Summary of LSTM Model

#1

Hi !
I have problem with summary method. I am working with basic Lstm model and I don’t know how fix the problem.
Model:

class LSTMModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):
        super(LSTMModel, self).__init__()
        self.hidden_dim = hidden_dim
        self.layer_dim = layer_dim
        self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True)
        self.fc = nn.Linear(64 * hidden_dim, output_dim)

    def forward(self, x):
        h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_()
        c0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_()
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
        out = torch.flatten(out,start_dim= 1, end_dim= 2)
        out = self.fc(out) 
        return out
model = LSTMModel(8, 18, 7, 9)
summary(model,(64, 8))

I receive AttributeError: ‘tuple’ object has no attribute ‘size’. So how can I make summary ?
Can you help me with this ? Thanks !

#2

Your input seems to be missing the batch dimension and after fixing it torchinfo.summary works:

class LSTMModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):
        super(LSTMModel, self).__init__()
        self.hidden_dim = hidden_dim
        self.layer_dim = layer_dim
        self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True)
        self.fc = nn.Linear(64 * hidden_dim, output_dim)

    def forward(self, x):
        h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_()
        c0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_()
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
        out = torch.flatten(out,start_dim= 1, end_dim= 2)
        out = self.fc(out) 
        return out

model = LSTMModel(8, 18, 7, 9)
x = torch.randn(64, 8)
out = model(x)
# RuntimeError: For unbatched 2-D input, hx and cx should also be 2-D but got (3-D, 3-D) tensors

x = torch.randn(1, 64, 8)
out = model(x) # works

torchinfo.summary(model, (1, 64, 8), device="cpu")
# ==========================================================================================
# Layer (type:depth-idx)                   Output Shape              Param #
# ==========================================================================================
# LSTMModel                                [1, 9]                    --
# ├─LSTM: 1-1                              [1, 64, 18]               18,432
# ├─Linear: 1-2                            [1, 9]                    10,377
# ==========================================================================================
# Total params: 28,809
# Trainable params: 28,809
# Non-trainable params: 0
# Total mult-adds (M): 1.19
# ==========================================================================================
# Input size (MB): 0.00
# Forward/backward pass size (MB): 0.01
# Params size (MB): 0.12
# Estimated Total Size (MB): 0.13
# ==========================================================================================
#3

Thanks! It works :blush:. Are there any differences between torchinfo.summary and torchsummary.summary? For torchsummary it does not work.

#4

In case you are referring to this repository then be aware of the note in the README:

Use the new and updated torchinfo.

as the last change to torchsummary directly was done ~3 years ago.