Question

以下代码中存在pytorch不匹配错误：

# Visualization
import matplotlib.pyplot as plt

digit_size = 28
z_sample = np.random.rand(1, latent_dim) # random
z_sample.dtype = 'float32'
z_sample = torch.tensor(z_sample) 
plt.figure(figsize=(20, 1))

for i in range(10):
    c = np.zeros((1, cond_dim), dtype='float32')
    c[i] = 1
    c = torch.tensor(c)
    cvae.eval()
    with torch.no_grad():
        img = cvae.decoder(z_sample, c)
        # reshape (if needed)

    plt.subplot(1, i+1)
    plt.axis('off')
    plt.imshow(img, cmap='Greys_r',)
plt.show()
##### test #####
print(np.size(z_sample))
print(latent_dim)

我定义的latent_dim等于2。我知道错误来自哪里。来自以下几行：

z_sample = np.random.rand(1, latent_dim) # random
z_sample.dtype = 'float32'
z_sample = torch.tensor(z_sample)

在第一行中，我定义了一个1 * 2随机向量但是在将其从“数组”更改为“张量”后，它给了我1 * 4向量，因此出现了不匹配错误。

谁知道我该如何解决？它也应该仍然是“ float32”。

编辑： CVAE是：

class CVAE(nn.Module):
    def __init__(self, x_dim, h_dim1, h_dim2, z_dim, c_dim):
        super(CVAE, self).__init__()

        # encoder part
        self.fc1 = nn.Linear(x_dim + c_dim, h_dim1)
        self.fc2 = nn.Linear(h_dim1, h_dim2)
        self.fc31 = nn.Linear(h_dim2, z_dim)
        self.fc32 = nn.Linear(h_dim2, z_dim)
        # decoder part
        self.fc4 = nn.Linear(z_dim + c_dim, h_dim2)
        self.fc5 = nn.Linear(h_dim2, h_dim1)
        self.fc6 = nn.Linear(h_dim1, x_dim)

    def encoder(self, x, c):
        concat_input = torch.cat((x, c), 1)
        h = F.relu(self.fc1(concat_input))
        h = F.relu(self.fc2(h))
        return self.fc31(h), self.fc32(h)

    def sampling(self, mu, log_var):
        std = torch.exp(0.5*log_var)
        eps = torch.randn_like(std)
        return eps.mul(std).add(mu) # return z sample

    def decoder(self, z, c):
        concat_input = torch.cat((z, c), 1)
        h = F.relu(self.fc4(concat_input))
        h = F.relu(self.fc5(h))
        return F.sigmoid(self.fc6(h))

    def forward(self, x, c):
        mu, log_var = self.encoder(x.view(-1, 784), c)
        z = self.sampling(mu, log_var)
        return self.decoder(z, c), mu, log_var

模型是：

# Create Model (change None)
cond_dim = train_loader.dataset.train_labels.unique().size(0)
latent_dim = 2
cvae = CVAE(x_dim=28*28, h_dim1=512, h_dim2=256, z_dim=latent_dim, c_dim=cond_dim)

# Device setting
cvae = cvae.to(device)

，错误是：

RuntimeError：大小不匹配，m1：[1 x 14]，m2：[12 x 256]，位于/pytorch/aten/src/TH/generic/THTensorMath.cpp:197

我该如何解决pytorch不匹配错误？

0 个答案: