lib/train/trainers/nerf.py

import torch.nn as nn
from lib.config import cfg
import torch
from lib.networks.renderer import volume_renderer
from lib.train import make_optimizer


class NetworkWrapper(nn.Module):
    def __init__(self, net):
        super(NetworkWrapper, self).__init__()

        self.net = net
        self.renderer = volume_renderer.Renderer(self.net)

        self.img2mse = lambda x, y : torch.mean((x - y) ** 2)
        self.acc_crit = torch.nn.functional.smooth_l1_loss

    def forward(self, batch):
        ret = self.renderer.render(batch)

        scalar_stats = {}
        loss = 0

        mask = batch['mask_at_box']
        img_loss = self.img2mse(ret['rgb_map'][mask], batch['rgb'][mask])
        scalar_stats.update({'img_loss': img_loss})
        loss += img_loss

        if 'rgb0' in ret:
            img_loss0 = self.img2mse(ret['rgb0'], batch['rgb'])
            scalar_stats.update({'img_loss0': img_loss0})
            loss += img_loss0

        scalar_stats.update({'loss': loss})
        image_stats = {}

        return ret, loss, scalar_stats, image_stats