Shortcuts

Note

You are reading the documentation for MMEditing 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMEditing 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMEditing 1.0 for more details.

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
Read the Docs v: latest
Versions
latest
stable
1.x
v0.16.0
v0.15.2
v0.15.1
v0.15.0
v0.14.0
v0.13.0
v0.12.0
dev-1.x
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.