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Source code for mmedit.models.editors.pconv.pconv_inpaintor

# Copyright (c) OpenMMLab. All rights reserved.
from typing import List

import torch

from mmedit.models.base_models import OneStageInpaintor
from mmedit.registry import MODELS
from mmedit.structures import EditDataSample, PixelData


@MODELS.register_module()
[docs]class PConvInpaintor(OneStageInpaintor): """Inpaintor for Partial Convolution method. This inpaintor is implemented according to the paper: Image inpainting for irregular holes using partial convolutions """
[docs] def forward_test(self, inputs, data_samples): """Forward function for testing. Args: inputs (torch.Tensor): Input tensor. data_samples (List[dict]): List of data sample dict. Returns: dict: Contain output results and eval metrics (if have). """ fake_reses, fake_imgs = self.forward_tensor(inputs, data_samples) predictions = [] for (fr, fi) in zip(fake_reses, fake_imgs): fi = (fi * 127.5 + 127.5) fr = (fr * 127.5 + 127.5) pred = EditDataSample( fake_res=fr, fake_img=fi, pred_img=PixelData(data=fi)) predictions.append(pred) return predictions
[docs] def forward_tensor(self, inputs, data_samples): """Forward function in tensor mode. Args: inputs (torch.Tensor): Input tensor. data_sample (dict): Dict contains data sample. Returns: dict: Dict contains output results. """ masked_img = inputs # N,3,H,W masks = torch.stack( list(d.mask.data for d in data_samples), dim=0) # N,1,H,W masks = 1. - masks masks = masks.repeat(1, 3, 1, 1) fake_reses, _ = self.generator(masked_img, masks) fake_imgs = fake_reses * (1. - masks) + masked_img * masks return fake_reses, fake_imgs
[docs] def train_step(self, data: List[dict], optim_wrapper): """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 (List[dict]): Batch of data as input. optim_wrapper (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. """ data = self.data_preprocessor(data, True) batch_inputs, data_samples = data['inputs'], data['data_samples'] log_vars = {} masked_img = batch_inputs # float gt_img = torch.stack([d.gt_img.data for d in data_samples]) # float, [-1,1] # print(gt_img.min(), gt_img.max(), gt_img.dtype) mask = torch.stack([d.mask.data for d in data_samples]) # uint8, {0,1} mask = mask.float() 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, gt_img, mask, masked_img) loss_g_, log_vars_g = self.parse_losses(g_losses) log_vars.update(log_vars_g) optim_wrapper.zero_grad() optim_wrapper.backward(loss_g_) optim_wrapper.step() return log_vars
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