"RuntimeError: input must have 3 dimensions, got 2" for BiLSTM forward function

1

Hi all,

This is my first time using PyTorch and would like to train a CNN -> BiLSTM -> CTC output.

Below is my code for the model, is it implemented correctly? Iā€™m getting the following error.

RuntimeError: input must have 3 dimensions, got 2 for the line out, _ = self.lstm1(out)

Cheers :slight_smile:

class ConvNetToBiLSTM(nn.Module):
    def __init__(self):
        super(ConvNetToBiLSTM, self).__init__()
        # 32 by 256
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        # 16 by 128
        self.layer2 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.drop_out = nn.Dropout()
        # 8 by 64
        self.fc1 = nn.Linear(8 * 64 * 64, 1024)

        self.layer_norm = nn.LayerNorm(1024)
        self.lstm1 = nn.LSTM(input_size=1024, hidden_size=100, num_layers=1, bidirectional=True)
        self.dropout = nn.Dropout()

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.drop_out(out)
        out = self.fc1(out)

        out = self.layer_norm(out)
        out = F.gelu(out)
        out, _ = self.lstm1(out)
        out = self.dropout(out)
        return out
2

out = out.reshape(out.size(0), -1) => You are reshaping here, causing dimensions to reduce.

Try adding a dimension like below but check how your model performs:

out = F.gelu(out)
out = out.unsqueeze(dim=0)
print(out.shape)
out, _ = self.lstm1(out)

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