Pytorch：需要一維目標張量，不支持多目標

Question

我想在時間序列上訓練一維 CNN。 我收到以下錯誤消息1D target tensor expected, multi-target not supported

這是帶有與我的數據結構相對應的模擬數據的代碼以及錯誤消息

import torch
from torch.utils.data import DataLoader
import torch.utils.data as data
import torch.nn as nn

import numpy as np
import random
from tqdm.notebook import tqdm

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)


train_dataset = []
n_item = 20
for i in range(0,n_item):
    train_data = np.random.uniform(-10, 10, 500)
    train_dataset.append(train_data)
train_dataset = np.asarray(train_dataset)
train_dataset.shape
ecg_train = torch.from_numpy(train_dataset).float()
labels_train = np.random.randint(2, size=n_item)
labels_train = torch.from_numpy(labels_train).long()


val_dataset = []
n_item = 10
for i in range(0,n_item):
    val_data = np.random.uniform(-10, 10, 500)
    val_dataset.append(val_data)
val_dataset = np.asarray(val_dataset)
val_dataset.shape
ecg_validation = torch.from_numpy(val_dataset).float()
labels_validation = np.random.randint(2, size=n_item)
labels_validation = torch.from_numpy(labels_validation).long()

class ECGNet(data.Dataset):
    """ImageNet Limited dataset."""
    
    def __init__(self, ecgs, labls, transform=None):
            self.ecg = ecgs
            self.target = labls
            self.transform = transform

    def __getitem__(self, idx):
        ecgVec = self.ecg[idx] #.reshape(10, -1)
        labelID = self.target[idx].reshape(1)

        return ecgVec,labelID

    def __len__(self):
        return len(self.ecg)


train_data = ECGNet(ecg_train, 
                             labels_train, 
                   )
print("size of Training dataset: {}".format(len(train_data)))

validation_data = ECGNet(ecg_validation, 
                             labels_validation, 
                   )
print("size of Training dataset: {}".format(len(validation_data)))


batch_size = 1
train_dataloader = DataLoader(dataset = train_data,
                              batch_size=batch_size, 
                              shuffle = True, 
                              num_workers = 0)

val_dataloader = DataLoader(dataset = validation_data,
                              batch_size=batch_size, 
                              shuffle = True, 
                              num_workers = 0)


def train_epoch(model, train_dataloader, optimizer, loss_fn):
    losses = []
    correct_predictions = 0
    # Iterate mini batches over training dataset
    for images, labels in tqdm(train_dataloader):
        images = images.to(device)
    #labels = labels.squeeze_()
        labels = labels.to(device)

        #labels = labels.to(device=device, dtype=torch.int64)
        # Run predictions
        output = model(images)
        # Set gradients to zero
        optimizer.zero_grad()
        # Compute loss
        loss = loss_fn(output, labels)
        # Backpropagate (compute gradients)
        loss.backward()
        # Make an optimization step (update parameters)
        optimizer.step()
        # Log metrics
        losses.append(loss.item())
        predicted_labels = output.argmax(dim=1)
        correct_predictions += (predicted_labels == labels).sum().item()
    accuracy = 100.0 * correct_predictions / len(train_dataloader.dataset)
    # Return loss values for each iteration and accuracy
    mean_loss = np.array(losses).mean()
    return mean_loss, accuracy

def evaluate(model, dataloader, loss_fn):
    losses = []
    correct_predictions = 0
    with torch.no_grad():
        for images, labels in dataloader:
            images = images.to(device)
            #labels = labels.squeeze_()
            labels = labels.to(device=device, dtype=torch.int64)
            # Run predictions
            output = model(images)
            # Compute loss
            loss = loss_fn(output, labels)
            # Save metrics
            predicted_labels = output.argmax(dim=1)
            correct_predictions += (predicted_labels == labels).sum().item()
            losses.append(loss.item())
    mean_loss = np.array(losses).mean()
    accuracy = 100.0 * correct_predictions / len(dataloader.dataset)
    # Return mean loss and accuracy
    return mean_loss, accuracy

def train(model, train_dataloader, val_dataloader, optimizer, n_epochs, loss_function):
    # We will monitor loss functions as the training progresses
    train_losses = []
    val_losses = []
    train_accuracies = []
    val_accuracies = []

    for epoch in range(n_epochs):
        model.train()
        train_loss, train_accuracy = train_epoch(model, train_dataloader, optimizer, loss_fn)
        model.eval()
        val_loss, val_accuracy = evaluate(model, val_dataloader, loss_fn)
        train_losses.append(train_loss)
        val_losses.append(val_loss)
        train_accuracies.append(train_accuracy)
        val_accuracies.append(val_accuracy)
        print('Epoch {}/{}: train_loss: {:.4f},        train_accuracy: {:.4f}, val_loss: {:.4f},        val_accuracy: {:.4f}'.format(epoch+1, n_epochs,
                                     train_losses[-1],
                                     train_accuracies[-1],
                                     val_losses[-1],
                                     val_accuracies[-1]))
    return train_losses, val_losses, train_accuracies, val_accuracies


class Simple1DCNN(torch.nn.Module):
    def __init__(self):
        super(Simple1DCNN, self).__init__()
        self.layer1 = torch.nn.Conv1d(in_channels=50, 
                                      out_channels=20, 
                                      kernel_size=5, 
                                      stride=2)
        self.act1 = torch.nn.ReLU()
        self.layer2 = torch.nn.Conv1d(in_channels=20, 
                                      out_channels=10, 
                                      kernel_size=1)
        
        self.fc1 = nn.Linear(10* 3, 2)
    def forward(self, x):
        print(x.shape)
        x = x.view(1, 50,-1)
        print(x.shape)
        x = self.layer1(x)
        print(x.shape)
        x = self.act1(x)
        print(x.shape)
        x = self.layer2(x)
        print(x.shape)
        x = x.view(1,-1)
        print(x.shape)
        x = self.fc1(x)
        print(x.shape)
        print(x)
        
        return x



model_a = Simple1DCNN()
model_a = model_a.to(device)

criterion = nn.CrossEntropyLoss()

loss_fn = torch.nn.CrossEntropyLoss()
n_epochs_a = 50
learning_rate_a = 0.01
alpha_a = 1e-5
momentum_a = 0.9
optimizer = torch.optim.SGD(model_a.parameters(), 
                            momentum = momentum_a,
                            nesterov = True,
                            weight_decay = alpha_a,
                            lr=learning_rate_a)
train_losses_a, val_losses_a, train_acc_a, val_acc_a = train(model_a,
                                                             train_dataloader,
                                                             val_dataloader,
                                                             optimizer,
                                                             n_epochs_a,
                                                             loss_fn
                                                            )

錯誤信息：

cpu
size of Training dataset: 20
size of Training dataset: 10
  0%|          | 0/20 [00:00<?, ?it/s]
torch.Size([1, 500])
torch.Size([1, 50, 10])
torch.Size([1, 20, 3])
torch.Size([1, 20, 3])
torch.Size([1, 10, 3])
torch.Size([1, 30])
torch.Size([1, 2])
tensor([[ 0.5785, -1.0169]], grad_fn=<AddmmBackward>)
Traceback (most recent call last):
  File "SO_question.py", line 219, in <module>
    train_losses_a, val_losses_a, train_acc_a, val_acc_a = train(model_a,
  File "SO_question.py", line 137, in train
    train_loss, train_accuracy = train_epoch(model, train_dataloader, optimizer, loss_fn)
  File "SO_question.py", line 93, in train_epoch
    loss = loss_fn(output, labels)
  File "/Users/mymac/Documents/programming/python/mainenv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/Users/mymac/Documents/programming/python/mainenv/lib/python3.8/site-packages/torch/nn/modules/loss.py", line 961, in forward
    return F.cross_entropy(input, target, weight=self.weight,
  File "/Users/mymac/Documents/programming/python/mainenv/lib/python3.8/site-packages/torch/nn/functional.py", line 2468, in cross_entropy
    return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
  File "/Users/mymac/Documents/programming/python/mainenv/lib/python3.8/site-packages/torch/nn/functional.py", line 2264, in nll_loss
    ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: 1D target tensor expected, multi-target not supported

我究竟做錯了什么？

Answer 1

您正在使用nn.CrossEntropyLoss作為訓練的標准。 您正確地將標簽作為基本事實 class 的索引傳遞： 0 s 和1 s。 但是，正如錯誤消息所暗示的那樣，它需要是一維張量！

只需刪除ECGNet的__getitem__中的重塑：

def __getitem__(self, idx):
    ecgVec = self.ecg[idx]
    labelID = self.target[idx]
    return ecgVec,labelID

---

編輯

我想將batch_size增加到8 。 但是現在我得到了錯誤[...]

您正在進行大量廣播（展平），這肯定會影響批量大小。 作為一般經驗法則，永遠不要擺弄axis=0 。 例如，如果您的輸入形狀為(8, 500) ，那么在執行x.view(1, 50, -1)時就會遇到問題。 由於生成的張量將是(1, 50, 80) （所需的形狀應該是(8, 50, 10) ）。 相反，您可以使用x.view(x.size(0), 50, -1)進行廣播。

與x.view(1, -1) later down forward相同。 您希望將張量展平，但不應將其與批次一起展平，它們需要保持分開！ 使用torch.flatten更安全，但我更喜歡nn.Flatten ，它默認從axis=1扁平化到axis=-1 。

---

我個人的建議是從一個簡單的設置開始（沒有火車環等......）來驗證架構和中間 output 形狀。 然后，添加必要的邏輯來處理訓練。

class ECGNet(data.Dataset):
    """ImageNet Limited dataset."""
    
    def __init__(self, ecgs, labls, transform=None):
        self.ecg = ecgs
        self.target = labls
        self.transform = transform

    def __getitem__(self, idx):
        ecgVec = self.ecg[idx]
        labelID = self.target[idx]
        return ecgVec, labelID

    def __len__(self):
        return len(self.ecg)


class Simple1DCNN(nn.Module):
    def __init__(self):
        super(Simple1DCNN, self).__init__()
        self.layer1 = nn.Conv1d(in_channels=50, 
                                out_channels=20, 
                                kernel_size=5, 
                                stride=2)
        self.act1 = nn.ReLU()
        self.layer2 = nn.Conv1d(in_channels=20, 
                                out_channels=10, 
                                kernel_size=1)
        
        self.fc1 = nn.Linear(10*3, 2)
        self.flatten = nn.Flatten()

    def forward(self, x):
        x = x.view(x.size(0), 50, -1)
        x = self.layer1(x)
        x = self.act1(x)
        x = self.layer2(x)
        x = self.flatten(x)
        x = self.fc1(x)
        
        return x

batch_size = 8
train_data = ECGNet(ecg_train, labels_train)
train_dl = DataLoader(dataset=train_data,
                      batch_size=batch_size, 
                      shuffle=True,
                      num_workers=0)

model = Simple1DCNN()
criterion = nn.CrossEntropyLoss()

然后

>>> x, y = next(iter(train_dl))
>>> y_hat = model(x)

>>> y_hat.shape
torch.Size([8, 2])

另外，確保你的損失有效：

>>> criterion(y_hat, y)
tensor(..., grad_fn=<NllLossBackward>)