decagon-pytorch/src/icosagon/loss.py

import torch
from icosagon.trainprep import PreparedData
from icosagon.declayer import Predictions


class CrossEntropyLoss(torch.nn.Module):
    def __init__(self, data: PreparedData, partition_type: str = 'train',
        reduction: str = 'sum', **kwargs) -> None:

        super().__init__(**kwargs)

        if not isinstance(data, PreparedData):
            raise TypeError('data must be an instance of PreparedData')

        if partition_type not in ['train', 'val', 'test']:
            raise ValueError('partition_type must be set to train, val or test')

        if reduction not in ['sum', 'mean']:
            raise ValueError('reduction must be set to sum or mean')

        self.data = data
        self.partition_type = partition_type
        self.reduction = reduction

    def forward(self, pred: Predictions) -> torch.Tensor:
        input = []
        target = []
        for fam in pred.relation_families:
            for rel in fam.relation_types:
                for edge_type in ['edges_pos', 'edges_back_pos']:
                    x = getattr(getattr(rel, edge_type), self.partition_type)
                    assert len(x.shape) == 1
                    input.append(x)
                    target.append(torch.ones_like(x))
                for edge_type in ['edges_neg', 'edges_back_neg']:
                    x = getattr(getattr(rel, edge_type), self.partition_type)
                    assert len(x.shape) == 1
                    input.append(x)
                    target.append(torch.zeros_like(x))
        input = torch.cat(input, dim=0)
        target = torch.cat(target, dim=0)
        res = torch.nn.functional.binary_cross_entropy(input, target,
            reduction=self.reduction)
        return res