Differentiable torch.histc?

autograd
4

The soft histogram might be usable instead of torch.histc.

import torch
import torch.nn as nn
import numpy as np

data = 50 + 25 * torch.randn(1000)

hist = torch.histc(data, bins=10, min=0, max=100)

print(hist)

class SoftHistogram(nn.Module):
    def __init__(self, bins, min, max, sigma):
        super(SoftHistogram, self).__init__()
        self.bins = bins
        self.min = min
        self.max = max
        self.sigma = sigma
        self.delta = float(max - min) / float(bins)
        self.centers = float(min) + self.delta * (torch.arange(bins).float() + 0.5)

    def forward(self, x):
        x = torch.unsqueeze(x, 0) - torch.unsqueeze(self.centers, 1)
        x = torch.sigmoid(self.sigma * (x + self.delta/2)) - torch.sigmoid(self.sigma * (x - self.delta/2))
        x = x.sum(dim=1)
        return x

softhist = SoftHistogram(bins=10, min=0, max=100, sigma=3)

data.requires_grad = True
hist = softhist(data)
print(hist)

hist.sum().backward()
print(data.grad.max())

Output:

tensor([ 27.,  59.,  89., 134., 153., 183., 142.,  97.,  52.,  32.])
tensor([ 26.2054,  60.0647,  87.4429, 135.9337, 150.7281, 184.4028, 139.8741,
         97.9176,  52.9988,  30.5099], grad_fn=<SumBackward2>)
tensor(0.7499)
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