Expected object of scalar type Double but got scalar type Float for argument #2 'weight'

username: liang_chen
cooked: <p>I met a problem with data type when transforming my own image training data to a <strong>torch.utils.data.Dataset</strong></p>
<p>The Dataset is defined as belows</p>
<pre><code class="lang-auto">class FaceDataset(torch.utils.data.Dataset):
    """
    读取本地动漫人脸数据
    """

    def __init__(self):
        data = np.loadtxt("Data1.csv",delimiter=',',skiprows=1,dtype='float32')
        self.torch_data = torch.from_numpy(data)
        self.len = data.shape[0]

    def __getitem__(self, index):
        return self.torch_data[index],1

    def __len__(self):
        return self.len
</code></pre>
<p>and the model</p>
<pre><code class="lang-auto">class CNN_Discriminator(nn.Module):
    def __init__(self):
        super(CNN_Discriminator, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2),  # batch, 32, 96，96,
            nn.LeakyReLU(0.2, True),
            nn.AvgPool2d(2, stride=2),  # batch, 32, 48, 48
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(32, 64, 5, padding=2),  # batch, 64, 48, 48
            nn.LeakyReLU(0.2, True),
            nn.AvgPool2d(2, stride=3)  # batch, 64, 16, 16
        )
        self.fc = nn.Sequential(
            nn.Linear(64 * 16 * 16, 1024),
            nn.LeakyReLU(0.2, True),
            nn.Linear(1024, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        '''
        x: batch, width, height, channel=3
        '''
        x = self.conv1(x)                       # !!!!   Code Went wrong Here!!
        x = self.conv2(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x
</code></pre>
<p>“main.py”   if needed</p>
<pre><code class="lang-auto">import torch
import numpy as np
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets
from torchvision import transforms
from torchvision.utils import save_image
from torch.autograd import Variable
import os
import model


def to_img(x):
    out = 0.5 * (x + 1)
    out = out.clamp(0, 1)
    out = out.view(-1, 3, 96, 96)
    return out

class FaceDataset(torch.utils.data.Dataset):
    """
    load imgs
    """

    def __init__(self):
        data = np.loadtxt("Data1.csv",delimiter=',',skiprows=1,dtype='float32')
        self.torch_data = torch.from_numpy(data)
        self.len = data.shape[0]

    def __getitem__(self, index):
        return self.torch_data[index],1

    def __len__(self):
        return self.len

batch_size = 2
num_epoch = 10
z_dimension = 512

dataSet = FaceDataset()

img_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])

dataloader = torch.utils.data.DataLoader(
    dataset=dataSet, batch_size=batch_size, shuffle=True,num_workers=8)

G = model.CNN_Generator(z_dimension,15*192*192)
D = model.CNN_Discriminator()

if torch.cuda.is_available():
    D = D.cuda()
    G = G.cuda()

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Binary cross entropy loss and optimizer
criterion = nn.BCELoss()
d_optimizer = torch.optim.Adam(D.parameters(), lr=0.0002)
g_optimizer = torch.optim.Adam(G.parameters(), lr=0.0002)

for epoch in range(num_epoch):
    for i, (img,_) in enumerate(dataloader):
        num_img = img.size(0)
        #train discriminator
        img = img.view(num_img,3,96,96)
        img = img.double()
        real_img = Variable(img).to(device)
        real_label = Variable(torch.ones(num_img)).to(device)
        fake_label = Variable(torch.zeros(num_img)).to(device)

        # compute loss of real_img
        real_out = D(real_img)                               #    !!!!!!Problem Here
        d_loss_real = criterion(real_out, real_label)
        real_scores = real_out  # closer to 1 means better

        # compute loss of fake_img
        z = Variable(torch.randn(num_img, z_dimension))#.cuda()
        fake_img = G(z)
        fake_out = D(fake_img)
        d_loss_fake = criterion(fake_out, fake_label)
        fake_scores = fake_out  # closer to 0 means better

        # bp and optimize
        d_loss = d_loss_real + d_loss_fake
        d_optimizer.zero_grad()
        d_loss.backward()
        d_optimizer.step()

        # ===============train generator
        # compute loss of fake_img
        z = Variable(torch.randn(num_img, z_dimension)).to(device)
        fake_img = G(z)
        output = D(fake_img)
        g_loss = criterion(output, real_label)

        # bp and optimize
        g_optimizer.zero_grad()
        g_loss.backward()
        g_optimizer.step()


        print('Epoch [{}/{}], Batch {}/{},d_loss: {:.6f}, g_loss: {:.6f} '
                  'D real: {:.6f}, D fake: {:.6f}'.format(
                epoch, num_epoch,i,dataSet.len/batch_size ,d_loss.data, g_loss.data,
                real_scores.data.mean(), fake_scores.data.mean()))
        if epoch == 0:
            real_images = to_img(real_img.cpu().data)
            save_image(real_images, './img/real_images.png')

        fake_images = to_img(fake_img.cpu().data)
        save_image(fake_images, './img/fake_images-{}.png'.format(epoch + 1))

torch.save(G.state_dict(), './generator.pth')
torch.save(D.state_dict(), './discriminator.pth')
</code></pre>

username: ptrblck
cooked: <p>numoy arrays use <code>float64</code> as their default type.<br>
In your <code>Dataset</code>, you could transform it to <code>float32</code> (which is the default type in PyTorch) via:</p>
<pre><code class="lang-python">self.torch_data = torch.from_numpy(data).float()
</code></pre>