distance_gan_model.py 文件源码

def get_std(self, num_items, vars, expectation):

        num_pairs = 0
        std_sum = 0.0

        # If self distance computed std for top and bottom half
        if self.use_self_distance:
            for i in xrange(num_items):
                var_half_1, var_half_2 = torch.chunk(vars[i], 2, dim=2)
                std_sum += np.square(self.as_np(self.distance(var_half_1, var_half_2)) - expectation)
            return np.sqrt(std_sum / num_items)

        # Otherwise compute std for all pairs of images
        for i in xrange(num_items - 1):
            for j in xrange(i + 1, num_items):
                num_pairs += 1
                std_sum += np.square(self.as_np(self.distance(vars[i], vars[j])) - expectation)

        return np.sqrt(std_sum / num_pairs)