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import sys
import os
import yaml
from pathlib import Path
import torch
import torch.nn as nn
import torch.optim as optim
import time
import logging
import numpy as np
from tqdm import tqdm
from dataset_utils import get_cifar10_dataloaders
from model import ShuffleNetG2
from get_representation import time_travel_saver
def setup_logger(log_file):
"""配置日志记录器，如果日志文件存在则覆盖
Args:
log_file: 日志文件路径
Returns:
logger: 配置好的日志记录器
"""
# 创建logger
logger = logging.getLogger('train')
logger.setLevel(logging.INFO)
# 移除现有的处理器
if logger.hasHandlers():
logger.handlers.clear()
# 创建文件处理器，使用'w'模式覆盖现有文件
fh = logging.FileHandler(log_file, mode='w')
fh.setLevel(logging.INFO)
# 创建控制台处理器
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
# 创建格式器
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# 添加处理器
logger.addHandler(fh)
logger.addHandler(ch)
return logger
def train_model(model, trainloader, testloader, epochs=200, lr=0.1, device='cuda:0',
save_dir='./epochs', model_name='model', interval=1):
"""通用的模型训练函数
Args:
model: 要训练的模型
trainloader: 训练数据加载器
testloader: 测试数据加载器
epochs: 训练轮数
lr: 学习率
device: 训练设备，格式为'cuda:N'，其中N为GPU编号（0,1,2,3）
save_dir: 模型保存目录
model_name: 模型名称
interval: 模型保存间隔
"""
# 检查并设置GPU设备
if not torch.cuda.is_available():
print("CUDA不可用，将使用CPU训练")
device = 'cpu'
elif not device.startswith('cuda:'):
device = f'cuda:0'
# 确保device格式正确
if device.startswith('cuda:'):
gpu_id = int(device.split(':')[1])
if gpu_id >= torch.cuda.device_count():
print(f"GPU {gpu_id} 不可用，将使用GPU 0")
device = 'cuda:0'
# 设置保存目录
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# 设置日志文件路径
log_file = os.path.join(os.path.dirname(save_dir),'epochs', 'train.log')
if not os.path.exists(os.path.dirname(log_file)):
os.makedirs(os.path.dirname(log_file))
logger = setup_logger(log_file)
# 损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50)
# 移动模型到指定设备
model = model.to(device)
best_acc = 0
start_time = time.time()
logger.info(f'开始训练 {model_name}')
logger.info(f'总轮数: {epochs}, 学习率: {lr}, 设备: {device}')
for epoch in range(epochs):
# 训练阶段
model.train()
train_loss = 0
correct = 0
total = 0
train_pbar = tqdm(trainloader, desc=f'Epoch {epoch+1}/{epochs} [Train]')
for batch_idx, (inputs, targets) in enumerate(train_pbar):
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
# 更新进度条
train_pbar.set_postfix({
'loss': f'{train_loss/(batch_idx+1):.3f}',
'acc': f'{100.*correct/total:.2f}%'
})
# 保存训练阶段的准确率
train_acc = 100.*correct/total
train_correct = correct
train_total = total
# 测试阶段
model.eval()
test_loss = 0
correct = 0
total = 0
test_pbar = tqdm(testloader, desc=f'Epoch {epoch+1}/{epochs} [Test]')
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(test_pbar):
inputs, targets = inputs.to(device), targets.to(device)
outputs = model(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
# 更新进度条
test_pbar.set_postfix({
'loss': f'{test_loss/(batch_idx+1):.3f}',
'acc': f'{100.*correct/total:.2f}%'
})
# 计算测试精度
acc = 100.*correct/total
# 记录训练和测试的损失与准确率
logger.info(f'Epoch: {epoch+1} | Train Loss: {train_loss/(len(trainloader)):.3f} | Train Acc: {train_acc:.2f}% | '
f'Test Loss: {test_loss/(batch_idx+1):.3f} | Test Acc: {acc:.2f}%')
# 保存可视化训练过程所需要的文件
if (epoch + 1) % interval == 0 or (epoch == 0):
# 创建一个专门用于收集embedding的顺序dataloader
ordered_trainloader = torch.utils.data.DataLoader(
trainloader.dataset,
batch_size=trainloader.batch_size,
shuffle=False,
num_workers=trainloader.num_workers
)
epoch_save_dir = os.path.join(save_dir, f'epoch_{epoch+1}')
save_model = time_travel_saver(model, ordered_trainloader, device, epoch_save_dir, model_name,
show=True, layer_name='avg_pool', auto_save_embedding=True)
save_model.save_checkpoint_embeddings_predictions()
if epoch == 0:
save_model.save_lables_index(path = "../dataset")
scheduler.step()
logger.info('训练完成!')
def main():
# 加载配置文件
config_path = Path(__file__).parent / 'train.yaml'
with open(config_path) as f:
config = yaml.safe_load(f)
# 创建模型
model = ShuffleNetG2(num_classes=10)
# 获取数据加载器
trainloader, testloader = get_cifar10_dataloaders(
batch_size=128,
num_workers=2,
local_dataset_path=config['dataset_path'],
shuffle=True
)
# 训练模型
train_model(
model=model,
trainloader=trainloader,
testloader=testloader,
epochs=config['epochs'],
lr=config['lr'],
device=f'cuda:{config["gpu"]}',
save_dir='../epochs',
model_name='ShuffleNetG2',
interval=config['interval']
)
if __name__ == '__main__':
main() |