Error in VAE Using Image Input

Hi everyone. I am trying to create a VAE which takes in different images from a dataset as the input, but am getting an error.

Here is the code:
from import imread
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np
import torch
from torch import nn, optim
from torch.autograd import Variable
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from PIL import Image
import os
import ast
import numpy as np
import pywt
import matplotlib.pyplot as plt
from PIL import Image as PImage
SAVEDIR = “D:/bob/myfiles/image/output/”
class VAE(nn.Module):
def init(self, x_dim, h_dim, z_dim):
super(VAE, self).init()

    # Encoder
    self.fc1 = nn.Linear(x_dim, h_dim)
    self.fc2_mu = nn.Linear(h_dim, z_dim)
    self.fc2_logvar = nn.Linear(h_dim, z_dim)

    # Decoder
    self.fc3 = nn.Linear(z_dim, h_dim)
    self.fc4 = nn.Linear(h_dim, x_dim)

def encoder(self, x):
    h = F.relu(self.fc1(x))
    return self.fc2_mu(h), self.fc2_logvar(h)

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

def decoder(self, z):
    h = F.relu(self.fc3(z))
    return F.sigmoid(self.fc4(h))

def forward(self, x):
    mu, logvar = self.encoder(x.view(-1, 4096))
    z = self.sampling(mu, logvar)
    return self.decoder(z), mu, 

vae = VAE(x_dim=4096, h_dim=400, z_dim=20).to(device)
optimizer = optim.Adam(vae.parameters(), lr=0.001)

reconstruction error + KL divergence losses

def loss_function(recon_x, x, mu, logvar):
BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), reduction=‘sum’)
KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
return BCE + KLD
dirpath = os.listdir(IMAGEDIR)
def train(epoch):
train_loss = 0
for subdir in dirpath:
full_subdir= IMAGEDIR + ‘/’ + subdir + ‘/’
imageList = os.listdir(full_subdir)
for image in imageList:
fullpath = full_subdir + image
inputImage = imread(fullpath)[:,:]
recon_batch, mu, logvar = vae(inputImage)
loss = loss_function(recon_batch, inputImage, mu, logvar)
train_loss += loss.item()

When I try to run the code, this is the error I get:
ValueError: Type must be a sub-type of ndarray type
This comes from the line that says: mu, logvar = self.encoder(x.view(-1, 4096)) in the forward function. Does anyone have any solutions as to how I can resolve this issue so my code will run properly? Thanks!

Based on the error message you are trying to call view on a numpy array instead of a PyTorch tensor, which raises this error since these calls are not performing the same operation:

# works on a tensor
x = torch.randn(2, 2, 4096)
x.view(-1, 4096)

# fails on a numpy array
x = np.random.randn(2, 2, 4096)
x.view(-1, 4096)
# ValueError: Type must be a sub-type of ndarray type

Pass a tensor to the model and it should work.

Oh okay, thank you for the response!