From 930c26c3b139cc9639ceaa3f57a452f92a79d146 Mon Sep 17 00:00:00 2001 From: Dominik Haentsch Date: Thu, 22 Feb 2024 11:37:22 +0100 Subject: [PATCH] support hf metadata for images in arrow source --- poetry.lock | 138 +++++++++++------- pyproject.toml | 2 +- .../core/arrow_dataset_source.py | 58 +++++++- 3 files changed, 133 insertions(+), 65 deletions(-) diff --git a/poetry.lock b/poetry.lock index d6d0c6bb..cd71b97c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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a/renumics/spotlight_plugins/core/arrow_dataset_source.py b/renumics/spotlight_plugins/core/arrow_dataset_source.py index 94b5e7ca..4ff9ecfe 100644 --- a/renumics/spotlight_plugins/core/arrow_dataset_source.py +++ b/renumics/spotlight_plugins/core/arrow_dataset_source.py @@ -1,7 +1,9 @@ import uuid -from typing import List, Union +from itertools import islice +from typing import Iterable, List, Union import numpy as np +import orjson import pyarrow as pa import pyarrow.dataset import pyarrow.types @@ -21,6 +23,11 @@ class UnknownArrowType(Exception): EMPTY_MAP: spotlight_dtypes.DTypeMap = {} +def iter_batched(values: Iterable, n: int) -> Iterable[tuple]: + it = iter(values) + return iter(lambda: tuple(islice(it, n)), ()) + + @datasource(pyarrow.dataset.Dataset) @datasource(pyarrow.dataset.FileSystemDataset) class ArrowDatasetSource(DataSource): @@ -28,6 +35,16 @@ def __init__(self, source: pyarrow.dataset.Dataset): self._dataset = source self._intermediate_dtypes: spotlight_dtypes.DTypeMap = self._convert_schema() + self._semantic_dtypes = {} + # support hf metadata (only images for now) + if hf_metadata := orjson.loads( + source.schema.metadata.get(b"huggingface", "null") + ): + features = hf_metadata.get("info", {}).get("features", {}) + for name, feat in features.items(): + if feat.get("_type") == "Image": + self._semantic_dtypes[name] = spotlight_dtypes.image_dtype + @property def column_names(self) -> List[str]: return self._dataset.schema.names @@ -38,7 +55,7 @@ def intermediate_dtypes(self) -> spotlight_dtypes.DTypeMap: @property def semantic_dtypes(self) -> spotlight_dtypes.DTypeMap: - return EMPTY_MAP + return self._semantic_dtypes def __len__(self) -> int: return self._dataset.count_rows() @@ -57,12 +74,37 @@ def get_column_values( column_name: str, indices: Union[List[int], np.ndarray, slice] = slice(None), ) -> np.ndarray: - if isinstance(indices, slice): - indices = np.array(range(*indices.indices(len(self)))) - - table = self._dataset.take(indices) - values = table[column_name] - return values.to_numpy() + if indices == slice(None): + table = self._dataset.to_table(columns=[column_name]) + else: + if isinstance(indices, slice): + indices = np.array(range(*indices.indices(len(self)))) + + table = self._dataset.take( + indices, + columns=[column_name], + batch_size=min(len(indices), 2048), + batch_readahead=0, + fragment_readahead=0, + ) + + raw_values = table[column_name] + + # convert hf image values + if self._semantic_dtypes.get(column_name) == spotlight_dtypes.image_dtype: + return np.array( + [ + ( + value["path"].as_py() + if value["bytes"].as_py() is None + else value["bytes"].as_py() + ) + for value in raw_values + ], + dtype=object, + ) + else: + return raw_values.to_numpy() def get_column_metadata(self, column_name: str) -> ColumnMetadata: field_index = self._dataset.schema.get_field_index(column_name)