Source code for flash.graph.classification.input_transform
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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from dataclasses import dataclass
from typing import Any, Callable, Dict, List
from torch.utils.data.dataloader import default_collate
from flash.core.data.io.input import DataKeys
from flash.core.data.io.input_transform import InputTransform
from flash.core.data.utilities.samples import to_sample
from flash.core.utilities.imports import _GRAPH_AVAILABLE
if _GRAPH_AVAILABLE:
from torch_geometric.data import Batch, Data
from torch_geometric.transforms import NormalizeFeatures
else:
Data = object
@dataclass
class PyGTransformAdapter:
"""Adapter to enable using ``PyG`` transforms within flash.
Args:
transform: Transform to apply.
"""
transform: Callable[[Data], Data]
def __call__(self, x: Dict[str, Any]):
data = x[DataKeys.INPUT]
data.y = x.get(DataKeys.TARGET, None)
data = self.transform(data)
return to_sample((data, data.y))
[docs]class GraphClassificationInputTransform(InputTransform):
@staticmethod
def _pyg_collate(samples: List[Dict[str, Any]]) -> Dict[str, Any]:
inputs = Batch.from_data_list([sample[DataKeys.INPUT] for sample in samples])
if DataKeys.TARGET in samples[0]:
targets = default_collate([sample[DataKeys.TARGET] for sample in samples])
return {DataKeys.INPUT: inputs, DataKeys.TARGET: targets}
return {DataKeys.INPUT: inputs}
def collate(self) -> Callable:
return self._pyg_collate
def per_sample_transform(self) -> Callable:
return PyGTransformAdapter(NormalizeFeatures())