for epoch in range(config.epochs): trainset = VideoDataset_images_with_motion_features(videos_dir, feature_dir, datainfo_train, transformations_train, 'test', config.crop_size, 'SlowFast') #testset = VideoDataset_images_with_motion_features(videos_dir, feature_dir, datainfo_test, transformations_test,'test', config.crop_size, 'SlowFast') #print((trainset.shape())) #trainsettemp=data_loader.VideoDataset_images_with_motion_features() #testsettemp=data_loader.VideoDataset_images_with_motion_features() kf = KFold(n_splits=10, shuffle=True) train_loader = torch.utils.data.DataLoader(trainset, batch_size=1, shuffle=False, num_workers=config.num_workers) for train_index, test_index in enumerate(kf.split(trainset)): print(train_index) #trainsettemp.append(trainset(train_index)) #testsettemp.append(trainset(test_index)) train_loader1 = torch.utils.data.Subset(train_loader,train_index) train_loader2=torch.utils.data.DataLoader(train_loader1,batch_size=1,shuffle=False,num_workers=config.num_workers)
时间: 2023-06-17 10:06:49 浏览: 65
这段代码是一个训练模型的循环。首先,它定义了一个数据集`trainset`,并使用`VideoDataset_images_with_motion_features`类从视频目录和特征目录中获取图像,然后将其转换为`SlowFast`格式。然后,它定义了一个`KFold`对象,将数据集分成10个折叠,并使用`torch.utils.data.DataLoader`类创建一个`train_loader`对象,用于加载训练数据。在每个循环中,使用`kf.split(trainset)`方法获取训练和测试数据的索引,并使用`torch.utils.data.Subset`类创建新的`train_loader1`对象,它仅包含训练数据的索引。最后,使用`torch.utils.data.DataLoader`类创建一个新的`train_loader2`对象,用于加载新的训练数据。
相关问题
epochs = 200 def train(train_loader): train_ls = [] for epoch in range(epochs): loss_sum = 0 for train_batch, labels_batch in train_loader: train_batch, labels_batch = train_batch.to(device), labels_batch.to(device)
这段代码看起来是一个训练模型的循环,循环了200个epochs。其中train_loader是一个数据加载器,用于加载训练数据集。在每个epoch中,代码会遍历train_loader中的每个batch并将其送入模型进行训练,同时计算每个batch的损失值。损失值的计算方法可能在代码的后续部分,这里无法确定。每个epoch的损失值会被累加到loss_sum中。最终训练结果可能是一个训练好的模型,或者是一些训练指标,比如损失或准确率等。
for epoch in range(num_epochs): model.train() train_loss = 0 train_mae = 0 for batch_data, batch_target in train_dataloader: optimizer.zero_grad() output = model(batch
_data) # 前向传播
loss = loss_fn(output, batch_target) # 计算loss
train_loss += loss.item()
mae = mean_absolute_error(output.detach().cpu().numpy(), batch_target.detach().cpu().numpy()) # 计算MAE
train_mae += mae
loss.backward() # 反向传播
optimizer.step() # 更新梯度
train_loss /= len(train_dataloader)
train_mae /= len(train_dataloader)
model.eval() # 切换到评估模式
eval_loss = 0
eval_mae = 0
with torch.no_grad():
for batch_data, batch_target in eval_dataloader:
output = model(batch_data)
loss = loss_fn(output, batch_target)
eval_loss += loss.item()
mae = mean_absolute_error(output.detach().cpu().numpy(), batch_target.detach().cpu().numpy())
eval_mae += mae
eval_loss /= len(eval_dataloader)
eval_mae /= len(eval_dataloader)
print(f"Epoch {epoch+1}/{num_epochs}, Train Loss: {train_loss:.4f}, Train MAE: {train_mae:.4f}, Eval Loss: {eval_loss:.4f}, Eval MAE: {eval_mae:.4f}")
if eval_loss < best_eval_loss:
best_eval_loss = eval_loss
torch.save(model.state_dict(), save_path)
print("Save model successfully!")