loss_functions.py

import torch
import torch.nn.functional as F

import diff_operators
import modules
import dataio
from kornia.losses import ssim_loss

def cross_entropy(mask, model_output, gt):
    return {'img_loss': F.cross_entropy(model_output['cls'], gt['cls'])}


def color_loss(output, gt):
    img_ref = F.normalize(output, p = 2, dim = 1)
    ref_p = F.normalize(gt, p = 2, dim = 1)
    loss_cos = 1 - torch.mean(F.cosine_similarity(img_ref, ref_p, dim=1))
    return loss_cos

def color_mse_ray(model_output, gt):
    return {'img_loss': ((model_output['new_img'] - gt['img']) ** 2).mean(), 'color_loss': color_loss(model_output['new_img'], gt['img'])}

def image_mse(mask, model_output, gt):
    if mask is None:
        return {'img_loss': ((model_output['model_out'] - gt['img']) ** 2).mean()}
    else:
        return {'img_loss': (mask * (model_output['model_out'] - gt['img']) ** 2).mean()}

def image_mse_lip(mask, model_output, gt):
    if mask is None:
        dic = {'img_loss': ((model_output['model_out'] - gt['img']) ** 2).mean()}
    else:
        dic = {'img_loss': (mask * (model_output['model_out'] - gt['img']) ** 2).mean()}
    dic['c_loss'] = 1e-4 * model_output['c']
    if 'grad' in model_output:
        dic['grad'] = 1e-6 * torch.sum(torch.linalg.norm(model_output['grad'], dim=1, ord=1))
    return dic

def image_mse_grad(mask, model_output, gt):
    if mask is None:
        dic = {'img_loss': ((model_output['model_out'] - gt['img']) ** 2).mean()}
    else:
        dic = {'img_loss': (mask * (model_output['model_out'] - gt['img']) ** 2).mean()}
    dic['grad_loss'] = torch.abs(model_output['new_img'] - gt['gradients']).mean() * 0.1
    return dic

def image_mse_grad_only(mask, model_output, gt):
    dic = {}
    dic['grad_loss'] = torch.square(model_output['new_img'] - gt['gradients']).mean()
    sz = 32
    dic['grad_loss'] += ssim_loss(model_output['new_img'].view(1, 1, sz, sz) * 256, gt['gradients'].view(1, 1, sz, sz) * 256, 7)
    return dic
def image_mse_ray(mask, model_output, gt):
    dic = {}
    dic['grad_loss'] = torch.abs(model_output['new_img'] - gt['img']).mean()
    return dic

def image_l1(mask, model_output, gt):
    if mask is None:
        return {'img_loss': torch.abs(model_output['model_out'] - gt['img']).mean()}
    else:
        return {'img_loss': (mask * torch.abs(model_output['model_out'] - gt['img'])).mean()}


def image_mse_TV_prior(mask, k1, model, model_output, gt):
    coords_rand = 2 * (torch.rand((model_output['model_in'].shape[0],
                                   model_output['model_in'].shape[1] // 2,
                                   model_output['model_in'].shape[2])).cuda() - 0.5)
    rand_input = {'coords': coords_rand}
    rand_output = model(rand_input)

    if mask is None:
        return {'img_loss': ((model_output['model_out'] - gt['img']) ** 2).mean(),
                'prior_loss': k1 * (torch.abs(diff_operators.gradient(
                    rand_output['model_out'], rand_output['model_in']))).mean()}
    else:
        return {'img_loss': (mask * (model_output['model_out'] - gt['img']) ** 2).mean(),
                'prior_loss': k1 * (torch.abs(diff_operators.gradient(
                    rand_output['model_out'], rand_output['model_in']))).mean()}


def image_mse_FH_prior(mask, k1, model, model_output, gt):
    coords_rand = 2 * (torch.rand((model_output['model_in'].shape[0],
                                   model_output['model_in'].shape[1] // 2,
                                   model_output['model_in'].shape[2])).cuda() - 0.5)
    rand_input = {'coords': coords_rand}
    rand_output = model(rand_input)

    img_hessian, status = diff_operators.hessian(rand_output['model_out'],
                                                 rand_output['model_in'])
    img_hessian = img_hessian.view(*img_hessian.shape[0:2], -1)
    hessian_norm = img_hessian.norm(dim=-1, keepdim=True)

    if mask is None:
        return {'img_loss': ((model_output['model_out'] - gt['img']) ** 2).mean(),
                'prior_loss': k1 * (torch.abs(hessian_norm)).mean()}
    else:
        return {'img_loss': (mask * (model_output['model_out'] - gt['img']) ** 2).mean(),
                'prior_loss': k1 * (torch.abs(hessian_norm)).mean()}


def latent_loss(model_output):
    return torch.mean(model_output['latent_vec'] ** 2)


def hypo_weight_loss(model_output):
    weight_sum = 0
    total_weights = 0

    for weight in model_output['hypo_params'].values():
        weight_sum += torch.sum(weight ** 2)
        total_weights += weight.numel()

    return weight_sum * (1 / total_weights)


def image_hypernetwork_loss(mask, kl, fw, model_output, gt):
    return {'img_loss': image_mse(mask, model_output, gt)['img_loss'],
            'latent_loss': kl * latent_loss(model_output),
            'hypo_weight_loss': fw * hypo_weight_loss(model_output)}


def function_mse(model_output, gt):
    return {'func_loss': ((model_output['model_out'] - gt['func']) ** 2).mean()}


def gradients_mse(model_output, gt):
    # compute gradients on the model
    gradients = diff_operators.gradient(model_output['model_out'], model_output['model_in'])
    # compare them with the ground-truth
    gradients_loss = torch.mean((gradients - gt['gradients']).pow(2).sum(-1))
    return {'gradients_loss': gradients_loss}


def gradients_color_mse(model_output, gt):
    # compute gradients on the model
    gradients_r = diff_operators.gradient(model_output['model_out'][..., 0], model_output['model_in'])
    gradients_g = diff_operators.gradient(model_output['model_out'][..., 1], model_output['model_in'])
    gradients_b = diff_operators.gradient(model_output['model_out'][..., 2], model_output['model_in'])
    gradients = torch.cat((gradients_r, gradients_g, gradients_b), dim=-1)
    # compare them with the ground-truth
    weights = torch.tensor([1e1, 1e1, 1., 1., 1e1, 1e1]).cuda()
    gradients_loss = torch.mean((weights * (gradients[0:2] - gt['gradients']).pow(2)).sum(-1))
    return {'gradients_loss': gradients_loss}


def laplace_mse(model_output, gt):
    # compute laplacian on the model
    laplace = diff_operators.laplace(model_output['model_out'], model_output['model_in'])
    # compare them with the ground truth
    laplace_loss = torch.mean((laplace - gt['laplace']) ** 2)
    return {'laplace_loss': laplace_loss}


def wave_pml(model_output, gt):
    source_boundary_values = gt['source_boundary_values']
    x = model_output['model_in']  # (meta_batch_size, num_points, 3)
    y = model_output['model_out']  # (meta_batch_size, num_points, 1)
    squared_slowness = gt['squared_slowness']
    dirichlet_mask = gt['dirichlet_mask']
    batch_size = x.shape[1]

    du, status = diff_operators.jacobian(y, x)
    dudt = du[..., 0]

    if torch.all(dirichlet_mask):
        diff_constraint_hom = torch.Tensor([0])
    else:
        hess, status = diff_operators.jacobian(du[..., 0, :], x)
        lap = hess[..., 1, 1, None] + hess[..., 2, 2, None]
        dudt2 = hess[..., 0, 0, None]
        diff_constraint_hom = dudt2 - 1 / squared_slowness * lap

    dirichlet = y[dirichlet_mask] - source_boundary_values[dirichlet_mask]
    neumann = dudt[dirichlet_mask]

    return {'dirichlet': torch.abs(dirichlet).sum() * batch_size / 1e1,
            'neumann': torch.abs(neumann).sum() * batch_size / 1e2,
            'diff_constraint_hom': torch.abs(diff_constraint_hom).sum()}


def helmholtz_pml(model_output, gt):
    source_boundary_values = gt['source_boundary_values']

    if 'rec_boundary_values' in gt:
        rec_boundary_values = gt['rec_boundary_values']

    wavenumber = gt['wavenumber'].float()
    x = model_output['model_in']  # (meta_batch_size, num_points, 2)
    y = model_output['model_out']  # (meta_batch_size, num_points, 2)
    squared_slowness = gt['squared_slowness'].repeat(1, 1, y.shape[-1] // 2)
    batch_size = x.shape[1]

    full_waveform_inversion = False
    if 'pretrain' in gt:
        pred_squared_slowness = y[:, :, -1] + 1.
        if torch.all(gt['pretrain'] == -1):
            full_waveform_inversion = True
            pred_squared_slowness = torch.clamp(y[:, :, -1], min=-0.999) + 1.
            squared_slowness_init = torch.stack((torch.ones_like(pred_squared_slowness),
                                                 torch.zeros_like(pred_squared_slowness)), dim=-1)
            squared_slowness = torch.stack((pred_squared_slowness, torch.zeros_like(pred_squared_slowness)), dim=-1)
            squared_slowness = torch.where((torch.abs(x[..., 0, None]) > 0.75) | (torch.abs(x[..., 1, None]) > 0.75),
                                           squared_slowness_init, squared_slowness)
        y = y[:, :, :-1]

    du, status = diff_operators.jacobian(y, x)
    dudx1 = du[..., 0]
    dudx2 = du[..., 1]

    a0 = 5.0

    # let pml extend from -1. to -1 + Lpml and 1 - Lpml to 1.0
    Lpml = 0.5
    dist_west = -torch.clamp(x[..., 0] + (1.0 - Lpml), max=0)
    dist_east = torch.clamp(x[..., 0] - (1.0 - Lpml), min=0)
    dist_south = -torch.clamp(x[..., 1] + (1.0 - Lpml), max=0)
    dist_north = torch.clamp(x[..., 1] - (1.0 - Lpml), min=0)

    sx = wavenumber * a0 * ((dist_west / Lpml) ** 2 + (dist_east / Lpml) ** 2)[..., None]
    sy = wavenumber * a0 * ((dist_north / Lpml) ** 2 + (dist_south / Lpml) ** 2)[..., None]

    ex = torch.cat((torch.ones_like(sx), -sx / wavenumber), dim=-1)
    ey = torch.cat((torch.ones_like(sy), -sy / wavenumber), dim=-1)

    A = modules.compl_div(ey, ex).repeat(1, 1, dudx1.shape[-1] // 2)
    B = modules.compl_div(ex, ey).repeat(1, 1, dudx1.shape[-1] // 2)
    C = modules.compl_mul(ex, ey).repeat(1, 1, dudx1.shape[-1] // 2)

    a, _ = diff_operators.jacobian(modules.compl_mul(A, dudx1), x)
    b, _ = diff_operators.jacobian(modules.compl_mul(B, dudx2), x)

    a = a[..., 0]
    b = b[..., 1]
    c = modules.compl_mul(modules.compl_mul(C, squared_slowness), wavenumber ** 2 * y)

    diff_constraint_hom = a + b + c
    diff_constraint_on = torch.where(source_boundary_values != 0.,
                                     diff_constraint_hom - source_boundary_values,
                                     torch.zeros_like(diff_constraint_hom))
    diff_constraint_off = torch.where(source_boundary_values == 0.,
                                      diff_constraint_hom,
                                      torch.zeros_like(diff_constraint_hom))
    if full_waveform_inversion:
        data_term = torch.where(rec_boundary_values != 0, y - rec_boundary_values, torch.Tensor([0.]).cuda())
    else:
        data_term = torch.Tensor([0.])

        if 'pretrain' in gt:  # we are not trying to solve for velocity
            data_term = pred_squared_slowness - squared_slowness[..., 0]

    return {'diff_constraint_on': torch.abs(diff_constraint_on).sum() * batch_size / 1e3,
            'diff_constraint_off': torch.abs(diff_constraint_off).sum(),
            'data_term': torch.abs(data_term).sum() * batch_size / 1}


def sdf(model_output, gt):
    '''
       x: batch of input coordinates
       y: usually the output of the trial_soln function
       '''
    gt_sdf = gt['sdf']
    gt_normals = gt['normals']

    coords = model_output['model_in']
    pred_sdf = model_output['model_out']

    gradient = diff_operators.gradient(pred_sdf, coords)

    # Wherever boundary_values is not equal to zero, we interpret it as a boundary constraint.
    sdf_constraint = torch.where(gt_sdf != -1, pred_sdf, torch.zeros_like(pred_sdf))
    inter_constraint = torch.where(gt_sdf != -1, torch.zeros_like(pred_sdf), torch.exp(-1e2 * torch.abs(pred_sdf)))
    normal_constraint = torch.where(gt_sdf != -1, 1 - F.cosine_similarity(gradient, gt_normals, dim=-1)[..., None],
                                    torch.zeros_like(gradient[..., :1]))
    grad_constraint = torch.abs(gradient.norm(dim=-1) - 1)
    # Exp      # Lapl
    # -----------------
    return {'sdf': torch.abs(sdf_constraint).mean() * 3e3,  # 1e4      # 3e3
            'inter': inter_constraint.mean() * 1e2,  # 1e2                   # 1e3
            'normal_constraint': normal_constraint.mean() * 1e2,  # 1e2
            'grad_constraint': grad_constraint.mean() * 5e1}  # 1e1      # 5e1

# inter = 3e3 for ReLU-PE