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Source code for mmedit.models.inpaintors.deepfillv1

# Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch.nn.parallel import DataParallel, DistributedDataParallel

from ..common import extract_around_bbox, extract_bbox_patch, set_requires_grad
from ..registry import MODELS
from .two_stage import TwoStageInpaintor


[docs]@MODELS.register_module() class DeepFillv1Inpaintor(TwoStageInpaintor):
[docs] def get_module(self, model, module_name): """Get an inner module from model. Since we will wrapper DDP for some model, we have to judge whether the module can be indexed directly. Args: model (nn.Module): This model may wrapped with DDP or not. module_name (str): The name of specific module. Return: nn.Module: Returned sub module. """ if isinstance(model, (DataParallel, DistributedDataParallel)): return getattr(model.module, module_name) return getattr(model, module_name)
[docs] def forward_train_d(self, data_batch, is_real, is_disc): """Forward function in discriminator training step. In this function, we modify the default implementation with only one discriminator. In DeepFillv1 model, they use two separated discriminators for global and local consistency. 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. Returns: dict: Contains the loss items computed in this function. """ global_pred, local_pred = self.disc(data_batch) loss_global = self.loss_gan(global_pred, is_real, is_disc) loss_local = self.loss_gan(local_pred, is_real, is_disc) if is_real: loss = dict( real_loss_global=loss_global, real_loss_local=loss_local) else: loss = dict( fake_loss_global=loss_global, fake_loss_local=loss_local) if self.with_disc_shift_loss: loss_d_shift_global = self.loss_disc_shift(loss_global) loss_d_shift_local = self.loss_disc_shift(loss_local) # 0.5 for average the fake and real data loss.update(loss_disc_shift_global=loss_d_shift_global * 0.5) loss.update(loss_disc_shift_local=loss_d_shift_local * 0.5) return loss
[docs] def two_stage_loss(self, stage1_data, stage2_data, data_batch): """Calculate two-stage loss. Args: stage1_data (dict): Contain stage1 results. stage2_data (dict): Contain stage2 results. data_batch (dict): Contain data needed to calculate loss. Returns: dict: Contain losses with name. """ gt = data_batch['gt_img'] mask = data_batch['mask'] masked_img = data_batch['masked_img'] loss = dict() results = dict( gt_img=gt.cpu(), mask=mask.cpu(), masked_img=masked_img.cpu()) # calculate losses for stage1 if self.stage1_loss_type is not None: fake_res = stage1_data['fake_res'] fake_img = stage1_data['fake_img'] for type_key in self.stage1_loss_type: tmp_loss = self.calculate_loss_with_type( type_key, fake_res, fake_img, gt, mask, prefix='stage1_') loss.update(tmp_loss) results.update( dict( stage1_fake_res=stage1_data['fake_res'].cpu(), stage1_fake_img=stage1_data['fake_img'].cpu())) if self.stage2_loss_type is not None: fake_res = stage2_data['fake_res'] fake_img = stage2_data['fake_img'] fake_local = stage2_data['fake_local'] for type_key in self.stage2_loss_type: tmp_loss = self.calculate_loss_with_type( type_key, fake_res, fake_img, gt, mask, prefix='stage2_', fake_local=fake_local) loss.update(tmp_loss) results.update( dict( stage2_fake_res=stage2_data['fake_res'].cpu(), stage2_fake_img=stage2_data['fake_img'].cpu())) return results, loss
[docs] def calculate_loss_with_type(self, loss_type, fake_res, fake_img, gt, mask, prefix='stage1_', fake_local=None): """Calculate multiple types of losses. Args: loss_type (str): Type of the loss. fake_res (torch.Tensor): Direct results from model. fake_img (torch.Tensor): Composited results from model. gt (torch.Tensor): Ground-truth tensor. mask (torch.Tensor): Mask tensor. prefix (str, optional): Prefix for loss name. Defaults to 'stage1_'. fake_local (torch.Tensor, optional): Local results from model. Defaults to None. Returns: dict: Contain loss value with its name. """ loss_dict = dict() if loss_type == 'loss_gan': g_fake_global_pred, g_fake_local_pred = self.disc( (fake_img, fake_local)) loss_g_fake_global = self.loss_gan( g_fake_global_pred, True, is_disc=False) loss_g_fake_local = self.loss_gan( g_fake_local_pred, True, is_disc=False) loss_dict[prefix + 'loss_g_fake'] = loss_g_fake_global + loss_g_fake_local elif 'percep' in loss_type: loss_pecep, loss_style = self.loss_percep(fake_img, gt) if loss_pecep is not None: loss_dict[prefix + loss_type] = loss_pecep if loss_style is not None: loss_dict[prefix + loss_type[:-6] + 'style'] = loss_style elif 'tv' in loss_type: loss_tv = self.loss_tv(fake_img, mask=mask) loss_dict[prefix + loss_type] = loss_tv elif 'l1' in loss_type: weight = 1. - mask if 'valid' in loss_type else mask loss_l1 = getattr(self, loss_type)(fake_res, gt, weight=weight) loss_dict[prefix + loss_type] = loss_l1 else: raise NotImplementedError( f'Please check your loss type {loss_type}' ' and the config dict in init function. ' 'We cannot find the related loss function.') return loss_dict
[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'] bbox_tensor = data_batch['mask_bbox'] # get common output from encdec if self.input_with_ones: tmp_ones = torch.ones_like(mask) input_x = torch.cat([masked_img, tmp_ones, mask], dim=1) else: input_x = torch.cat([masked_img, mask], dim=1) stage1_fake_res, stage2_fake_res = self.generator(input_x) stage1_fake_img = masked_img * (1. - mask) + stage1_fake_res * mask stage2_fake_img = masked_img * (1. - mask) + stage2_fake_res * mask stage2_fake_local, bbox_new = extract_around_bbox( stage2_fake_img, bbox_tensor, self.train_cfg.local_size) gt_local = extract_bbox_patch(bbox_new, gt_img) fake_gt_local = torch.cat([stage2_fake_local, gt_local], dim=2) # discriminator training step # In this version, we only use the results from the second stage to # train discriminators, which is a commonly used setting. This can be # easily modified to your custom training schedule. if self.train_cfg.disc_step > 0 and self.with_gan: set_requires_grad(self.disc, True) fake_data = (stage2_fake_img.detach(), stage2_fake_local.detach()) real_data = (gt_img, gt_local) disc_losses = self.forward_train_d(fake_data, False, is_disc=True) loss_disc, log_vars_d = self.parse_losses(disc_losses) log_vars.update(log_vars_d) optimizer['disc'].zero_grad() loss_disc.backward() disc_losses = self.forward_train_d(real_data, True, is_disc=True) loss_disc, log_vars_d = self.parse_losses(disc_losses) log_vars.update(log_vars_d) loss_disc.backward() if self.with_gp_loss: loss_gp_global = self.loss_gp( self.get_module(self.disc, 'global_disc'), gt_img, stage2_fake_img, mask=mask) loss_gp_local = self.loss_gp( self.get_module(self.disc, 'local_disc'), gt_local, stage2_fake_local) loss_disc, log_vars_d = self.parse_losses( dict( loss_gp_global=loss_gp_global, loss_gp_local=loss_gp_local)) log_vars.update(log_vars_d) 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(), stage1_fake_res=stage1_fake_res.cpu(), stage1_fake_img=stage1_fake_img.cpu(), stage2_fake_res=stage2_fake_res.cpu(), stage2_fake_img=stage2_fake_img.cpu(), fake_gt_local=fake_gt_local.cpu(), fake_res=stage2_fake_res.cpu(), fake_img=stage2_fake_img.cpu()) outputs = dict( log_vars=log_vars, num_samples=len(data_batch['gt_img'].data), results=results) return outputs # prepare stage1 results and stage2 results dict for calculating losses stage1_results = dict( fake_res=stage1_fake_res, fake_img=stage1_fake_img) stage2_results = dict( fake_res=stage2_fake_res, fake_img=stage2_fake_img, fake_local=stage2_fake_local) # generator (encdec) and refiner training step, results contain the # data for visualization if self.with_gan: set_requires_grad(self.disc, False) results, two_stage_losses = self.two_stage_loss( stage1_results, stage2_results, data_batch) loss_two_stage, log_vars_two_stage = self.parse_losses( two_stage_losses) log_vars.update(log_vars_two_stage) optimizer['generator'].zero_grad() loss_two_stage.backward() optimizer['generator'].step() results['fake_gt_local'] = fake_gt_local.cpu() outputs = dict( log_vars=log_vars, num_samples=len(data_batch['gt_img'].data), results=results) return outputs
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