Source code for ts.torch_handler.request_envelope.json

Uses JSON formatted inputs/outputs, following the structure outlined in
import json
from base64 import b64decode
from itertools import chain

from .base import BaseEnvelope

[docs]class JSONEnvelope(BaseEnvelope): """ Implementation. Captures batches in JSON format, returns also in JSON format. """ _lengths = []
[docs] def parse_input(self, data): lengths, batch = self._batch_from_json(data) self._lengths = lengths return batch
[docs] def format_output(self, data): return self._batch_to_json(data, self._lengths)
def _batch_from_json(self, data_rows): """ Joins the instances of a batch of JSON objects """ mini_batches = [self._from_json(data_row) for data_row in data_rows] lengths = [len(mini_batch) for mini_batch in mini_batches] full_batch = list(chain.from_iterable(mini_batches)) return lengths, full_batch def _from_json(self, data): """ Extracts the data from the JSON object """ rows = (data.get("data") or data.get("body") or data)["instances"] if isinstance(rows[0], dict): for row_i, row in enumerate(rows): if list(row.keys()) == ["b64"]: rows[row_i] = b64decode(row["b64"]) else: for col, col_value in row.items(): if isinstance(col_value, dict) and list(col_value.keys()) == [ "b64" ]: row[col] = b64decode(col_value["b64"]) return rows def _batch_to_json(self, batch, lengths): """ Splits the batched output into mini-batches and returns JSON """ outputs = [] cursor = 0 for length in lengths: cursor_end = cursor + length mini_batch = batch[cursor:cursor_end] outputs.append(self._to_json(mini_batch)) cursor = cursor_end return outputs def _to_json(self, output): """ Converts the output of the model back into compatible JSON """ out_dict = {"predictions": output} return json.dumps(out_dict)


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