Testing new data

[RodGal-2020]

I'm trying to use the model, but I'm having problems with the following step. I have already trained the model, and now I have the checkpoint_v_0.pth file, with which I do the following to load the generated model:

model = torch.load(PATH/to/pth/file)
print(model)

And this returns something like:

SPOTER(
  (transformer): Transformer(
    (encoder): TransformerEncoder(
      (layers): ModuleList(
        (0): TransformerEncoderLayer(
          (self_attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=108, out_features=108, bias=True)
          )
          (linear1): Linear(in_features=108, out_features=2048, bias=True)
          (dropout): Dropout(p=0.1, inplace=False)
          (linear2): Linear(in_features=2048, out_features=108, bias=True)
          (norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
          (norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
          (dropout1): Dropout(p=0.1, inplace=False)
          (dropout2): Dropout(p=0.1, inplace=False)
        )
        (1): TransformerEncoderLayer(
...

which makes me believe that everything is OK to this point.

Now I would like to see how I can use the model to make a prediction with new data, and there's where the problem is.

When running model(input) to get the results, some errors appear, and I believe that I'm not giving the correct kind of input. I'm using the second line from WLASL100_train_25fps.csv, changing the "s for [s in order to get a hierarchy like the following:

[
           [a, b, c],
           [d],
           [e, f]
]

However, this doesn't seem to work. Am I using a different format to the one the model should be given?

The exact input I'm giving the model follows, with the np.array conversion:

parsed_example = 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input = np.array([np.array(sublist) for sublist in parsed_example])