customLosses.py

import numpy as np
import torch
import torch.nn as nn
from torchvision.utils import make_grid
from torch.autograd import Variable
from torch.autograd import grad as torch_grad


def gradient_penalty(real_data, generated_data, discriminator, gp_weight=10):
    batch_size = real_data.size()[0]

    # Calculate interpolation
    alpha = torch.rand(batch_size, 1, 1, 1)
    alpha = alpha.expand_as(real_data)
    alpha = alpha.cuda()
    interpolated = alpha * real_data.data + (1 - alpha) * generated_data.data
    interpolated = Variable(interpolated, requires_grad=True)
    interpolated = interpolated.cuda()

    # Calculate probability of interpolated examples
    prob_interpolated = discriminator(interpolated)

    # Calculate gradients of probabilities with respect to examples
    gradients = torch_grad(outputs=prob_interpolated, inputs=interpolated,
                            grad_outputs=torch.ones(prob_interpolated.size()).cuda(),
                            create_graph=True, retain_graph=True)[0]

    # Gradients have shape (batch_size, num_channels, img_width, img_height),
    # so flatten to easily take norm per example in batch
    gradients = gradients.view(batch_size, -1)

    # Derivatives of the gradient close to 0 can cause problems because of
    # the square root, so manually calculate norm and add epsilon
    gradients_norm = torch.sqrt(torch.sum(gradients ** 2, dim=1) + 1e-12)

    # Return gradient penalty
    return gp_weight * ((gradients_norm - 1) ** 2).mean()