src/deprecated/pipeline_wrapper.py

from transformers import Pipeline

from src.deprecated.conversion import csv_to_pandas
from src.deprecated.pydantic_models import ECGConfig, ECGSample
from src.deprecated.ecg_processing import process_batch


class MyPipeline(Pipeline):
    def _sanitize_parameters(self, **kwargs):
        preprocess_kwargs = {}
        if "maybe_arg" in kwargs:
            preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
        return preprocess_kwargs, {}, {}

    def preprocess(self, inputs: str) -> dict:
        # inputs are csv files
        df = csv_to_pandas(inputs)
        # Implode
        cols_to_implode = ['timestamp_idx', 'ecg', 'label']
        df_imploded = df.groupby(list(set(df.columns) - set(cols_to_implode))) \
            .agg({'timestamp_idx': list,
                  'ecg': list,
                  'label': list}) \
            .reset_index()
        # Get metadata
        config_cols = [col for col in df.columns if col.startswith('configs.')]
        configs = df_imploded[config_cols].iloc[0].to_dict()
        configs = {key.removeprefix('configs.'): value for key, value in configs.items()}
        configs = ECGConfig(**configs)
        batch_cols = [col for col in df.columns if col.startswith('batch.')]
        batch = df_imploded[batch_cols].iloc[0].to_dict()
        batch = {key.removeprefix('batch.'): value for key, value in batch.items()}
        # Get samples
        samples = df_imploded.to_dict(orient='records')
        samples = [ECGSample(**sample) for sample in samples]

        model_input = {"samples": samples, "configs": configs, "batch": batch}
        return {"model_input": model_input}

    def _forward(self, model_inputs):
        # model_inputs == {"model_input": model_input}
        samples = model_inputs["model_input"]["samples"]
        configs = model_inputs["model_input"]["configs"]
        batch = model_inputs["model_input"]["batch"]

        features_df = process_batch(samples, configs)
        return features_df

    def postprocess(self, model_outputs):
        return model_outputs