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Source code for mmedit.models.inpaintors.aot_inpaintor

# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from pathlib import Path

import mmcv
import torch

from mmedit.core import tensor2img
from ..common import set_requires_grad
from ..registry import MODELS
from .one_stage import OneStageInpaintor

[docs]@MODELS.register_module() class AOTInpaintor(OneStageInpaintor): """Inpaintor for AOT-GAN method. This inpaintor is implemented according to the paper: Aggregated Contextual Transformations for High-Resolution Image Inpainting """
[docs] def forward_train_d(self, data_batch, is_real, is_disc, mask): """Forward function in discriminator training step. In this function, we compute the prediction for each data batch (real or fake). Meanwhile, the standard gan loss will be computed with several proposed losses for stable training. Args: data (torch.Tensor): Batch of real data or fake data. is_real (bool): If True, the gan loss will regard this batch as real data. Otherwise, the gan loss will regard this batch as fake data. is_disc (bool): If True, this function is called in discriminator training step. Otherwise, this function is called in generator training step. This will help us to compute different types of adversarial loss, like LSGAN. mask (torch.Tensor): Mask of data. Returns: dict: Contains the loss items computed in this function. """ pred = self.disc(data_batch) loss_ = self.loss_gan(pred, is_real, is_disc, mask=mask) loss = dict(real_loss=loss_) if is_real else dict(fake_loss=loss_) if self.with_disc_shift_loss: loss_d_shift = self.loss_disc_shift(loss_) # 0.5 for average the fake and real data loss.update(loss_disc_shift=loss_d_shift * 0.5) return loss
[docs] def generator_loss(self, fake_res, fake_img, data_batch): """Forward function in generator training step. In this function, we mainly compute the loss items for generator with the given (fake_res, fake_img). In general, the `fake_res` is the direct output of the generator and the `fake_img` is the composition of direct output and ground-truth image. Args: fake_res (torch.Tensor): Direct output of the generator. fake_img (torch.Tensor): Composition of `fake_res` and ground-truth image. data_batch (dict): Contain other elements for computing losses. Returns: tuple(dict): Dict contains the results computed within this function for visualization and dict contains the loss items computed in this function. """ gt = data_batch['gt_img'] mask = data_batch['mask'] masked_img = data_batch['masked_img'] loss = dict() if self.with_gan: pred = self.disc(fake_img) loss_g_fake = self.loss_gan(pred, True, False, mask=mask) loss['loss_g_fake'] = loss_g_fake if self.with_l1_valid_loss: loss_l1_valid = self.loss_l1_valid(fake_res, gt) loss['loss_l1_valid'] = loss_l1_valid if self.with_out_percep_loss: loss_out_percep, loss_out_style = self.loss_percep(fake_res, gt) if loss_out_percep is not None: loss['loss_out_percep'] = loss_out_percep if loss_out_style is not None: loss['loss_out_style'] = loss_out_style res = dict( gt_img=gt.cpu(), masked_img=masked_img.cpu(), fake_res=fake_res.cpu(), fake_img=fake_img.cpu()) return res, loss
[docs] def forward_test(self, masked_img, mask, save_image=False, save_path=None, iteration=None, **kwargs): """Forward function for testing. Args: masked_img (torch.Tensor): Tensor with shape of (n, 3, h, w). mask (torch.Tensor): Tensor with shape of (n, 1, h, w). save_image (bool, optional): If True, results will be saved as image. Default: False. save_path (str, optional): If given a valid str, the reuslts will be saved in this path. Default: None. iteration (int, optional): Iteration number. Default: None. Returns: dict: Contain output results and eval metrics (if exist). """ masked_img = masked_img.float() + mask input_x =[masked_img, mask], dim=1) fake_res = self.generator(input_x) fake_img = fake_res * mask + masked_img * (1. - mask) output = dict() eval_results = {} if self.eval_with_metrics: gt_img = kwargs['gt_img'] data_dict = dict( gt_img=gt_img, fake_res=fake_res, fake_img=fake_img, mask=None) for metric_name in self.test_cfg['metrics']: if metric_name in ['ssim', 'psnr']: eval_results[metric_name] = self._eval_metrics[ metric_name](tensor2img(fake_img, min_max=(-1, 1)), tensor2img(gt_img, min_max=(-1, 1))) else: eval_results[metric_name] = self._eval_metrics[ metric_name]()(data_dict).item() output['eval_result'] = eval_results else: output['fake_res'] = fake_res output['fake_img'] = fake_img output['meta'] = None if 'meta' not in kwargs else kwargs['meta'][0] if save_image: assert save_image and save_path is not None, ( 'Save path should been given') assert output['meta'] is not None, ( 'Meta information should be given to save image.') tmp_filename = output['meta']['gt_img_path'] filestem = Path(tmp_filename).stem if iteration is not None: filename = f'{filestem}_{iteration}.png' else: filename = f'{filestem}.png' mmcv.mkdir_or_exist(save_path) img_list = [kwargs['gt_img']] if 'gt_img' in kwargs else [] img_list.extend( [masked_img, mask.expand_as(masked_img), fake_res, fake_img]) img =, dim=3).cpu() self.save_visualization(img, osp.join(save_path, filename)) output['save_img_path'] = osp.abspath( osp.join(save_path, filename)) return output
[docs] def train_step(self, data_batch, optimizer): """Train step function. In this function, the inpaintor will finish the train step following the pipeline: 1. get fake res/image 2. compute reconstruction losses for generator 3. compute adversarial loss for discriminator 4. optimize generator 5. optimize discriminator Args: data_batch (torch.Tensor): Batch of data as input. optimizer (dict[torch.optim.Optimizer]): Dict with optimizers for generator and discriminator (if exist). Returns: dict: Dict with loss, information for logger, the number of samples and results for visualization. """ log_vars = {} gt_img = data_batch['gt_img'] mask = data_batch['mask'] masked_img = data_batch['masked_img'] masked_img = masked_img.float() + mask # get common output from encdec input_x =[masked_img, mask], dim=1) fake_res = self.generator(input_x) fake_img = gt_img * (1. - mask) + fake_res * mask # discriminator training step if self.train_cfg.disc_step > 0: set_requires_grad(self.disc, True) disc_losses_real = self.forward_train_d( gt_img, True, True, mask=mask) disc_losses_fake = self.forward_train_d( fake_img.detach(), False, True, mask=mask) disc_losses_ = disc_losses_real['real_loss'] + disc_losses_fake[ 'fake_loss'] disc_losses = dict(disc_losses=disc_losses_) loss_disc, log_vars_d = self.parse_losses(disc_losses) log_vars.update(log_vars_d) optimizer['disc'].zero_grad() loss_disc.backward() optimizer['disc'].step() self.disc_step_count = (self.disc_step_count + 1) % self.train_cfg.disc_step if self.disc_step_count != 0: # results contain the data for visualization results = dict( gt_img=gt_img.cpu(), masked_img=masked_img.cpu(), fake_res=fake_res.cpu(), fake_img=fake_img.cpu()) outputs = dict( log_vars=log_vars, num_samples=len(data_batch['gt_img'].data), results=results) return outputs # generator (encdec) training step, results contain the data # for visualization if self.with_gan: set_requires_grad(self.disc, False) results, g_losses = self.generator_loss(fake_res, fake_img, data_batch) loss_g, log_vars_g = self.parse_losses(g_losses) log_vars.update(log_vars_g) optimizer['generator'].zero_grad() loss_g.backward() optimizer['generator'].step() outputs = dict( log_vars=log_vars, num_samples=len(data_batch['gt_img'].data), results=results) return outputs
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