Expected object of scalar type Double but got scalar type Float - CONVLSTM

Hello I have the following ConvLSTM-Autoencoder model. I have been getting the error

RuntimeError: expected scalar type Double but found Float

I think it comes from convLSTM cell, but I just couldn’t figure it out. Task is n-step ahead frame prediction. Sequence length is 3 and we want to predict the future 2 frames

Here is the code for convLSTM:

class ConvLSTMCell(nn.Module):

    def __init__(self, input_dim, hidden_dim, kernel_size, bias):
        Initialize ConvLSTM cell.

        input_dim: int
            Number of channels of input tensor.
        hidden_dim: int
            Number of channels of hidden state.
        kernel_size: (int, int)
            Size of the convolutional kernel.
        bias: bool
            Whether or not to add the bias.

        super(ConvLSTMCell, self).__init__()

        self.input_dim = input_dim
        self.hidden_dim = hidden_dim

        self.kernel_size = kernel_size
        self.padding = kernel_size[0] // 2, kernel_size[1] // 2
        self.bias = bias

        self.conv = nn.Conv2d(in_channels=self.input_dim + self.hidden_dim,
                              out_channels=4 * self.hidden_dim,

    def forward(self, input_tensor, cur_state):
        h_cur, c_cur = cur_state

        combined = torch.cat([input_tensor, h_cur], dim=1)  # concatenate along channel axis

        combined_conv = self.conv(combined)
        cc_i, cc_f, cc_o, cc_g = torch.split(combined_conv, self.hidden_dim, dim=1)
        i = torch.sigmoid(cc_i)
        f = torch.sigmoid(cc_f)
        o = torch.sigmoid(cc_o)
        g = torch.tanh(cc_g)

        c_next = f * c_cur + i * g
        h_next = o * torch.tanh(c_next)

        return h_next, c_next

    def init_hidden(self, batch_size, image_size):
        height, width = image_size
        return (torch.zeros(batch_size, self.hidden_dim, height, width, device=self.conv.weight.device),
                torch.zeros(batch_size, self.hidden_dim, height, width, device=self.conv.weight.device))

class ConvLSTM(nn.Module):

        input_dim: Number of channels in input
        hidden_dim: Number of hidden channels
        kernel_size: Size of kernel in convolutions
        num_layers: Number of LSTM layers stacked on each other
        batch_first: Whether or not dimension 0 is the batch or not
        bias: Bias or no bias in Convolution
        return_all_layers: Return the list of computations for all layers
        Note: Will do same padding.
        A tensor of size B, T, C, H, W or T, B, C, H, W
        A tuple of two lists of length num_layers (or length 1 if return_all_layers is False).
            0 - layer_output_list is the list of lists of length T of each output
            1 - last_state_list is the list of last states
                    each element of the list is a tuple (h, c) for hidden state and memory
        >> x = torch.rand((32, 10, 64, 128, 128))
        >> convlstm = ConvLSTM(64, 16, 3, 1, True, True, False)
        >> _, last_states = convlstm(x)
        >> h = last_states[0][0]  # 0 for layer index, 0 for h index

    def __init__(self, input_dim, hidden_dim, kernel_size, num_layers,
                 batch_first=False, bias=True, return_all_layers=False):
        super(ConvLSTM, self).__init__()


        # Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers
        kernel_size = self._extend_for_multilayer(kernel_size, num_layers)
        hidden_dim = self._extend_for_multilayer(hidden_dim, num_layers)
        if not len(kernel_size) == len(hidden_dim) == num_layers:
            raise ValueError('Inconsistent list length.')

        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.kernel_size = kernel_size
        self.num_layers = num_layers
        self.batch_first = batch_first
        self.bias = bias
        self.return_all_layers = return_all_layers

        cell_list = []
        for i in range(0, self.num_layers):
            cur_input_dim = self.input_dim if i == 0 else self.hidden_dim[i - 1]


        self.cell_list = nn.ModuleList(cell_list)

    def forward(self, input_tensor, hidden_state=None):
        input_tensor: todo
            5-D Tensor either of shape (t, b, c, h, w) or (b, t, c, h, w)
        hidden_state: todo
            None. todo implement stateful
        last_state_list, layer_output
        if not self.batch_first:
            # (t, b, c, h, w) -> (b, t, c, h, w)
            input_tensor = input_tensor.permute(1, 0, 2, 3, 4)

        b, _, _, h, w = input_tensor.size()

        # Implement stateful ConvLSTM
        if hidden_state is not None:
            raise NotImplementedError()
            # Since the init is done in forward. Can send image size here
            hidden_state = self._init_hidden(batch_size=b,
                                             image_size=(h, w))

        layer_output_list = []
        last_state_list = []

        seq_len = input_tensor.size(1)
        cur_layer_input = input_tensor

        for layer_idx in range(self.num_layers):

            h, c = hidden_state[layer_idx]
            output_inner = []
            for t in range(seq_len):
                h, c = self.cell_list[layer_idx](input_tensor=cur_layer_input[:, t, :, :, :],
                                                 cur_state=[h, c])

            layer_output = torch.stack(output_inner, dim=1)
            cur_layer_input = layer_output

            last_state_list.append([h, c])

        if not self.return_all_layers:
            layer_output_list = layer_output_list[-1:]
            last_state_list = last_state_list[-1:]

        return layer_output_list, last_state_list

    def _init_hidden(self, batch_size, image_size):
        init_states = []
        for i in range(self.num_layers):
            init_states.append(self.cell_list[i].init_hidden(batch_size, image_size))
        return init_states

    def _check_kernel_size_consistency(kernel_size):
        if not (isinstance(kernel_size, tuple) or
                (isinstance(kernel_size, list) and all([isinstance(elem, tuple) for elem in kernel_size]))):
            raise ValueError('`kernel_size` must be tuple or list of tuples')

    def _extend_for_multilayer(param, num_layers):
        if not isinstance(param, list):
            param = [param] * num_layers
        return param

Code for AE:

class EncoderDecoderConvLSTM(nn.Module):
    def __init__(self, nf, in_chan):
        super(EncoderDecoderConvLSTM, self).__init__()

        """ ARCHITECTURE 

        # Encoder (ConvLSTM)
        # Encoder Vector (final hidden state of encoder)
        # Decoder (ConvLSTM) - takes Encoder Vector as input
        # Decoder (3D CNN) - produces regression predictions for our model

        ## Input is 3 days
        self.encoder_1_convlstm = ConvLSTMCell(input_dim=in_chan,
                                               kernel_size=(3, 3),
        self.encoder_2_convlstm = ConvLSTMCell(input_dim=nf,
                                               kernel_size=(3, 3),

        self.encoder_3_convlstm = ConvLSTMCell(input_dim=nf,
                                               kernel_size=(3, 3),
        ## Output is 2 days
        self.decoder_1_convlstm = ConvLSTMCell(input_dim=nf,  # nf + 1
                                               kernel_size=(3, 3),

        self.decoder_2_convlstm = ConvLSTMCell(input_dim=nf,
                                               kernel_size=(3, 3),

        self.decoder_CNN = nn.Conv3d(in_channels=nf,
                                     kernel_size=(1, 3, 3),
                                     padding=(0, 1, 1))

    def autoencoder(self, x, seq_len, future_step, h_t, c_t, h_t2, c_t2, h_t3, c_t3, h_t4, c_t4, h_t5,c_t5):

        outputs = []

        # encoder
        for t in range(seq_len):
            h_t, c_t = self.encoder_1_convlstm(input_tensor=x[:, t, :, :],
                                               cur_state=[h_t, c_t])  # we could concat to provide skip conn here
            h_t2, c_t2 = self.encoder_2_convlstm(input_tensor=h_t,
                                                 cur_state=[h_t2, c_t2])  # we could concat to provide skip conn here
            h_t3, c_t3 = self.encoder_3_convlstm(input_tensor=h_t2,cur_state=[h_t3,c_t3])

        # encoder_vector
        encoder_vector = h_t3

        # decoder
        for t in range(future_step):
            h_t4, c_t4 = self.decoder_1_convlstm(input_tensor=encoder_vector,
                                                 cur_state=[h_t4, c_t4])  # we could concat to provide skip conn here
            h_t5, c_t5 = self.decoder_2_convlstm(input_tensor=h_t4,
                                                 cur_state=[h_t5, c_t5])  # we could concat to provide skip conn here
            encoder_vector = h_t5
            outputs += [h_t5]  # predictions

        outputs = torch.stack(outputs, 1)
        outputs = outputs.permute(0, 2, 1, 3, 4)
        #outputs = self.decoder_CNN(outputs)
        #outputs = torch.nn.Sigmoid()(outputs)

        return outputs

    def forward(self, x, future_seq=0, hidden_state=None):

            5-D Tensor of shape (b, t, c, h, w)        #   batch, time, channel, height, width

        # find size of different input dimensions
        b, seq_len, _, h, w = x.size()

        # initialize hidden states
        h_t, c_t = self.encoder_1_convlstm.init_hidden(batch_size=b, image_size=(h, w))
        h_t2, c_t2 = self.encoder_2_convlstm.init_hidden(batch_size=b, image_size=(h, w))
        h_t3, c_t3 = self.encoder_2_convlstm.init_hidden(batch_size=b, image_size=(h, w))
        h_t4, c_t4 = self.decoder_1_convlstm.init_hidden(batch_size=b, image_size=(h, w))
        h_t5, c_t5 = self.decoder_2_convlstm.init_hidden(batch_size=b, image_size=(h, w))

        # autoencoder forward
        outputs = self.autoencoder(x, seq_len, future_seq, h_t, c_t, h_t2, c_t2, h_t3, c_t3, h_t4, c_t4, h_t5, c_t5)

        return outputs

My model:


class MyModel(pl.LightningModule):

    def __init__(self, hparams=None, model=None):
        super(MyModel, self).__init__()

        # default config
        self.normalize = False
        self.model = model

        # logging config
        self.log_images = True

        # Training config
        self.criterion = torch.nn.MSELoss()
        self.batch_size = 8
        self.n_steps_past = 3
        self.n_steps_ahead = 2  # 4

    def forward(self, x):

        output = self.model(x, future_seq=self.n_steps_ahead)

        return output

    def training_step(self, batch, batch_idx):
        x, y = batch[0],batch[1]
        #x = x.permute(0, 1, 4, 2, 3)
        print("x shape: ",x.shape)
        y = y.squeeze()

        y_hat = self.forward(x).squeeze()  # is squeeze neccessary?

        loss = self.criterion(y_hat, y)

        # save learning_rate
        lr_saved = self.trainer.optimizers[0].param_groups[-1]['lr']
        lr_saved = torch.scalar_tensor(lr_saved)

        # save predicted images every 250 global_step
        if self.log_images:
            if self.global_step % 250 == 0:
                final_image = self.create_video(x, y_hat, y)

                    'epoch_' + str(self.current_epoch) + '_step' + str(self.global_step) + '_generated_images',
                    final_image, 0)

        tensorboard_logs = {'train_mse_loss': loss,
                            'learning_rate': lr_saved}

        return {'loss': loss, 'log': tensorboard_logs}

    def test_step(self, batch, batch_idx):
        # OPTIONAL
        x, y = batch
        y_hat = self.forward(x)
        return {'test_loss': self.criterion(y_hat, y)}

    def test_end(self, outputs):
        # OPTIONAL
        avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()
        tensorboard_logs = {'test_loss': avg_loss}
        return {'avg_test_loss': avg_loss, 'log': tensorboard_logs}

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=0.001)

    def train_dataloader(self):
        #Create dataset
        #Load train data
        DL_train = DataLoader(train_ds)

        return DL_train

    def test_dataloader(self):
        #Load test data
        DL_test = DataLoader(test_ds) 
        return DL_test

def run_trainer():
    conv_lstm_model = EncoderDecoderConvLSTM(nf=16, in_chan=29)

    model = MyModel(model=conv_lstm_model)
    #model = model.to(device='cuda')

    trainer = Trainer(max_epochs=8)


Could you check the dtype of your input and the registered parameters of your model?
Based on the error message I guess your input might be in float64 while the model would use float32 by default. If so, then transform the input via x = x.float() and it should work.