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Source code for mmedit.models.inpaintors.pconv_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 ..registry import MODELS
from .one_stage import OneStageInpaintor


[docs]@MODELS.register_module() class PConvInpaintor(OneStageInpaintor):
[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. Defaults to False. save_path (str, optional): If given a valid str, the results will be saved in this path. Defaults to None. iteration (int, optional): Iteration number. Defaults to None. Returns: dict: Contain output results and eval metrics (if have). """ mask_input = mask.expand_as(masked_img) mask_input = 1. - mask_input fake_res, final_mask = self.generator(masked_img, mask_input) fake_img = fake_res * mask + masked_img * (1. - mask) output = dict() eval_result = {} if self.eval_with_metrics: gt_img = kwargs['gt_img'] data_dict = dict(gt_img=gt_img, fake_res=fake_res, mask=mask) for metric_name in self.test_cfg['metrics']: if metric_name in ['ssim', 'psnr']: eval_result[metric_name] = self._eval_metrics[metric_name]( tensor2img(fake_img, min_max=(-1, 1)), tensor2img(gt_img, min_max=(-1, 1))) else: eval_result[metric_name] = self._eval_metrics[metric_name]( )(data_dict).item() output['eval_result'] = eval_result else: output['fake_res'] = fake_res output['fake_img'] = fake_img output['final_mask'] = final_mask 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) if kwargs.get('gt_img', None) is not None: img_list = [kwargs['gt_img']] else: img_list = [] img_list.extend( [masked_img, mask.expand_as(masked_img), fake_res, fake_img]) img = torch.cat(img_list, 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. optimize discriminator (if have) 3. optimize generator If `self.train_cfg.disc_step > 1`, the train step will contain multiple iterations for optimizing discriminator with different input data and only one iteration for optimizing gerator after `disc_step` iterations for discriminator. Args: data_batch (torch.Tensor): Batch of data as input. optimizer (dict[torch.optim.Optimizer]): Dict with optimizers for generator and discriminator (if have). 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'] mask_input = mask.expand_as(gt_img) mask_input = 1. - mask_input fake_res, final_mask = self.generator(masked_img, mask_input) fake_img = gt_img * (1. - mask) + fake_res * mask 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() results.update(dict(final_mask=final_mask)) outputs = dict( log_vars=log_vars, num_samples=len(data_batch['gt_img'].data), results=results) return outputs
[docs] def forward_dummy(self, x): mask = x[:, -3:, ...].clone() x = x[:, :-3, ...] res, _ = self.generator(x, mask) return res
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