PytorchOCR/torchocr/networks/losses/DetBasicLoss.py

# -*- coding: utf-8 -*-
# @Time    : 2019/12/4 14:39
# @Author  : zhoujun
import torch
import torch.nn as nn


class BalanceCrossEntropyLoss(nn.Module):
    '''
    Balanced cross entropy loss.
    Shape:
        - Input: :math:`(N, 1, H, W)`
        - GT: :math:`(N, 1, H, W)`, same shape as the input
        - Mask: :math:`(N, H, W)`, same spatial shape as the input
        - Output: scalar.

    Examples::

        >>> m = nn.Sigmoid()
        >>> loss = nn.BCELoss()
        >>> input = torch.randn(3, requires_grad=True)
        >>> target = torch.empty(3).random_(2)
        >>> output = loss(m(input), target)
        >>> output.backward()
    '''

    def __init__(self, negative_ratio=3.0, eps=1e-6):
        super(BalanceCrossEntropyLoss, self).__init__()
        self.negative_ratio = negative_ratio
        self.eps = eps

    def forward(self,
                pred: torch.Tensor,
                gt: torch.Tensor,
                mask: torch.Tensor,
                return_origin=False):
        '''
        Args:
            pred: shape :math:`(N, 1, H, W)`, the prediction of network
            gt: shape :math:`(N, 1, H, W)`, the target
            mask: shape :math:`(N, H, W)`, the mask indicates positive regions
        '''

        positive = (gt * mask).byte()
        negative = ((1 - gt) * mask).byte()
        positive_count = int(positive.float().sum())
        negative_count = min(int(negative.float().sum()), int(positive_count * self.negative_ratio))
        loss = nn.functional.binary_cross_entropy(pred, gt, reduction='none')
        positive_loss = loss * positive.float()
        negative_loss = loss * negative.float()

        negative_loss, _ = torch.topk(negative_loss.view(-1), negative_count)

        balance_loss = (positive_loss.sum() + negative_loss.sum()) / (positive_count + negative_count + self.eps)

        if return_origin:
            return balance_loss, loss
        return balance_loss


class DiceLoss(nn.Module):
    '''
    Loss function from https://arxiv.org/abs/1707.03237,
    where iou computation is introduced heatmap manner to measure the
    diversity bwtween tow heatmaps.
    '''

    def __init__(self, eps=1e-6):
        super(DiceLoss, self).__init__()
        self.eps = eps

    def forward(self, pred: torch.Tensor, gt, mask, weights=None):
        '''
        pred: one or two heatmaps of shape (N, 1, H, W),
            the losses of tow heatmaps are added together.
        gt: (N, 1, H, W)
        mask: (N, H, W)
        '''
        return self._compute(pred, gt, mask, weights)

    def _compute(self, pred, gt, mask, weights):
        if pred.dim() == 4:
            pred = pred[:, 0, :, :]
            gt = gt[:, 0, :, :]
        assert pred.shape == gt.shape
        assert pred.shape == mask.shape
        if weights is not None:
            assert weights.shape == mask.shape
            mask = weights * mask
        intersection = (pred * gt * mask).sum()

        union = (pred * mask).sum() + (gt * mask).sum() + self.eps
        loss = 1 - 2.0 * intersection / union
        assert loss <= 1
        return loss


class MaskL1Loss(nn.Module):
    def __init__(self, eps=1e-6):
        super(MaskL1Loss, self).__init__()
        self.eps = eps

    def forward(self, pred: torch.Tensor, gt, mask):
        loss = (torch.abs(pred - gt) * mask).sum() / (mask.sum() + self.eps)
        return loss


class BCELoss(nn.Module):
    def __init__(self, reduction='mean'):
        super(BCELoss, self).__init__()
        self.reduction = reduction

    def forward(self, input, label, mask=None, weight=None, name=None):
        loss = nn.functional.binary_cross_entropy(input, label, reduction=self.reduction)
        return loss


class BalanceLoss(nn.Module):
    def __init__(self,
                 balance_loss=True,
                 main_loss_type='DiceLoss',
                 negative_ratio=3,
                 return_origin=False,
                 eps=1e-6,
                 **kwargs):
        super(BalanceLoss, self).__init__()
        self.balance_loss = balance_loss
        self.main_loss_type = main_loss_type
        self.negative_ratio = negative_ratio
        self.return_origin = return_origin
        self.eps = eps

        if self.main_loss_type == "CrossEntropy":
            self.loss = nn.CrossEntropyLoss()
        elif self.main_loss_type == "Euclidean":
            self.loss = nn.MSELoss()
        elif self.main_loss_type == "DiceLoss":
            self.loss = DiceLoss(self.eps)
        elif self.main_loss_type == "BCELoss":
            self.loss = BCELoss(reduction='none')
        elif self.main_loss_type == "MaskL1Loss":
            self.loss = MaskL1Loss(self.eps)
        else:
            loss_type = [
                'CrossEntropy', 'DiceLoss', 'Euclidean', 'BCELoss', 'MaskL1Loss'
            ]
            raise Exception(
                "main_loss_type in BalanceLoss() can only be one of {}".format(
                    loss_type))

    def forward(self, pred, gt, mask=None):
        """
        The BalanceLoss for Differentiable Binarization text detection
        args:
            pred (variable): predicted feature maps.
            gt (variable): ground truth feature maps.
            mask (variable): masked maps.
        return: (variable) balanced loss
        """
        positive = (gt * mask).byte()
        negative = ((1 - gt) * mask).byte()

        positive_count = int(positive.float().sum())
        negative_count = int(min(negative.float().sum(), positive_count * self.negative_ratio))

        loss = self.loss(pred, gt, mask=mask)

        if not self.balance_loss:
            return loss

        positive_loss = positive.float() * loss
        negative_loss = negative.float() * loss
        if negative_count > 0:
            negative_loss, _ = torch.topk(negative_loss.view(-1), negative_count)
            balance_loss = (positive_loss.sum() + negative_loss.sum()) / (
                    positive_count + negative_count + self.eps)
        else:
            balance_loss = positive_loss.sum() / (positive_count + self.eps)
        if self.return_origin:
            return balance_loss, loss

        return balance_loss