convert_to parameters #18
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raphaelsty
reviewed
Jun 25, 2024
| @@ -93,7 +93,7 @@ def add_documents( | |||
| # TODO: use dynamic batching insert | |||
| data_objects = [ | |||
| wvc.data.DataObject( | |||
| properties={"doc_id": doc_id}, vector=token_embedding | |||
| properties={"doc_id": doc_id}, vector=token_embedding.tolist() | |||
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I would replace doc_id with document_id overall I think it's fine to use plain English to as variable name (not a blocker for merge, just a detail)
giga_cherche/scores/colbert_score.py
Outdated
| @@ -22,8 +22,16 @@ def colbert_score( | |||
| Returns: | |||
| Tensor: Matrix with res[i][j] = colbert_score(a[i], b[j]) | |||
| """ | |||
| a = _convert_to_batch_tensor(a) | |||
| b = _convert_to_batch_tensor(b) | |||
| if not isinstance(a, Tensor): | |||
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import numpy as np
import torch
from torch import Tensor
def convert_to_tensor(data):
if not isinstance(data, Tensor):
if isinstance(data[0], np.ndarray):
data = torch.from_numpy(np.array(data, dtype=np.float32))
else:
data = torch.stack(data)
return data
a = convert_to_tensor(a)
b = convert_to_tensor(b)
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This PR corrects the behavior of
convert_to_numpyandconvert_to_tensorparameters of the encode function, by returning either a list of numpy arrays or a list of tensors (as we cannot stack everything, since documents might not have the same length).I also adjusted the different part of the code relying on the encode function and it does not seems to brings regression.
Also added the padding option parameters, but I am still unsure about it has we create a big tensor to then split it into a list, when it will certainly be used as a tensor in the end, so the overhead is a bit painful.
@raphaelsty if you could please have a look and tell me what you think about this.