从张量 Pytorch 中批量采样数据

Question

我从 trainX 中分离出 train_x 和 valid_x，从 trainY 中分离出 train_y 和 valid_y，它们的形状如下所示。我想对标签图像进行分类 LABELS = set(["Faces", "Leopards", "Motorbikes", "airplanes"]).

print(train_x.shape, len(train_y))
torch.Size([1339, 96, 96, 3]) 1339

print(valid_x.shape, len(valid_y))
torch.Size([335, 96, 96, 3]) 335

print(testX.shape, len(testY))
torch.Size([559, 96, 96, 3]) 559

所以我想在数据批处理代码上使用常规 train/valid，如下所示：

#train the network

n_epochs = 20

valid_loss = []
train_loss = []

for epoch in range(1,n_epochs+1):
    
    cur_train_loss = 0.0
    cur_valid_loss = 0.0
    
    #####################
    #### Train model ####
    #####################
    cnn_model.train()
    
    for data, target in trainLoader:
        if train_on_gpu:
            data, target = data.cuda(), target.cuda()
        optimizer.zero_grad()        
        output = cnn_model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        cur_train_loss += loss.item() * data.size(0)
        
    ########################
    #### Validate model ####
    ########################
    
    cnn_model.eval()
    
    for data, target in validLoader:
        if train_on_gpu:
            data, target = data.cuda(), target.cuda()
        output = cnn_model(data)
        loss = criterion(output, target)
        cur_valid_loss += loss.item() * data.size(0)
    
    # calculate avg loss
    avg_train_loss = cur_train_loss / len(trainLoader.sampler)
    avg_valid_loss = cur_valid_loss / len(validLoader.sampler)
    
    train_loss.append(avg_train_loss)
    valid_loss.append(avg_valid_loss)
    
    print('Epoch: {} \t train_loss: {:.6f} \t valid_loss: {:.6f}'.format(epoch, avg_train_loss, avg_valid_loss))

所以我必须为此做什么？我已经搜索过了，但没有找到具体的内容。我想为此使用 pytorch。我已经为另一个类似的问题建立了模型，但我使用 DataLoader 一次加载一批数据进行训练和验证。

Answer 1

您可以使用 torch.utils.data.TensorDataset 创建数据集，其中 train_x 的每个样本都与其在 train_y 中的相应标签相关联，这样 DataLoader 就可以创建批次就像你习惯的那样。

from torch.utils.data import DataLoader, TensorDataset

train_dataset = TensorDataset(train_x, train_y)
train_dataloader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)

valid_dataset = TensorDataset(valid_x, valid_y)
valid_dataloader = DataLoader(valid_dataset, batch_size=BATCH_SIZE, shuffle=False)

test_dataset = TensorDataset(testX, testY)
test_dataloader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)

从张量 Pytorch 中批量采样数据

Sampling data batch wise from tensor Pytorch

batch-processing

python-3.x

conv-neural-network

pytorch

dataloader