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48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
import logging
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from pathlib import Path
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from haystack.modeling.data_handler.processor import SquadProcessor
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from haystack.modeling.model.tokenization import Tokenizer
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from haystack.modeling.utils import set_all_seeds
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import torch
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from ..conftest import SAMPLES_PATH
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def test_processor_saving_loading(tmp_path, caplog):
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if caplog is not None:
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caplog.set_level(logging.CRITICAL)
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set_all_seeds(seed=42)
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lang_model = "roberta-base"
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tokenizer = Tokenizer.load(pretrained_model_name_or_path=lang_model, do_lower_case=False)
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processor = SquadProcessor(
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tokenizer=tokenizer,
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max_seq_len=256,
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label_list=["start_token", "end_token"],
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train_filename="train-sample.json",
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dev_filename="dev-sample.json",
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test_filename=None,
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data_dir=SAMPLES_PATH / "qa",
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)
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dicts = processor.file_to_dicts(file=SAMPLES_PATH / "qa" / "dev-sample.json")
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data, tensor_names, _ = processor.dataset_from_dicts(dicts=dicts, indices=[1])
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save_dir = tmp_path / Path("testsave/processor")
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processor.save(save_dir)
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processor = processor.load_from_dir(save_dir)
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dicts = processor.file_to_dicts(file=SAMPLES_PATH / "qa" / "dev-sample.json")
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data_loaded, tensor_names_loaded, _ = processor.dataset_from_dicts(dicts, indices=[1])
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assert tensor_names == tensor_names_loaded
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for i in range(len(data.tensors)):
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assert torch.all(torch.eq(data.tensors[i], data_loaded.tensors[i]))
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if __name__ == "__main__":
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test_processor_saving_loading(None)
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