image-match/python/py/Lib/site-packages/timm/loss/binary_cross_entropy.py

""" Binary Cross Entropy w/ a few extras

Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import Optional, Union

import torch
import torch.nn as nn
import torch.nn.functional as F


class BinaryCrossEntropy(nn.Module):
    """ BCE with optional one-hot from dense targets, label smoothing, thresholding
    NOTE for experiments comparing CE to BCE /w label smoothing, may remove
    """
    def __init__(
            self,
            smoothing=0.1,
            target_threshold: Optional[float] = None,
            weight: Optional[torch.Tensor] = None,
            reduction: str = 'mean',
            sum_classes: bool = False,
            pos_weight: Optional[Union[torch.Tensor, float]] = None,
    ):
        super(BinaryCrossEntropy, self).__init__()
        assert 0. <= smoothing < 1.0
        if pos_weight is not None:
            if not isinstance(pos_weight, torch.Tensor):
                pos_weight = torch.tensor(pos_weight)
        self.smoothing = smoothing
        self.target_threshold = target_threshold
        self.reduction = 'none' if sum_classes else reduction
        self.sum_classes = sum_classes
        self.register_buffer('weight', weight)
        self.register_buffer('pos_weight', pos_weight)

    def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        batch_size = x.shape[0]
        assert batch_size == target.shape[0]

        if target.shape != x.shape:
            # NOTE currently assume smoothing or other label softening is applied upstream if targets are already sparse
            num_classes = x.shape[-1]
            # FIXME should off/on be different for smoothing w/ BCE? Other impl out there differ
            off_value = self.smoothing / num_classes
            on_value = 1. - self.smoothing + off_value
            target = target.long().view(-1, 1)
            target = torch.full(
                (batch_size, num_classes),
                off_value,
                device=x.device, dtype=x.dtype).scatter_(1, target, on_value)

        if self.target_threshold is not None:
            # Make target 0, or 1 if threshold set
            target = target.gt(self.target_threshold).to(dtype=target.dtype)

        loss = F.binary_cross_entropy_with_logits(
            x, target,
            self.weight,
            pos_weight=self.pos_weight,
            reduction=self.reduction,
        )
        if self.sum_classes:
            loss = loss.sum(-1).mean()
        return loss