IndexError：尺寸超出范圍 - PyTorch 尺寸預計在 [-1, 0] 范圍內，但得到 1

Question

Despite already numerous answers on this very topic, failing to see in the example below (extract fromhttps://gist.github.com/lirnli/c16ef186c75588e705d9864fb816a13c on Variational Recurrent Networks) which input and output dimensions trigger the error.

嘗試更改torch.cat中的尺寸並抑制對squeeze()的調用后，錯誤仍然存在，

<ipython-input-51-cdc928891ad7> in generate(self, hidden, temperature)
     56         x_sample = x = x_out.div(temperature).exp().multinomial(1).squeeze()
     57         x = self.phi_x(x)
---> 58         tc = torch.cat([x,z], dim=1)
     59 
     60         hidden_next = self.rnn(tc,hidden)

IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

因此如何在tc = torch.cat([x,z], dim=1)中塑造x和z的尺寸？ 注意代碼如下，

import torch
from torch import nn, optim
from torch.autograd import Variable

class VRNNCell(nn.Module):
    def __init__(self):
        super(VRNNCell,self).__init__()
        self.phi_x = nn.Sequential(nn.Embedding(128,64), nn.Linear(64,64), nn.ELU())
        self.encoder = nn.Linear(128,64*2) # output hyperparameters
        self.phi_z = nn.Sequential(nn.Linear(64,64), nn.ELU())
        self.decoder = nn.Linear(128,128) # logits
        self.prior = nn.Linear(64,64*2) # output hyperparameters
        self.rnn = nn.GRUCell(128,64)

    def forward(self, x, hidden):
        x = self.phi_x(x)
        # 1. h => z
        z_prior = self.prior(hidden)
        # 2. x + h => z
        z_infer = self.encoder(torch.cat([x,hidden], dim=1))
        # sampling
        z = Variable(torch.randn(x.size(0),64))*z_infer[:,64:].exp()+z_infer[:,:64]
        z = self.phi_z(z)
        # 3. h + z => x
        x_out = self.decoder(torch.cat([hidden, z], dim=1))
        # 4. x + z => h
        hidden_next = self.rnn(torch.cat([x,z], dim=1),hidden)
        return x_out, hidden_next, z_prior, z_infer

    def calculate_loss(self, x, hidden):
        x_out, hidden_next, z_prior, z_infer = self.forward(x, hidden)
        # 1. logistic regression loss
        loss1 = nn.functional.cross_entropy(x_out, x) 
        # 2. KL Divergence between Multivariate Gaussian
        mu_infer, log_sigma_infer = z_infer[:,:64], z_infer[:,64:]
        mu_prior, log_sigma_prior = z_prior[:,:64], z_prior[:,64:]
        loss2 = (2*(log_sigma_infer-log_sigma_prior)).exp() \
                + ((mu_infer-mu_prior)/log_sigma_prior.exp())**2 \
                - 2*(log_sigma_infer-log_sigma_prior) - 1
        loss2 = 0.5*loss2.sum(dim=1).mean()
        return loss1, loss2, hidden_next
    
    def generate(self, hidden=None, temperature=None):
        if hidden is None:
            hidden=Variable(torch.zeros(1,64))
        if temperature is None:
            temperature = 0.8
        # 1. h => z
        z_prior = self.prior(hidden)
        # sampling
        z = Variable(torch.randn(z_prior.size(0),64))*z_prior[:,64:].exp()+z_prior[:,:64]
        z = self.phi_z(z)
        # 2. h + z => x
        x_out = self.decoder(torch.cat([hidden, z], dim=1))
        # sampling
        x_sample = x = x_out.div(temperature).exp().multinomial(1).squeeze()
        x = self.phi_x(x)
        # 3. x + z => h
        # hidden_next = self.rnn(torch.cat([x,z], dim=1),hidden)
        tc = torch.cat([x,z], dim=1)
        hidden_next = self.rnn(tc,hidden)
        return x_sample, hidden_next
    
    def generate_text(self, hidden=None,temperature=None, n=100):
        res = []
        hidden = None
        for _ in range(n):
            x_sample, hidden = self.generate(hidden,temperature)
            res.append(chr(x_sample.data[0]))
        return "".join(res)
        

# Test
net = VRNNCell()
x = Variable(torch.LongTensor([12,13,14]))
hidden = Variable(torch.rand(3,64))
output, hidden_next, z_infer, z_prior = net(x, hidden)
loss1, loss2, _ = net.calculate_loss(x, hidden)
loss1, loss2

hidden = Variable(torch.zeros(1,64))
net.generate_text()

Answer 1

錯誤

IndexError：維度超出范圍（預計在 [-1, 0] 范圍內，但得到 1）

意味着您正在嘗試訪問張量中不存在的索引。 例如，以下代碼會導致您遇到相同的IndexError 。

# sample input tensors
In [210]: x = torch.arange(4)
In [211]: z = torch.arange(6)

# trying to concatenate along the second dimension 
# but the tensors have only one dimension (i.e., `0`).

In [212]: torch.cat([x, z], dim=1)

所以，克服這個問題的一種方法是在連接之前將張量提升到更高的維度，如果這是你需要的。

# promoting tensors to 2D before concatenation
In [216]: torch.cat([x[None, :], z[None, :]], dim=1)
Out[216]: tensor([[0, 1, 2, 3, 0, 1, 2, 3, 4, 5]])

因此，在您的情況下，您必須分析和理解x需要什么形狀，以便它可以沿維度 1 與z連接，然后將tc作為輸入傳遞給self.rnn()以及hidden 。

據我所見， x[None, :] ， z[None, :]應該可以工作。

---

調試成功訓練

您發布的代碼是為 PyTorch v0.4.1編寫的。 從那時起，PyTorch Python API 發生了很多變化，但代碼沒有更新。

以下是使代碼成功運行和訓練所需的更改。 復制以下函數並將其粘貼到代碼中的適當位置。

def generate(self, hidden=None, temperature=None):
        if hidden is None:
            hidden=Variable(torch.zeros(1,64))
        if temperature is None:
            temperature = 0.8
        # 1. h => z
        z_prior = self.prior(hidden)
        # sampling
        z = Variable(torch.randn(z_prior.size(0),64))*z_prior[:,64:].exp()+z_prior[:,:64]
        z = self.phi_z(z)
        # 2. h + z => x
        x_out = self.decoder(torch.cat([hidden, z], dim=1))
        # sampling
        x_sample = x = x_out.div(temperature).exp().multinomial(1).squeeze()
        x = self.phi_x(x)
        # 3. x + z => h
        x = x[None, ...]   # changed here
        xz = torch.cat([x,z], dim=1)  # changed here
        hidden_next = self.rnn(xz,hidden) # changed here
        return x_sample, hidden_next

---

def generate_text(self, hidden=None,temperature=None, n=100):
        res = []
        hidden = None
        for _ in range(n):
            x_sample, hidden = self.generate(hidden,temperature)
            res.append(chr(x_sample.data))      # changed here
        return "".join(res)

---

for epoch in range(max_epoch):
    batch = next(g)
    loss_seq = 0
    loss1_seq, loss2_seq = 0, 0
    optimizer.zero_grad()
    for x in batch:
        loss1, loss2, hidden = net.calculate_loss(Variable(x),hidden)
        loss1_seq += loss1.data  # changed here
        loss2_seq += loss2.data  # changed here
        loss_seq = loss_seq + loss1+loss2
    loss_seq.backward()
    optimizer.step()
    hidden.detach_()
    if epoch%100==0:
        print('>> epoch {}, loss {:12.4f}, decoder loss {:12.4f}, latent loss {:12.4f}'.format(epoch, loss_seq.data, loss1_seq, loss2_seq))  # changed here
        print(net.generate_text())
        print()

---

注意：在這些更改之后，我的訓練循環在 PyTorch v1.7.1上繼續進行，沒有任何錯誤。 查看帶有# changed here的評論以了解更改。