TypeError: forward() missing 2 required positional arguments: 'cap_lens' and 'hidden'

[rchavezj]

Hello Everyone!

I’ve been working on deploying a machine learning model (Pytorch) into production (iOS), however I’m having a few obstacles I need to hurdle. I tried following the steps here (https://github.com/longcw/pytorch2caffe/) to convert it to either caffe2 or ONNX (https://github.com/onnx/onnx-coreml), knowing the input_size is required to make a forward pass through the network.

I keep getting an error with the forward function that was from the text_encoder RNN model.

The text_encoder is a machine learning model I loaded (Followed these steps: https://stackoverflow.com/questions/42703500/best-way-to-save-a-trained-model-in-pytorch) from another program file I used to train on.

I’m not sure if I’m either getting the error from Dimension mis-match or not properly calling enough arguments for my forward function. Any help is greatly appreciated!

[Diego]

EDIT:
sorry this is just a user warning

EDIT #2:
you dont need to wrap your tensors in Variables anymore. Since 0.4 Variables and Tensors are merged. I recommend you take a loot at the migration guide to make your code cleaner and compatible with future releases

I think the problem is, you are using dropout with only one layer. You need at least 2 layers to apply dropout if you are using the LSTM class. You can check the documentation here. It says:

dropout – If non-zero, introduces a Dropout layer on the outputs of each LSTM layer except the last layer, with dropout probability equal to dropout. Default: 0

best regards,
Diego

[rchavezj]

I’ll check out the documentation and get back to you shortly. Thank you for the starting point!

[Diego]

I think the cause of the error is you call your forward function like so:

output_var = text_encoder(input_var)

Yet your forward function is defined as:

def forward(self, captions, cap_lens, hidden, mask=None)

You are only passing 2 parameters (self, input_var) to your forward function but it needs at least 4.

best regards,
Diego

[rchavezj]

You are only passing 2 parameters (self, input_var) to your forward function but it needs at least 4.

What values of cap_lens & hidden should i use for replicating a loaded model?

[Diego]

From your code its is unclear to me what these parameters represent, could you post the others methods were these variables are used?

[rchavezj]

You don’t need to wrap your tensors in Variables anymore

Are you referring to input_var using a Variable keyword? Instead of

input_var = Variable(torch.randn(27297, 300), requires_grad=True)

I should have it as

input_var = torch.randn(27297, 300)

What Happens with

requires_grad=True?

[Diego]

You should do it like so:

input_var = torch.randn(27297, 300, requires_grad=True)

remember to check the migration guide

This is not the root of your problem though

[rchavezj]

I’m using this git repo (https://github.com/taoxugit/AttnGAN) for cap_lens and the hidden variable. Their located inside AttnGAN/code/pretrain_DAMSM.py @line_65

however they were generated data (prepare_data) from the class AttnGAN/code/datasets.py @line_28