continuous_histogram

gistfile1.txt

import math

def normal(x, mu, sigma):
    return 1.0 / (sigma * math.sqrt(2*math.pi)) * torch.exp(-(x - mu)**2 / (2*sigma**2))

def continuous_histogram(batch, bins=32):
    hists = Variable(torch.zeros(batch.size(0), 3, bins).cuda())
    binvals = Variable(torch.linspace(0, 1, bins).cuda())
    # Expand values so we compute histogram in parallel.
    binvals = binvals.view(1, 1, 1, bins).expand(*batch.size(), bins)
    batch = batch.view(*batch.size(), 1).expand(*batch.size(), bins)
    hist = normal(batch, binvals, 0.01).sum(dim=2).squeeze()
    hist /= 10000 * bins
    return hist