RuntimeError: Expected object of backend CPU but got backend CUDA for argument #4 'mat1'

Pranavan_Theivendira: <p>Hi Everyone,</p>
<p>I am getting the following error, when I try to run the program in a GPU.</p>
<pre><code class="lang-auto">Traceback (most recent call last):
  File "train_model.py", line 219, in &lt;module&gt;
    image_size=64, color=True, device = device)
  File "train_model.py", line 198, in train_model
    outputs = cpc_model(inputx, inputy)
  File "/home/pranavan/anaconda3/envs/capsule/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "train_model.py", line 155, in forward
    x = self.n_pl(x)
  File "/home/pranavan/anaconda3/envs/capsule/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "train_model.py", line 123, in forward
    outputs.append(self.fc1_layers[i](x).unsqueeze(1))
  File "/home/pranavan/anaconda3/envs/capsule/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/pranavan/anaconda3/envs/capsule/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 92, in forward
    return F.linear(input, self.weight, self.bias)
  File "/home/pranavan/anaconda3/envs/capsule/lib/python3.7/site-packages/torch/nn/functional.py", line 1406, in linear
    ret = torch.addmm(bias, input, weight.t())
RuntimeError: Expected object of backend CPU but got backend CUDA for argument #4 'mat1'
</code></pre>
<p>This is my source code. I have copied below.</p>
<pre><code class="lang-auto">import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.transforms as transforms

from data_utils import SortedNumberGenerator


# Done
# Similar to flatten layer of keras
class Flatten(nn.Module):
    def forward(self, x):
        return x.view(x.size()[0], -1)


# Done - needs to be tested
# Auto regressive model
class Autoregressor(nn.Module):

    def __init__(self, no_of_layers=256):
        super(Autoregressor, self).__init__()
        # very bad to hard code input shape. change in the long run
        self.gru = nn.GRU(input_size=128, hidden_size=256, num_layers=no_of_layers, batch_first=True)

    def forward(self, x):
        x, y = self.gru(x)
        x = x[:, x.size(1) - 1, :].squeeze()  # getting the last output like  keras - return_sequences = False
        return x


# Done - needs to be tested
# decoder of the neural network
class Encoder(nn.Module):
    def __init__(self, codesize=128):
        super(Encoder, self).__init__()

        self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=2)
        self.batch_norm1 = nn.BatchNorm2d(num_features=64)

        self.conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2)
        self.batch_norm2 = nn.BatchNorm2d(num_features=64)

        self.conv3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2)
        self.batch_norm3 = nn.BatchNorm2d(num_features=64)

        self.conv4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2)
        self.batch_norm4 = nn.BatchNorm2d(num_features=64)

        self.flatten1 = Flatten()
        self.fc1 = nn.Linear(in_features=576, out_features=256)

        self.batch_norm5 = nn.BatchNorm1d(num_features=256)

        self.fc2 = nn.Linear(in_features=256, out_features=codesize)

    def forward(self, x):
        x = F.leaky_relu(self.batch_norm1(self.conv1(x)))
        x = F.leaky_relu(self.batch_norm2(self.conv2(x)))
        x = F.leaky_relu(self.batch_norm3(self.conv3(x)))
        x = F.leaky_relu(self.batch_norm4(self.conv4(x)))

        x = self.flatten1(x)
        x = self.fc1(x)

        x = F.leaky_relu(self.batch_norm5(x))

        x = self.fc2(x)

        return x


# Done - needs to be tested
# keras TimeDistributed Layer alternative in pytorch - found in https://discuss.pytorch.org/t/any-pytorch-function-can-work-as-keras-timedistributed/1346/4
class TimeDistributed(nn.Module):
    def __init__(self, module):
        super(TimeDistributed, self).__init__()
        self.module = module

    def forward(self, x):
        # Squash samples and timesteps into a single axis
        x_reshape = x.contiguous().view(-1, x.size(-3), x.size(-2), x.size(-1))  # (samples * timesteps, C, H, W)
        y = self.module(x_reshape)
        y = y.contiguous().view(x.size(0), -1, y.size(-1))

        return y


# Done - needs to be tested
class CPCLayer(nn.Module):
    ''' Computes dot product between true and predicted embedding vectors '''

    def __init__(self, **kwargs):
        super(CPCLayer, self).__init__(**kwargs)

    def forward(self, x, y):
        # Compute dot product among vectors
        preds, y_encoded = x, y
        dot_product = torch.mean(y_encoded * preds, dim=-1)
        dot_product = torch.mean(dot_product, dim=-1, keepdim=True)  # average along the temporal dimension

        # Keras loss functions take probabilities
        dot_product_probs = torch.sigmoid(dot_product)

        return dot_product_probs


# Done - Need to be tested
# A thorough check is needed
class NetworkPredictionLayer(nn.Module):

    def __init__(self, code_size, predict_terms):
        super().__init__()
        self.predict_terms = predict_terms
        self.code_size = code_size
        self.fc1_layers = []
        for i in range(predict_terms):
            self.fc1_layers.append(nn.Linear(in_features=256, out_features=self.code_size))

    def forward(self, x):
        outputs = []
        for i in range(self.predict_terms):
            outputs.append(self.fc1_layers[i](x).unsqueeze(1))

        outputs = torch.cat(outputs, dim=1)

        return outputs


# WIP
class CPCModel(nn.Module):

    def __init__(self, code_size=128, terms=4, pred_terms=4):
        super(CPCModel, self).__init__()
        self.code_size = code_size
        self.ne = Encoder(codesize=code_size)
        self.terms = terms
        self.pred_terms = pred_terms
        self.td_1 = TimeDistributed(module=self.ne)
        self.td_2 = TimeDistributed(module=self.ne)
        self.n_ar = Autoregressor(no_of_layers=256)
        self.n_pl = NetworkPredictionLayer(code_size=code_size, predict_terms=terms)
        self.pred_terms = pred_terms
        self.cpc = CPCLayer()

    def forward(self, x, y):
        # print('Input X shape : ', x.shape)

        x = self.td_1(x)
        # print('Output from time distributed - 1', x.shape)

        x = self.n_ar(x)
        # print('Output from autoregressor : ', x.shape)

        x = self.n_pl(x)
        # print('Output from network prediction layer : ', x.shape)

        y = self.td_2(y)
        # print('output from time distributed 2', y.shape)

        z = self.cpc(x, y)
        # print('output from cpc layer', z.shape)

        return z


def train_model(epochs, batch_size, output_dir, code_size, lr=1e-4, terms=4, predict_terms=4, image_size=28,
                color=False, device = 'cuda'):
    # Prepare data
    train_data = SortedNumberGenerator(batch_size=batch_size, subset='train', terms=terms,
                                       positive_samples=batch_size // 2, predict_terms=predict_terms,
                                       image_size=image_size, color=color, rescale=True)
    validation_data = SortedNumberGenerator(batch_size=batch_size, subset='valid', terms=terms,
                                            positive_samples=batch_size // 2, predict_terms=predict_terms,
                                            image_size=image_size, color=color, rescale=True)

    cpc_model = CPCModel().to(device)
    criterion = nn.BCELoss()
    optimizer = optim.Adam(cpc_model.parameters(), lr=0.001)

    for lam in range(epochs):
        print('Epoch number : \t', (lam + 1))
        running_loss = 0.0
        print("Started Training")
        for i, data in enumerate(train_data):
            inputs, labels = data
            # print(type(inputs), type(labels))
            labels = torch.tensor(labels).to(device)
            inputx, inputy = torch.tensor(inputs[0]), torch.tensor(inputs[1])
            inputx = inputx.permute((0, 1, 4, 2, 3))
            inputy = inputy.permute((0, 1, 4, 2, 3))
            inputx, inputy = inputx.to(device), inputy.to(device)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = cpc_model(inputx, inputy)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()

            if i % 20 == 19:
                print('[%d, %5d] loss: %.3f' % (1, i + 1, running_loss / 20))
                running_loss = 0.0

    print("Finished Training!!!")

    # train_transformer = transforms.Compose([transforms.ToTensor()])

    # print(type(train_data), type(validation_data))


if __name__ == '__main__':
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print(device)
    train_model(epochs=10, batch_size=4, output_dir='models/64x64', code_size=128, lr=1e-3, terms=4, predict_terms=4,
                image_size=64, color=True, device = device)
    # print(device)
    # x = torch.rand((32, 4, 3, 64, 64))
    # y = torch.rand((32, 4, 3, 64, 64))
    # cpc_model = CPCModel()
    # print('Final shape : ', cpc_model(x, y).shape)
</code></pre>
<p>I appreciate your help. Thanks in advance.</p>

alex.veuthey: <p>Lists of modules are not handled in <code>nn.Module</code>, you need to either use <code>nn.Sequential</code> or <code>nn.ModuleList</code>. Check <a href="https://discuss.pytorch.org/t/list-of-nn-module-in-a-nn-module/219">this thread</a> for more detalis on the reasoning and <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.ModuleList" rel="nofollow noopener">here</a> for the doc on <code>nn.ModuleList</code>, which is probably closer to what you want.</p>

Pranavan_Theivendira: <p>Thanks <a class="mention" href="/u/alex.veuthey">@alex.veuthey</a> . That is the perfect solution. It resolved the issue. Thanks again for your time.</p>