dev_best_loss = float('inf')
    # 早停
    # last_improve = 0
    # flag = False
    
    epochs_dir = train_config.get('epochs_dir', '../epochs')
    
    for epoch in range(config.num_epochs):
        print(f'Epoch [{epoch + 1}/{config.num_epochs}]')
        model.train()
        
        epoch_loss = 0
        epoch_acc = 0
        batch_count = 0
        
        for i, (trains, labels) in enumerate(tqdm(train_iter, desc=f"训练 Epoch {epoch+1}")):
            try:
                outputs = model(trains)
                model.zero_grad()
                loss = F.cross_entropy(outputs, labels)
                loss.backward()
                optimizer.step()
            except RuntimeError as e:
                if "NCCL" in str(e) or "cuda" in str(e).lower():
                    print(f"\n❌ 多GPU训练错误: {e}")
                    print("建议在配置文件中设置 force_single_gpu: true")
                    return
                else:
                    raise e
            
            epoch_loss += loss.item()
            
            # 计算准确率
            true = labels.data.cpu()
            predic = torch.max(outputs.data, 1)[1].cpu()
            train_acc = metrics.accuracy_score(true, predic)
            epoch_acc += train_acc
            batch_count += 1
            
            if total_batch % 100 == 0:
                # 每100轮输出在训练集和验证集上的效果
                try:
                    dev_acc, dev_loss = evaluate(model, dev_iter, device)
                except RuntimeError as e:
                    if "NCCL" in str(e) or "cuda" in str(e).lower():
                        print(f"\n❌ 验证过程多GPU错误: {e}")
                        print("建议在配置文件中设置 force_single_gpu: true")
                        return
                    else:
                        raise e
                if dev_loss < dev_best_loss:
                    dev_best_loss = dev_loss
                    improve = '*'
                    # last_improve = total_batch
                else:
                    improve = ''
                
                time_dif = get_time_dif(start_time)
                msg = 'Iter: {0:>6},  Train Loss: {1:>5.2},  Train Acc: {2:>6.2%},  Val Loss: {3:>5.2},  Val Acc: {4:>6.2%},  Time: {5} {6}'
                print(msg.format(total_batch, loss.item(), train_acc, dev_loss, dev_acc, time_dif, improve))
                model.train()
                
            total_batch += 1
            # if total_batch - last_improve > config.require_improvement:
            #     print("长时间无改进，提前停止训练...")
            #     flag = True
            #     break
                
        # if flag:
        #     break
            
        # 每个epoch结束后保存数据
        print(f"保存 Epoch {epoch+1} 的数据...")
        try:
            # 顺序保存train_data、dev_data、test_data
            save_epoch_data(model, train_data, dev_data, test_data, config, epoch+1, epochs_dir, device)
        except RuntimeError as e:
            if "NCCL" in str(e) or "cuda" in str(e).lower():
                print(f"\n❌ 保存数据时多GPU错误: {e}")
                print("建议在配置文件中设置 gpu_ids: [0]")
                return
            else:
                raise e
        
        # 输出epoch统计信息
        avg_loss = epoch_loss / batch_count
        avg_acc = epoch_acc / batch_count
        print(f"Epoch {epoch+1} - 平均损失: {avg_loss:.4f}, 平均准确率: {avg_acc:.4f}")
    
    # 最终测试
    print("进行最终测试...")
    model.eval()
    test_acc, test_loss = evaluate(model, test_iter, device)
    print(f"最终测试结果 - 损失: {test_loss:.4f}, 准确率: {test_acc:.4f}")
    
    total_time = get_time_dif(start_time)
    print(f"总训练时间: {total_time}")


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="TextRCNN训练脚本")
    parser.add_argument('--config', type=str, default='train.yaml', help='训练配置文件路径')
    args = parser.parse_args()
    
    train(args.config)

	# coding: UTF-8
	import os
	import sys
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import numpy as np
	import yaml
	import json
	from sklearn import metrics
	import time
	from tqdm import tqdm
	import argparse

	# 添加当前目录到路径
	sys.path.append(os.path.dirname(os.path.abspath(__file__)))

	from model import TextRCNN as Model, Config
	from dataset_utils import build_dataset, build_iterator, get_time_dif


	def init_network(model, method='xavier', exclude='embedding', seed=123):
	"""权重初始化，默认xavier"""
	for name, w in model.named_parameters():
	if exclude not in name:
	if 'weight' in name:
	if method == 'xavier':
	nn.init.xavier_normal_(w)
	elif method == 'kaiming':
	nn.init.kaiming_normal_(w)
	else:
	nn.init.normal_(w)
	elif 'bias' in name:
	nn.init.constant_(w, 0)


	def evaluate(model, data_iter, device):
	"""评估函数"""
	model.eval()
	loss_total = 0
	predict_all = np.array([], dtype=int)
	labels_all = np.array([], dtype=int)
	with torch.no_grad():
	for texts, labels in data_iter:
	outputs = model(texts)
	loss = F.cross_entropy(outputs, labels)
	loss_total += loss
	labels = labels.data.cpu().numpy()
	predic = torch.max(outputs.data, 1)[1].cpu().numpy()
	labels_all = np.append(labels_all, labels)
	predict_all = np.append(predict_all, predic)

	acc = metrics.accuracy_score(labels_all, predict_all)
	return acc, loss_total / len(data_iter)


	def save_epoch_data(model, train_data, dev_data, test_data, config, epoch, save_dir, device):
	"""保存每个epoch的模型、特征向量和预测值
	按顺序保存train_data、dev_data、test_data的特征向量

	Args:
	model: 训练好的模型
	train_data: 原始训练数据集
	dev_data: 原始验证数据集
	test_data: 原始测试数据集
	config: 配置对象
	epoch: 当前epoch数
	save_dir: 保存目录
	device: 设备
	"""
	epoch_dir = os.path.join(save_dir, f'epoch_{epoch}')
	os.makedirs(epoch_dir, exist_ok=True)

	# 保存模型
	model_path = os.path.join(epoch_dir, 'model.pt')
	if hasattr(model, 'module'): # 多GPU情况
	torch.save(model.module.state_dict(), model_path)
	else:
	torch.save(model.state_dict(), model_path)

	# 重新创建数据迭代器以保证顺序一致
	from dataset_utils import build_iterator

	# 按顺序处理三个数据集
	datasets = [
	('train', train_data),
	('dev', dev_data),
	('test', test_data)
	]

	model.eval()
	all_features = []
	all_predictions = []

	if epoch == 1:
	# 保存数据集索引信息 index.json
	index_data = {}
	start_idx = 0
	for name,dataset in datasets:
	# 生成该数据集在embedding中的索引列表
	indices = list(range(start_idx, start_idx + len(dataset)))
	index_data[name] = indices
	start_idx += len(dataset)

	with open(os.path.join("../dataset", "index.json"), "w", encoding="utf-8") as f:
	json.dump(index_data, f, ensure_ascii=False, indent=2)

	# 保存模型信息 info.json
	info_data = {
	"model": "TextRCNN",
	"classes": config.class_list
	}

	with open(os.path.join("../dataset", 'info.json'), 'w', encoding='utf-8') as f:
	json.dump(info_data, f, ensure_ascii=False, indent=2)

	print(" - ✓ 已保存 index.json 和 info.json")
	print(" index.json: 包含各数据集的索引映射")
	print(f" info.json: 模型={info_data['model']}, 类别数={len(info_data['classes'])}")

	print(f"正在提取epoch {epoch}的特征向量（按train/dev/test顺序）...")

	with torch.no_grad():
	for dataset_name, dataset in datasets:
	print(f" 正在处理 {dataset_name} 数据集 ({len(dataset)} 个样本)...")

	# 为每个数据集创建新的迭代器
	data_iter = build_iterator(dataset, config)

	dataset_features = []
	dataset_predictions = []

	for batch_idx, (texts, labels) in enumerate(tqdm(data_iter, desc=f"提取{dataset_name}特征")):
	# 获取特征向量
	if hasattr(model, 'module'):
	features = model.module.feature(texts)
	predictions = model.module.get_prediction(texts)
	else:
	features = model.feature(texts)
	predictions = model.get_prediction(texts)

	dataset_features.append(features)
	dataset_predictions.append(predictions)

	# 合并当前数据集的特征
	if dataset_features: # 检查是否为空
	dataset_embeddings = np.vstack(dataset_features)
	dataset_preds = np.vstack(dataset_predictions)

	all_features.append(dataset_embeddings)
	all_predictions.append(dataset_preds)

	print(f" {dataset_name} 特征形状: {dataset_embeddings.shape}")

	# 合并所有数据集的特征
	if all_features:
	embeddings = np.vstack(all_features)
	predictions = np.vstack(all_predictions)

	# 保存特征向量和预测值
	np.save(os.path.join(epoch_dir, 'embeddings.npy'), embeddings)
	np.save(os.path.join(epoch_dir, 'predictions.npy'), predictions)

	print(f"Epoch {epoch} 数据已保存到 {epoch_dir}")
	print(f" - 特征向量形状: {embeddings.shape}")
	print(f" - 输出向量形状: {predictions.shape}")
	else:
	print("警告：没有提取到任何特征数据")


	def train(config_path):
	"""训练主函数"""
	# 加载配置
	with open(config_path, 'r', encoding='utf-8') as f:
	train_config = yaml.safe_load(f)

	# 设置GPU设备
	gpu_ids = train_config.get('gpu_ids', [0])
	if not isinstance(gpu_ids, list):
	gpu_ids = [gpu_ids]

	# 检查GPU可用性
	if not torch.cuda.is_available():
	print("CUDA不可用，使用CPU")
	device = torch.device('cpu')
	gpu_ids = []
	else:
	available_gpus = torch.cuda.device_count()
	print(f"可用GPU数量: {available_gpus}")

	# 验证指定的GPU ID是否有效
	valid_gpu_ids = [gpu_id for gpu_id in gpu_ids if 0 <= gpu_id < available_gpus]
	if not valid_gpu_ids:
	print(f"警告：指定的GPU ID {gpu_ids} 无效，使用GPU 0")
	valid_gpu_ids = [0]

	gpu_ids = valid_gpu_ids
	device = torch.device(f'cuda:{gpu_ids[0]}')

	print(f"使用设备: {device}")
	print(f"指定GPU ID: {gpu_ids}")

	# 初始化配置
	config = Config(train_config['dataset_path'], train_config.get('embedding', 'random'))
	config.num_epochs = train_config.get('num_epochs', 20)
	config.batch_size = train_config.get('batch_size', 128)
	config.learning_rate = train_config.get('learning_rate', 1e-3)

	# 构建数据集
	print("构建数据集...")
	vocab, train_data, dev_data, test_data = build_dataset(config, train_config.get('use_word', False))
	config.n_vocab = len(vocab)

	# 更新设备配置
	config.device = device

	# 构建数据迭代器
	train_iter = build_iterator(train_data, config)
	dev_iter = build_iterator(dev_data, config)
	test_iter = build_iterator(test_data, config)

	# 初始化模型
	model = Model(config)

	# GPU训练设置
	if len(gpu_ids) > 1 and torch.cuda.is_available():
	try:
	print(f"尝试使用多GPU训练: {gpu_ids}")
	model = nn.DataParallel(model, device_ids=gpu_ids)
	print("✓ 多GPU训练模式已启用")
	except Exception as e:
	print(f"⚠️ 多GPU初始化失败: {e}")
	print("回退到单GPU训练模式")
	# 回退到单GPU
	model = model.to(device)
	elif len(gpu_ids) == 1 and torch.cuda.is_available():
	print(f"使用单GPU训练模式: GPU {gpu_ids[0]}")
	model = model.to(device)
	else:
	print("使用CPU训练模式")
	model = model.to(device)

	# 初始化网络权重
	init_network(model)

	# 优化器
	optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)

	# 训练
	print("开始训练...")
	start_time = time.time()
	total_batch = 0
	dev_best_loss = float('inf')
	# 早停
	# last_improve = 0
	# flag = False

	epochs_dir = train_config.get('epochs_dir', '../epochs')

	for epoch in range(config.num_epochs):
	print(f'Epoch [{epoch + 1}/{config.num_epochs}]')
	model.train()

	epoch_loss = 0
	epoch_acc = 0
	batch_count = 0

	for i, (trains, labels) in enumerate(tqdm(train_iter, desc=f"训练 Epoch {epoch+1}")):
	try:
	outputs = model(trains)
	model.zero_grad()
	loss = F.cross_entropy(outputs, labels)
	loss.backward()
	optimizer.step()
	except RuntimeError as e:
	if "NCCL" in str(e) or "cuda" in str(e).lower():
	print(f"\n❌ 多GPU训练错误: {e}")
	print("建议在配置文件中设置 force_single_gpu: true")
	return
	else:
	raise e

	epoch_loss += loss.item()

	# 计算准确率
	true = labels.data.cpu()
	predic = torch.max(outputs.data, 1)[1].cpu()
	train_acc = metrics.accuracy_score(true, predic)
	epoch_acc += train_acc
	batch_count += 1

	if total_batch % 100 == 0:
	# 每100轮输出在训练集和验证集上的效果
	try:
	dev_acc, dev_loss = evaluate(model, dev_iter, device)
	except RuntimeError as e:
	if "NCCL" in str(e) or "cuda" in str(e).lower():
	print(f"\n❌ 验证过程多GPU错误: {e}")
	print("建议在配置文件中设置 force_single_gpu: true")
	return
	else:
	raise e
	if dev_loss < dev_best_loss:
	dev_best_loss = dev_loss
	improve = '*'
	# last_improve = total_batch
	else:
	improve = ''

	time_dif = get_time_dif(start_time)
	msg = 'Iter: {0:>6}, Train Loss: {1:>5.2}, Train Acc: {2:>6.2%}, Val Loss: {3:>5.2}, Val Acc: {4:>6.2%}, Time: {5} {6}'
	print(msg.format(total_batch, loss.item(), train_acc, dev_loss, dev_acc, time_dif, improve))
	model.train()

	total_batch += 1
	# if total_batch - last_improve > config.require_improvement:
	# print("长时间无改进，提前停止训练...")
	# flag = True
	# break

	# if flag:
	# break

	# 每个epoch结束后保存数据
	print(f"保存 Epoch {epoch+1} 的数据...")
	try:
	# 顺序保存train_data、dev_data、test_data
	save_epoch_data(model, train_data, dev_data, test_data, config, epoch+1, epochs_dir, device)
	except RuntimeError as e:
	if "NCCL" in str(e) or "cuda" in str(e).lower():
	print(f"\n❌ 保存数据时多GPU错误: {e}")
	print("建议在配置文件中设置 gpu_ids: [0]")
	return
	else:
	raise e

	# 输出epoch统计信息
	avg_loss = epoch_loss / batch_count
	avg_acc = epoch_acc / batch_count
	print(f"Epoch {epoch+1} - 平均损失: {avg_loss:.4f}, 平均准确率: {avg_acc:.4f}")

	# 最终测试
	print("进行最终测试...")
	model.eval()
	test_acc, test_loss = evaluate(model, test_iter, device)
	print(f"最终测试结果 - 损失: {test_loss:.4f}, 准确率: {test_acc:.4f}")

	total_time = get_time_dif(start_time)
	print(f"总训练时间: {total_time}")


	if __name__ == '__main__':
	parser = argparse.ArgumentParser(description="TextRCNN训练脚本")
	parser.add_argument('--config', type=str, default='train.yaml', help='训练配置文件路径')
	args = parser.parse_args()

	train(args.config)