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mmedit.models.base_models.one_stage 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple, Union

import torch
from mmengine.config import Config
from mmengine.model import BaseModel
from mmengine.optim import OptimWrapperDict

from mmedit.registry import MODELS
from mmedit.structures import EditDataSample, PixelData
from mmedit.utils import SampleList
from ..utils import set_requires_grad

[文档]FORWARD_RETURN_TYPE = Union[dict, torch.Tensor, Tuple[torch.Tensor, torch.Tensor], SampleList]
@MODELS.register_module()
[文档]class OneStageInpaintor(BaseModel): """Standard one-stage inpaintor with commonly used losses. An inpaintor must contain an encoder-decoder style generator to inpaint masked regions. A discriminator will be adopted when adversarial training is needed. In this class, we provide a common interface for inpaintors. For other inpaintors, only some funcs may be modified to fit the input style or training schedule. Args: data_preprocessor (dict): Config of data_preprocessor. encdec (dict): Config for encoder-decoder style generator. disc (dict): Config for discriminator. loss_gan (dict): Config for adversarial loss. loss_gp (dict): Config for gradient penalty loss. loss_disc_shift (dict): Config for discriminator shift loss. loss_composed_percep (dict): Config for perceptual and style loss with composed image as input. loss_out_percep (dict): Config for perceptual and style loss with direct output as input. loss_l1_hole (dict): Config for l1 loss in the hole. loss_l1_valid (dict): Config for l1 loss in the valid region. loss_tv (dict): Config for total variation loss. train_cfg (dict): Configs for training scheduler. `disc_step` must be contained for indicates the discriminator updating steps in each training step. test_cfg (dict): Configs for testing scheduler. init_cfg (dict, optional): Initialization config dict. """ def __init__(self, data_preprocessor: Union[dict, Config], encdec: dict, disc: Optional[dict] = None, loss_gan: Optional[dict] = None, loss_gp: Optional[dict] = None, loss_disc_shift: Optional[dict] = None, loss_composed_percep: Optional[dict] = None, loss_out_percep: bool = False, loss_l1_hole: Optional[dict] = None, loss_l1_valid: Optional[dict] = None, loss_tv: Optional[dict] = None, train_cfg: Optional[dict] = None, test_cfg: Optional[dict] = None, init_cfg: Optional[dict] = None): super().__init__( data_preprocessor=data_preprocessor, init_cfg=init_cfg) self.with_l1_hole_loss = loss_l1_hole is not None self.with_l1_valid_loss = loss_l1_valid is not None self.with_tv_loss = loss_tv is not None self.with_composed_percep_loss = loss_composed_percep is not None self.with_out_percep_loss = loss_out_percep self.with_gan = disc is not None and loss_gan is not None self.with_gp_loss = loss_gp is not None self.with_disc_shift_loss = loss_disc_shift is not None self.is_train = train_cfg is not None self.train_cfg = train_cfg self.test_cfg = test_cfg self.generator = MODELS.build(encdec) # build loss modules if self.with_gan: self.disc = MODELS.build(disc) self.loss_gan = MODELS.build(loss_gan) if self.with_l1_hole_loss: self.loss_l1_hole = MODELS.build(loss_l1_hole) if self.with_l1_valid_loss: self.loss_l1_valid = MODELS.build(loss_l1_valid) if self.with_composed_percep_loss: self.loss_percep = MODELS.build(loss_composed_percep) if self.with_gp_loss: self.loss_gp = MODELS.build(loss_gp) if self.with_disc_shift_loss: self.loss_disc_shift = MODELS.build(loss_disc_shift) if self.with_tv_loss: self.loss_tv = MODELS.build(loss_tv) self.disc_step_count = 0
[文档] def forward(self, inputs: torch.Tensor, data_samples: Optional[SampleList], mode: str = 'tensor') -> FORWARD_RETURN_TYPE: """Forward function. Args: inputs (torch.Tensor): batch input tensor collated by :attr:`data_preprocessor`. data_samples (List[BaseDataElement], optional): data samples collated by :attr:`data_preprocessor`. mode (str): mode should be one of ``loss``, ``predict`` and ``tensor``. Default: 'tensor'. - ``loss``: Called by ``train_step`` and return loss ``dict`` used for logging - ``predict``: Called by ``val_step`` and ``test_step`` and return list of ``BaseDataElement`` results used for computing metric. - ``tensor``: Called by custom use to get ``Tensor`` type results. Returns: ForwardResults: - If ``mode == loss``, return a ``dict`` of loss tensor used for backward and logging. - If ``mode == predict``, return a ``list`` of :obj:`BaseDataElement` for computing metric and getting inference result. - If ``mode == tensor``, return a tensor or ``tuple`` of tensor or ``dict`` or tensor for custom use. """ if mode == 'tensor': raw = self.forward_tensor(inputs, data_samples) return raw elif mode == 'predict': # Pre-process runs in BaseModel.val_step / test_step predictions = self.forward_test(inputs, data_samples) predictions = self.convert_to_datasample(data_samples, predictions) return predictions elif mode == 'loss': raise NotImplementedError('This mode should not be used in ' 'current training schedule. Please use ' '`train_step` for training.') else: raise ValueError('Invalid forward mode.')
[文档] def train_step(self, data: List[dict], optim_wrapper: OptimWrapperDict) -> dict: """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] mask = torch.stack([d.mask.data for d in data_samples]) # uint8, {0,1} mask = mask.float() # get common output from encdec input_x = torch.cat([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 = self.forward_train_d( fake_img.detach(), False, is_disc=True) loss_disc, log_vars_d = self.parse_losses(disc_losses) log_vars.update(log_vars_d) optim_wrapper['disc'].zero_grad() loss_disc.backward() disc_losses = self.forward_train_d(gt_img, 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_d_gp = self.loss_gp( self.disc, gt_img, fake_img, mask=mask) loss_disc, log_vars_d = self.parse_losses( dict(loss_gp=loss_d_gp)) log_vars.update(log_vars_d) loss_disc.backward() optim_wrapper['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 log_vars # 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, gt_img, mask, masked_img) loss_g, log_vars_g = self.parse_losses(g_losses) log_vars.update(log_vars_g) optim_wrapper['generator'].zero_grad() loss_g.backward() optim_wrapper['generator'].step() # outputs = dict( # log_vars=log_vars, # num_samples=len(data_batch['gt_img'].data), # results=results) return log_vars
[文档] def forward_train(self, *args, **kwargs) -> None: """Forward function for training. In this version, we do not use this interface. """ raise NotImplementedError('This interface should not be used in '
'current training schedule. Please use ' '`train_step` for training.')
[文档] def forward_train_d(self, data_batch: torch.Tensor, is_real: bool, is_disc: bool) -> dict: """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_batch (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. """ pred = self.disc(data_batch) loss_ = self.loss_gan(pred, is_real, is_disc) 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
[文档] def generator_loss(self, fake_res: torch.Tensor, fake_img: torch.Tensor, gt: torch.Tensor, mask: torch.Tensor, masked_img: torch.Tensor) -> Tuple[dict, dict]: """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. gt (torch.Tensor): Ground-truth image. mask (torch.Tensor): Mask image. masked_img (torch.Tensor): Composition of mask image and ground-truth image. Returns: tuple(dict): Dict contains the results computed within this \ function for visualization and dict contains the loss items \ computed in this function. """ loss = dict() if self.with_gan: g_fake_pred = self.disc(fake_img) loss_g_fake = self.loss_gan(g_fake_pred, True, is_disc=False) loss['loss_g_fake'] = loss_g_fake if self.with_l1_hole_loss: loss_l1_hole = self.loss_l1_hole(fake_res, gt, weight=mask) loss['loss_l1_hole'] = loss_l1_hole if self.with_l1_valid_loss: loss_loss_l1_valid = self.loss_l1_valid( fake_res, gt, weight=1. - mask) loss['loss_l1_valid'] = loss_loss_l1_valid if self.with_composed_percep_loss: loss_pecep, loss_style = self.loss_percep(fake_img, gt) if loss_pecep is not None: loss['loss_composed_percep'] = loss_pecep if loss_style is not None: loss['loss_composed_style'] = loss_style 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 if self.with_tv_loss: loss_tv = self.loss_tv(fake_img, mask=mask) loss['loss_tv'] = loss_tv res = dict( gt_img=gt.cpu(), masked_img=masked_img.cpu(), fake_res=fake_res.cpu(), fake_img=fake_img.cpu()) return res, loss
[文档] def forward_tensor(self, inputs: torch.Tensor, data_samples: SampleList ) -> Tuple[torch.Tensor, torch.Tensor]: """Forward function in tensor mode. Args: inputs (torch.Tensor): Input tensor. data_samples (List[dict]): List of data sample dict. Returns: tuple: Direct output of the generator and composition of `fake_res` and ground-truth image. """ # Pre-process runs in BaseModel.val_step / test_step masked_imgs = inputs # N,3,H,W masks = torch.stack( list(d.mask.data for d in data_samples), dim=0) # N,1,H,W input_xs = torch.cat([masked_imgs, masks], dim=1) # N,4,H,W fake_reses = self.generator(input_xs) fake_imgs = fake_reses * masks + masked_imgs * (1. - masks) return fake_reses, fake_imgs
[文档] def forward_test(self, inputs: torch.Tensor, data_samples: SampleList) -> SampleList: """Forward function for testing. Args: inputs (torch.Tensor): Input tensor. data_samples (List[dict]): List of data sample dict. Returns: predictions (List[DataSample]): List of prediction saved in DataSample. """ 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
[文档] def convert_to_datasample(self, inputs: SampleList, data_samples: SampleList) -> SampleList: for data_sample, output in zip(inputs, data_samples): data_sample.output = output return inputs
[文档] def forward_dummy(self, x: torch.Tensor) -> torch.Tensor: """Forward dummy function for getting flops. Args: x (torch.Tensor): Input tensor with shape of (n, c, h, w). Returns: torch.Tensor: Results tensor with shape of (n, 3, h, w). """ res = self.generator(x) return res
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