data/utils/losses.py

"""
Helpers for various likelihood-based losses. These are ported from the original
Ho et al. diffusion models codebase:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/utils.py
"""

import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F


def normal_kl(mean1, logvar1, mean2, logvar2):
    """
    Compute the KL divergence between two gaussians.

    Shapes are automatically broadcasted, so batches can be compared to
    scalars, among other use cases.
    """
    tensor = None
    for obj in (mean1, logvar1, mean2, logvar2):
        if isinstance(obj, th.Tensor):
            tensor = obj
            break
    assert tensor is not None, "at least one argument must be a Tensor"

    # Force variances to be Tensors. Broadcasting helps convert scalars to
    # Tensors, but it does not work for th.exp().
    logvar1, logvar2 = [
        x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
        for x in (logvar1, logvar2)
    ]

    return 0.5 * (
            -1.0
            + logvar2
            - logvar1
            + th.exp(logvar1 - logvar2)
            + ((mean1 - mean2) ** 2) * th.exp(-logvar2)
    )


def approx_standard_normal_cdf(x):
    """
    A fast approximation of the cumulative distribution function of the
    standard normal.
    """
    return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))


def discretized_gaussian_log_likelihood(x, *, means, log_scales):
    """
    Compute the log-likelihood of a Gaussian distribution discretizing to a
    given image.

    :param x: the target images. It is assumed that this was uint8 values,
              rescaled to the range [-1, 1].
    :param means: the Gaussian mean Tensor.
    :param log_scales: the Gaussian log stddev Tensor.
    :return: a tensor like x of log probabilities (in nats).
    """
    assert x.shape == means.shape == log_scales.shape
    centered_x = x - means
    inv_stdv = th.exp(-log_scales)
    plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
    cdf_plus = approx_standard_normal_cdf(plus_in)
    min_in = inv_stdv * (centered_x - 1.0 / 255.0)
    cdf_min = approx_standard_normal_cdf(min_in)
    log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
    log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
    cdf_delta = cdf_plus - cdf_min
    log_probs = th.where(
        x < -0.999,
        log_cdf_plus,
        th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
    )
    assert log_probs.shape == x.shape
    return log_probs


def make_one_hot(input, num_classes):
    """Convert class index tensor to one hot encoding tensor.
    Args:
         input: A tensor of shape [N, 1, *]
         num_classes: An int of number of class
    Returns:
        A tensor of shape [N, num_classes, *]
    """
    shape = np.array(input.shape)
    shape[1] = num_classes
    shape = tuple(shape)
    result = th.zeros(shape)
    result = result.scatter_(1, input.cpu(), 1)

    return result


import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F


def make_one_hot(input, num_classes):
    """Convert class index tensor to one hot encoding tensor.
    Args:
         input: A tensor of shape [N, 1, *]
         num_classes: An int of number of class
    Returns:
        A tensor of shape [N, num_classes, *]
    """
    shape = np.array(input.shape)
    shape[1] = num_classes
    shape = tuple(shape)
    result = torch.zeros(shape)
    result = result.scatter_(1, input.cpu(), 1)

    return result


class BinaryDiceLoss(nn.Module):
    """Dice loss of binary class
    Args:
        smooth: A float number to smooth loss, and avoid NaN error, default: 1
        p: Denominator value: \sum{x^p} + \sum{y^p}, default: 2
        predict: A tensor of shape [N, *]
        target: A tensor of shape same with predict
        reduction: Reduction method to apply, return mean over batch if 'mean',
            return sum if 'sum', return a tensor of shape [N,] if 'none'
    Returns:
        Loss tensor according to arg reduction
    Raise:
        Exception if unexpected reduction
    """
    def __init__(self, smooth=1, p=2, reduction='mean'):
        super(BinaryDiceLoss, self).__init__()
        self.smooth = smooth
        self.p = p
        self.reduction = reduction

    def forward(self, predict, target):
        assert predict.shape[0] == target.shape[0], "predict & target batch size don't match"
        predict = predict.contiguous().view(predict.shape[0], -1)
        target = target.contiguous().view(target.shape[0], -1)

        num = torch.sum(torch.mul(predict, target), dim=1) + self.smooth
        den = torch.sum(predict.pow(self.p) + target.pow(self.p), dim=1) + self.smooth

        loss = 1 - num / den

        if self.reduction == 'mean':
            return loss.mean()
        elif self.reduction == 'sum':
            return loss.sum()
        elif self.reduction == 'none':
            return loss
        else:
            raise Exception('Unexpected reduction {}'.format(self.reduction))


class DiceLoss(nn.Module):
    """Dice loss, need one hot encode input
    Args:
        weight: An array of shape [num_classes,]
        ignore_index: class index to ignore
        predict: A tensor of shape [N, C, *]
        target: A tensor of same shape with predict
        other args pass to BinaryDiceLoss
    Return:
        same as BinaryDiceLoss
    """
    def __init__(self, weight=None, ignore_index=None, **kwargs):
        super(DiceLoss, self).__init__()
        self.kwargs = kwargs
        self.weight = weight
        self.ignore_index = ignore_index

    def forward(self,pred, mask):
        weit = 1  + torch.abs(F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15) - mask) / 100
        wbce = F.binary_cross_entropy_with_logits(pred, mask, reduction='none')
        wbce = (weit * wbce).sum(dim=(2, 3)) / weit.sum(dim=(2, 3))

        pred = torch.sigmoid(pred)
        inter = ((pred * mask) * weit).sum(dim=(2, 3))
        union = ((pred + mask) * weit).sum(dim=(2, 3))
        wiou = 1 - (inter + 1) / (union - inter + 1)
        return (wbce + wiou).mean()

class PSNRLoss(nn.Module):
    """Peak Signal to Noise Ratio
    img1 and img2 have range [0, 255]"""

    def __init__(self):
        super(PSNRLoss, self).__init__()
        self.name = "PSNR"

    def forward(self, img1, img2):
        mse = th.mean((img1 - img2) ** 2)
        return 20 * th.log10(1.0 / th.sqrt(mse))