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Source code for mmedit.models.losses.loss_comps.disc_auxiliary_loss_comps

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

import torch
import torch.nn as nn

from mmedit.registry import MODELS
from ..gan_loss import (disc_shift_loss, gradient_penalty_loss,
                        r1_gradient_penalty_loss)


@MODELS.register_module()
[docs]class DiscShiftLossComps(nn.Module): """Disc Shift Loss. This loss is proposed in PGGAN as an auxiliary loss for discriminator. **Note for the design of ``data_info``:** In ``MMEditing``, almost all of loss modules contain the argument ``data_info``, which can be used for constructing the link between the input items (needed in loss calculation) and the data from the generative model. For example, in the training of GAN model, we will collect all of important data/modules into a dictionary: .. code-block:: python :caption: Code from StaticUnconditionalGAN, train_step :linenos: data_dict_ = dict( gen=self.generator, disc=self.discriminator, disc_pred_fake=disc_pred_fake, disc_pred_real=disc_pred_real, fake_imgs=fake_imgs, real_imgs=real_imgs, iteration=curr_iter, batch_size=batch_size) But in this loss, we will need to provide ``pred`` as input. Thus, an example of the ``data_info`` is: .. code-block:: python :linenos: data_info = dict( pred='disc_pred_fake') Then, the module will automatically construct this mapping from the input data dictionary. In addition, in general, ``disc_shift_loss`` will be applied over real and fake data. In this case, users just need to add this loss module twice, but with different ``data_info``. Our model will automatically add these two items. Args: loss_weight (float, optional): Weight of this loss item. Defaults to ``1.``. data_info (dict, optional): Dictionary contains the mapping between loss input args and data dictionary. If ``None``, this module will directly pass the input data to the loss function. Defaults to None. loss_name (str, optional): Name of the loss item. If you want this loss item to be included into the backward graph, `loss_` must be the prefix of the name. Defaults to 'loss_disc_shift'. """ def __init__(self, loss_weight: float = 1.0, data_info: Optional[dict] = None, loss_name: str = 'loss_disc_shift') -> None: super().__init__() self.loss_weight = loss_weight self.data_info = data_info self._loss_name = loss_name
[docs] def forward(self, *args, **kwargs) -> torch.Tensor: """Forward function. If ``self.data_info`` is not ``None``, a dictionary containing all of the data and necessary modules should be passed into this function. If this dictionary is given as a non-keyword argument, it should be offered as the first argument. If you are using keyword argument, please name it as `outputs_dict`. If ``self.data_info`` is ``None``, the input argument or key-word argument will be directly passed to loss function, ``disc_shift_loss``. """ # use data_info to build computational path if self.data_info is not None: # parse the args and kwargs if len(args) == 1: assert isinstance(args[0], dict), ( 'You should offer a dictionary containing network outputs ' 'for building up computational graph of this loss module.') outputs_dict = args[0] elif 'outputs_dict' in kwargs: assert len(args) == 0, ( 'If the outputs dict is given in keyworded arguments, no' ' further non-keyworded arguments should be offered.') outputs_dict = kwargs.pop('outputs_dict') else: raise NotImplementedError( 'Cannot parsing your arguments passed to this loss module.' ' Please check the usage of this module') # link the outputs with loss input args according to self.data_info loss_input_dict = { k: outputs_dict[v] for k, v in self.data_info.items() } kwargs.update(loss_input_dict) return disc_shift_loss(**kwargs) * self.loss_weight else: # if you have not define how to build computational graph, this # module will just directly return the loss as usual. return disc_shift_loss(*args, **kwargs) * self.loss_weight
[docs] def loss_name(self) -> str: """Loss Name. This function must be implemented and will return the name of this loss function. This name will be used to combine different loss items by simple sum operation. In addition, if you want this loss item to be included into the backward graph, `loss_` must be the prefix of the name. Returns: str: The name of this loss item. """ return self._loss_name
@MODELS.register_module()
[docs]class GradientPenaltyLossComps(nn.Module): """Gradient Penalty for WGAN-GP. In the detailed implementation, there are two streams where one uses the pixel-wise gradient norm, but the other adopts normalization along instance (HWC) dimensions. Thus, ``norm_mode`` are offered to define which mode you want. **Note for the design of ``data_info``:** In ``MMEditing``, almost all of loss modules contain the argument ``data_info``, which can be used for constructing the link between the input items (needed in loss calculation) and the data from the generative model. For example, in the training of GAN model, we will collect all of important data/modules into a dictionary: .. code-block:: python :caption: Code from StaticUnconditionalGAN, train_step :linenos: data_dict_ = dict( gen=self.generator, disc=self.discriminator, disc_pred_fake=disc_pred_fake, disc_pred_real=disc_pred_real, fake_imgs=fake_imgs, real_imgs=real_imgs, iteration=curr_iter, batch_size=batch_size) But in this loss, we will need to provide ``discriminator``, ``real_data``, and ``fake_data`` as input. Thus, an example of the ``data_info`` is: .. code-block:: python :linenos: data_info = dict( discriminator='disc', real_data='real_imgs', fake_data='fake_imgs') Then, the module will automatically construct this mapping from the input data dictionary. Args: loss_weight (float, optional): Weight of this loss item. Defaults to ``1.``. data_info (dict, optional): Dictionary contains the mapping between loss input args and data dictionary. If ``None``, this module will directly pass the input data to the loss function. Defaults to None. norm_mode (str): This argument decides along which dimension the norm of the gradients will be calculated. Currently, we support ["pixel" , "HWC"]. Defaults to "pixel". loss_name (str, optional): Name of the loss item. If you want this loss item to be included into the backward graph, `loss_` must be the prefix of the name. Defaults to 'loss_gp'. """ def __init__(self, loss_weight: float = 1.0, norm_mode: str = 'pixel', data_info: Optional[dict] = None, loss_name: str = 'loss_gp') -> None: super().__init__() self.loss_weight = loss_weight self.norm_mode = norm_mode self.data_info = data_info self._loss_name = loss_name
[docs] def forward(self, *args, **kwargs) -> torch.Tensor: """Forward function. If ``self.data_info`` is not ``None``, a dictionary containing all of the data and necessary modules should be passed into this function. If this dictionary is given as a non-keyword argument, it should be offered as the first argument. If you are using keyword argument, please name it as `outputs_dict`. If ``self.data_info`` is ``None``, the input argument or key-word argument will be directly passed to loss function, ``gradient_penalty_loss``. """ # use data_info to build computational path if self.data_info is not None: # parse the args and kwargs if len(args) == 1: assert isinstance(args[0], dict), ( 'You should offer a dictionary containing network outputs ' 'for building up computational graph of this loss module.') outputs_dict = args[0] elif 'outputs_dict' in kwargs: assert len(args) == 0, ( 'If the outputs dict is given in keyworded arguments, no' ' further non-keyworded arguments should be offered.') outputs_dict = kwargs.pop('outputs_dict') else: raise NotImplementedError( 'Cannot parsing your arguments passed to this loss module.' ' Please check the usage of this module') # link the outputs with loss input args according to self.data_info loss_input_dict = { k: outputs_dict[v] for k, v in self.data_info.items() } kwargs.update(loss_input_dict) # kwargs.update( # dict(weight=self.loss_weight, norm_mode=self.norm_mode)) kwargs.update(dict(norm_mode=self.norm_mode)) return gradient_penalty_loss(**kwargs) * self.loss_weight else: # if you have not define how to build computational graph, this # module will just directly return the loss as usual. # return gradient_penalty_loss( # *args, weight=self.loss_weight, **kwargs) return gradient_penalty_loss(*args, **kwargs) * self.loss_weight
[docs] def loss_name(self) -> str: """Loss Name. This function must be implemented and will return the name of this loss function. This name will be used to combine different loss items by simple sum operation. In addition, if you want this loss item to be included into the backward graph, `loss_` must be the prefix of the name. Returns: str: The name of this loss item. """ return self._loss_name
@MODELS.register_module()
[docs]class R1GradientPenaltyComps(nn.Module): """R1 gradient penalty for WGAN-GP. R1 regularizer comes from: "Which Training Methods for GANs do actually Converge?" ICML'2018 Different from original gradient penalty, this regularizer only penalized gradient w.r.t. real data. **Note for the design of ``data_info``:** In ``MMEditing``, almost all of loss modules contain the argument ``data_info``, which can be used for constructing the link between the input items (needed in loss calculation) and the data from the generative model. For example, in the training of GAN model, we will collect all of important data/modules into a dictionary: .. code-block:: python :caption: Code from StaticUnconditionalGAN, train_step :linenos: data_dict_ = dict( gen=self.generator, disc=self.discriminator, disc_pred_fake=disc_pred_fake, disc_pred_real=disc_pred_real, fake_imgs=fake_imgs, real_imgs=real_imgs, iteration=curr_iter, batch_size=batch_size) But in this loss, we will need to provide ``discriminator`` and ``real_data`` as input. Thus, an example of the ``data_info`` is: .. code-block:: python :linenos: data_info = dict( discriminator='disc', real_data='real_imgs') Then, the module will automatically construct this mapping from the input data dictionary. Args: loss_weight (float, optional): Weight of this loss item. Defaults to ``1.``. data_info (dict, optional): Dictionary contains the mapping between loss input args and data dictionary. If ``None``, this module will directly pass the input data to the loss function. Defaults to None. norm_mode (str): This argument decides along which dimension the norm of the gradients will be calculated. Currently, we support ["pixel" , "HWC"]. Defaults to "pixel". interval (int, optional): The interval of calculating this loss. Defaults to 1. loss_name (str, optional): Name of the loss item. If you want this loss item to be included into the backward graph, `loss_` must be the prefix of the name. Defaults to 'loss_r1_gp'. """ def __init__(self, loss_weight: float = 1.0, norm_mode: str = 'pixel', interval: int = 1, data_info: Optional[dict] = None, use_apex_amp: bool = False, loss_name: str = 'loss_r1_gp') -> None: super().__init__() self.loss_weight = loss_weight self.norm_mode = norm_mode self.interval = interval self.data_info = data_info self.use_apex_amp = use_apex_amp self._loss_name = loss_name
[docs] def forward(self, *args, **kwargs) -> torch.Tensor: """Forward function. If ``self.data_info`` is not ``None``, a dictionary containing all of the data and necessary modules should be passed into this function. If this dictionary is given as a non-keyword argument, it should be offered as the first argument. If you are using keyword argument, please name it as `outputs_dict`. If ``self.data_info`` is ``None``, the input argument or key-word argument will be directly passed to loss function, ``r1_gradient_penalty_loss``. """ if self.interval > 1: assert self.data_info is not None # use data_info to build computational path if self.data_info is not None: # parse the args and kwargs if len(args) == 1: assert isinstance(args[0], dict), ( 'You should offer a dictionary containing network outputs ' 'for building up computational graph of this loss module.') outputs_dict = args[0] elif 'outputs_dict' in kwargs: assert len(args) == 0, ( 'If the outputs dict is given in keyworded arguments, no' ' further non-keyworded arguments should be offered.') outputs_dict = kwargs.pop('outputs_dict') else: raise NotImplementedError( 'Cannot parsing your arguments passed to this loss module.' ' Please check the usage of this module') if self.interval > 1 and outputs_dict[ 'iteration'] % self.interval != 0: return None # link the outputs with loss input args according to self.data_info loss_input_dict = { k: outputs_dict[v] for k, v in self.data_info.items() } kwargs.update(loss_input_dict) kwargs.update( dict(norm_mode=self.norm_mode, use_apex_amp=self.use_apex_amp)) return r1_gradient_penalty_loss(**kwargs) * self.loss_weight else: # if you have not define how to build computational graph, this # module will just directly return the loss as usual. return r1_gradient_penalty_loss( *args, norm_mode=self.norm_mode, **kwargs) * self.loss_weight
[docs] def loss_name(self) -> str: """Loss Name. This function must be implemented and will return the name of this loss function. This name will be used to combine different loss items by simple sum operation. In addition, if you want this loss item to be included into the backward graph, `loss_` must be the prefix of the name. Returns: str: The name of this loss item. """ return self._loss_name
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