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mmedit.models.base_models.base_gan 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta
from copy import deepcopy
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn
from mmengine import Config, MessageHub
from mmengine.model import BaseModel, is_model_wrapper
from mmengine.optim import OptimWrapper, OptimWrapperDict
from torch import Tensor

from mmedit.registry import MODELS
from mmedit.structures import EditDataSample, PixelData
from mmedit.utils.typing import ForwardInputs, NoiseVar, SampleList
from ..utils import (get_valid_noise_size, get_valid_num_batches,
                     noise_sample_fn, set_requires_grad)

[文档]ModelType = Union[Dict, nn.Module]
[文档]class BaseGAN(BaseModel, metaclass=ABCMeta): """Base class for GAN models. Args: generator (ModelType): The config or model of the generator. discriminator (Optional[ModelType]): The config or model of the discriminator. Defaults to None. data_preprocessor (Optional[Union[dict, Config]]): The pre-process config or :class:`~mmedit.models.GenDataPreprocessor`. generator_steps (int): The number of times the generator is completely updated before the discriminator is updated. Defaults to 1. discriminator_steps (int): The number of times the discriminator is completely updated before the generator is updated. Defaults to 1. ema_config (Optional[Dict]): The config for generator's exponential moving average setting. Defaults to None. """ def __init__(self, generator: ModelType, discriminator: Optional[ModelType] = None, data_preprocessor: Optional[Union[dict, Config]] = None, generator_steps: int = 1, discriminator_steps: int = 1, noise_size: Optional[int] = None, ema_config: Optional[Dict] = None, loss_config: Optional[Dict] = None): super().__init__(data_preprocessor=data_preprocessor) # get valid noise_size noise_size = get_valid_noise_size(noise_size, generator) # build generator if isinstance(generator, dict): self._gen_cfg = deepcopy(generator) # build generator with default `noise_size` and `num_classes` gen_args = dict() if noise_size: gen_args['noise_size'] = noise_size if hasattr(self, 'num_classes') and self.num_classes is not None: gen_args['num_classes'] = self.num_classes generator = MODELS.build(generator, default_args=gen_args) self.generator = generator # get noise_size from generator because generator may have default # `noise_size` value self.noise_size = getattr(self.generator, 'noise_size', noise_size) # build discriminator if discriminator: if isinstance(discriminator, dict): self._disc_cfg = deepcopy(discriminator) # build discriminator with default `num_classes` disc_args = dict() if hasattr(self, 'num_classes'): disc_args['num_classes'] = self.num_classes discriminator = MODELS.build( discriminator, default_args=disc_args) self.discriminator = discriminator self._gen_steps = generator_steps self._disc_steps = discriminator_steps if ema_config is None: self._ema_config = None self._with_ema_gen = False else: self._ema_config = deepcopy(ema_config) self._init_ema_model(self._ema_config) self._with_ema_gen = True self._init_loss(loss_config) @staticmethod
[文档] def gather_log_vars(log_vars_list: List[Dict[str, Tensor]] ) -> Dict[str, Tensor]: """Gather a list of log_vars. Args: log_vars_list: List[Dict[str, Tensor]] Returns: Dict[str, Tensor] """ if len(log_vars_list) == 1: return log_vars_list[0] log_keys = log_vars_list[0].keys() log_vars = dict() for k in log_keys: assert all([k in log_vars for log_vars in log_vars_list ]), (f'\'{k}\' not in some of the \'log_vars\'.') log_vars[k] = torch.mean( torch.stack([log_vars[k] for log_vars in log_vars_list], dim=0)) return log_vars
[文档] def _init_loss(self, loss_config: Optional[Dict] = None) -> None: """Initialize customized loss modules. If loss_config is a dict, we allow kinds of value for each field. 1. `loss_config` is None: Users will implement all loss calculations in their own function. Weights for each loss terms are hard coded. 2. `loss_config` is dict of scalar or string: Users will implement all loss calculations and use passed `loss_config` to control the weight or behavior of the loss calculation. Users will unpack and use each field in this dict by themself. loss_config = dict(gp_norm_mode='HWC', gp_loss_weight=10) 3. `loss_config` is dict of dict: Each field in `loss_config` will used to build a corresponding loss module. And use loss calculation function predefined by :class:`BaseGAN` to calculate the loss. loss_config = dict() Example: loss_config = dict( # `BaseGAN` pre-defined fields gan_loss=dict(type='GANLoss', gan_type='wgan-logistic-ns'), disc_auxiliary_loss=dict( type='R1GradientPenalty', loss_weight=10. / 2., interval=2, norm_mode='HWC', data_info=dict( real_data='real_imgs', discriminator='disc')), gen_auxiliary_loss=dict( type='GeneratorPathRegularizer', loss_weight=2, pl_batch_shrink=2, interval=g_reg_interval, data_info=dict( generator='gen', num_batches='batch_size')), # user-defined field for loss weights or loss calculation my_loss_2=dict(weight=2, norm_mode='L1'), my_loss_3=2, my_loss_4_norm_type='L2') Args: loss_config (Optional[Dict], optional): Loss config used to build loss modules or define the loss weights. Defaults to None. """ if loss_config is None: self.gan_loss = None self.gen_auxiliary_losses = None self.disc_auxiliary_losses = None self.loss_config = dict() return self.loss_config = deepcopy(loss_config) # build pre-defined losses gan_loss = loss_config.get('gan_loss', None) if gan_loss is not None: self.gan_loss = MODELS.build(gan_loss) else: self.gan_loss = None disc_auxiliary_loss = loss_config.get('disc_auxiliary_loss', None) if disc_auxiliary_loss: if not isinstance(disc_auxiliary_loss, list): disc_auxiliary_loss = [disc_auxiliary_loss] self.disc_auxiliary_losses = nn.ModuleList( [MODELS.build(loss) for loss in disc_auxiliary_loss]) else: self.disc_auxiliary_losses = None gen_auxiliary_loss = loss_config.get('gen_auxiliary_loss', None) if gen_auxiliary_loss: if not isinstance(gen_auxiliary_loss, list): gen_auxiliary_loss = [gen_auxiliary_loss] self.gen_auxiliary_losses = nn.ModuleList( [MODELS.build(loss) for loss in gen_auxiliary_loss]) else: self.gen_auxiliary_losses = None
[文档] def noise_fn(self, noise: NoiseVar = None, num_batches: int = 1): """Sampling function for noise. There are three scenarios in this function: - If `noise` is a callable function, sample `num_batches` of noise with passed `noise`. - If `noise` is `None`, sample `num_batches` of noise from gaussian distribution. - If `noise` is a `torch.Tensor`, directly return `noise`. Args: noise (Union[Tensor, Callable, List[int], None]): You can directly give a batch of label through a ``torch.Tensor`` or offer a callable function to sample a batch of label data. Otherwise, the ``None`` indicates to use the default noise sampler. Defaults to `None`. num_batches (int, optional): The number of batches label want to sample. If `label` is a Tensor, this will be ignored. Defaults to 1. Returns: Tensor: Sampled noise tensor. """ return noise_sample_fn( noise=noise, num_batches=num_batches, noise_size=self.noise_size, device=self.device)
@property
[文档] def generator_steps(self) -> int: """int: The number of times the generator is completely updated before the discriminator is updated.""" return self._gen_steps
@property
[文档] def discriminator_steps(self) -> int: """int: The number of times the discriminator is completely updated before the generator is updated.""" return self._disc_steps
@property
[文档] def device(self) -> torch.device: """Get current device of the model. Returns: torch.device: The current device of the model. """ return next(self.parameters()).device
@property
[文档] def with_ema_gen(self) -> bool: """Whether the GAN adopts exponential moving average. Returns: bool: If `True`, means this GAN model is adopted to exponential moving average and vice versa. """ return self._with_ema_gen
[文档] def _init_ema_model(self, ema_config: dict): """Initialize a EMA model corresponding to the given `ema_config`. If `ema_config` is an empty dict or `None`, EMA model will not be initialized. Args: ema_config (dict): Config to initialize the EMA model. """ ema_config.setdefault('type', 'ExponentialMovingAverage') self.ema_start = ema_config.pop('start_iter', 0) src_model = self.generator.module if is_model_wrapper( self.generator) else self.generator self.generator_ema = MODELS.build( ema_config, default_args=dict(model=src_model))
[文档] def _get_valid_model(self, batch_inputs: ForwardInputs) -> str: """Try to get the valid forward model from inputs. - If forward model is defined in `batch_inputs`, it will be used as forward model. - If forward model is not defined in `batch_inputs`, 'ema' will returned if :property:`with_ema_gen` is true. Otherwise, 'orig' will be returned. Args: batch_inputs (ForwardInputs): Inputs passed to :meth:`forward`. Returns: str: Forward model to generate image. ('orig', 'ema' or 'ema/orig'). """ if isinstance(batch_inputs, dict): sample_model = batch_inputs.get('sample_model', None) else: # batch_inputs is a Tensor sample_model = None # set default value if sample_model is None: if self.with_ema_gen: sample_model = 'ema' else: sample_model = 'orig' # security checking for mode assert sample_model in [ 'ema', 'ema/orig', 'orig' ], ('Only support \'ema\', \'ema/orig\', \'orig\' ' f'in {self.__class__.__name__}\'s image sampling.') if sample_model in ['ema', 'ema/orig']: assert self.with_ema_gen, ( f'\'{self.__class__.__name__}\' do not have EMA model.') return sample_model
[文档] def forward(self, inputs: ForwardInputs, data_samples: Optional[list] = None, mode: Optional[str] = None) -> SampleList: """Sample images with the given inputs. If forward mode is 'ema' or 'orig', the image generated by corresponding generator will be returned. If forward mode is 'ema/orig', images generated by original generator and EMA generator will both be returned in a dict. Args: batch_inputs (ForwardInputs): Dict containing the necessary information (e.g. noise, num_batches, mode) to generate image. data_samples (Optional[list]): Data samples collated by :attr:`data_preprocessor`. Defaults to None. mode (Optional[str]): `mode` is not used in :class:`BaseGAN`. Defaults to None. Returns: SampleList: A list of ``EditDataSample`` contain generated results. """ if isinstance(inputs, Tensor): noise = inputs sample_kwargs = {} else: noise = inputs.get('noise', None) num_batches = get_valid_num_batches(inputs) noise = self.noise_fn(noise, num_batches=num_batches) sample_kwargs = inputs.get('sample_kwargs', dict()) num_batches = noise.shape[0] sample_model = self._get_valid_model(inputs) if sample_model in ['ema', 'ema/orig']: generator = self.generator_ema else: # sample model is 'orig' generator = self.generator num_batches = noise.shape[0] outputs = generator(noise, return_noise=False, **sample_kwargs) if sample_model == 'ema/orig': generator = self.generator outputs_orig = generator( noise, return_noise=False, **sample_kwargs) outputs = dict(ema=outputs, orig=outputs_orig) batch_sample_list = [] for idx in range(num_batches): gen_sample = EditDataSample() if data_samples: gen_sample.update(data_samples[idx]) if isinstance(inputs, dict) and 'img' in inputs: gen_sample.gt_img = PixelData(data=inputs['img'][idx]) if isinstance(outputs, dict): gen_sample.ema = EditDataSample( fake_img=PixelData(data=outputs['ema'][idx]), sample_model='ema') gen_sample.orig = EditDataSample( fake_img=PixelData(data=outputs['orig'][idx]), sample_model='orig') gen_sample.sample_model = 'ema/orig' else: gen_sample.fake_img = PixelData(data=outputs[idx]) gen_sample.sample_model = sample_model # Append input condition (noise and sample_kwargs) to # batch_sample_list gen_sample.noise = noise[idx] gen_sample.sample_kwargs = deepcopy(sample_kwargs) batch_sample_list.append(gen_sample) return batch_sample_list
[文档] def val_step(self, data: dict) -> SampleList: """Gets the generated image of given data. Calls ``self.data_preprocessor(data)`` and ``self(inputs, data_sample, mode=None)`` in order. Return the generated results which will be passed to evaluator. Args: data (dict): Data sampled from metric specific sampler. More detials in `Metrics` and `Evaluator`. Returns: SampleList: Generated image or image dict. """ data = self.data_preprocessor(data) outputs = self(**data) return outputs
[文档] def test_step(self, data: dict) -> SampleList: """Gets the generated image of given data. Same as :meth:`val_step`. Args: data (dict): Data sampled from metric specific sampler. More detials in `Metrics` and `Evaluator`. Returns: List[EditDataSample]: Generated image or image dict. """ data = self.data_preprocessor(data) outputs = self(**data) return outputs
[文档] def train_step(self, data: dict, optim_wrapper: OptimWrapperDict) -> Dict[str, Tensor]: """Train GAN model. In the training of GAN models, generator and discriminator are updated alternatively. In MMEditing's design, `self.train_step` is called with data input. Therefore we always update discriminator, whose updating is relay on real data, and then determine if the generator needs to be updated based on the current number of iterations. More details about whether to update generator can be found in :meth:`should_gen_update`. Args: data (dict): Data sampled from dataloader. optim_wrapper (OptimWrapperDict): OptimWrapperDict instance contains OptimWrapper of generator and discriminator. Returns: Dict[str, torch.Tensor]: A ``dict`` of tensor for logging. """ message_hub = MessageHub.get_current_instance() curr_iter = message_hub.get_info('iter') data = self.data_preprocessor(data, True) disc_optimizer_wrapper: OptimWrapper = optim_wrapper['discriminator'] disc_accu_iters = disc_optimizer_wrapper._accumulative_counts with disc_optimizer_wrapper.optim_context(self.discriminator): log_vars = self.train_discriminator( **data, optimizer_wrapper=disc_optimizer_wrapper) # add 1 to `curr_iter` because iter is updated in train loop. # Whether to update the generator. We update generator with # discriminator is fully updated for `self.n_discriminator_steps` # iterations. And one full updating for discriminator contains # `disc_accu_counts` times of grad accumulations. if (curr_iter + 1) % (self.discriminator_steps * disc_accu_iters) == 0: set_requires_grad(self.discriminator, False) gen_optimizer_wrapper = optim_wrapper['generator'] gen_accu_iters = gen_optimizer_wrapper._accumulative_counts log_vars_gen_list = [] # init optimizer wrapper status for generator manually gen_optimizer_wrapper.initialize_count_status( self.generator, 0, self.generator_steps * gen_accu_iters) for _ in range(self.generator_steps * gen_accu_iters): with gen_optimizer_wrapper.optim_context(self.generator): log_vars_gen = self.train_generator( **data, optimizer_wrapper=gen_optimizer_wrapper) log_vars_gen_list.append(log_vars_gen) log_vars_gen = self.gather_log_vars(log_vars_gen_list) log_vars_gen.pop('loss', None) # remove 'loss' from gen logs set_requires_grad(self.discriminator, True) # only do ema after generator update if self.with_ema_gen and (curr_iter + 1) >= ( self.ema_start * self.discriminator_steps * disc_accu_iters): self.generator_ema.update_parameters( self.generator.module if is_model_wrapper(self.generator) else self.generator) # if not update buffer, copy buffer from orig model if not self.generator_ema.update_buffers: self.generator_ema.sync_buffers( self.generator.module if is_model_wrapper( self.generator) else self.generator) elif self.with_ema_gen: # before ema, copy weights from orig self.generator_ema.sync_parameters( self.generator.module if is_model_wrapper(self.generator) else self.generator) log_vars.update(log_vars_gen) return log_vars
[文档] def _get_gen_loss(self, out_dict): losses_dict = {} # gan loss losses_dict['loss_disc_fake_g'] = self.gan_loss( out_dict['disc_pred_fake_g'], target_is_real=True, is_disc=False) # gen auxiliary loss if self.gen_auxiliary_losses is not None: for loss_module in self.gen_auxiliary_losses: loss_ = loss_module(out_dict) if loss_ is None: continue # the `loss_name()` function return name as 'loss_xxx' if loss_module.loss_name() in losses_dict: losses_dict[loss_module.loss_name( )] = losses_dict[loss_module.loss_name()] + loss_ else: losses_dict[loss_module.loss_name()] = loss_ loss, log_var = self.parse_losses(losses_dict) return loss, log_var
[文档] def _get_disc_loss(self, out_dict): # Construct losses dict. If you hope some items to be included in the # computational graph, you have to add 'loss' in its name. Otherwise, # items without 'loss' in their name will just be used to print # information. losses_dict = {} # gan loss losses_dict['loss_disc_fake'] = self.gan_loss( out_dict['disc_pred_fake'], target_is_real=False, is_disc=True) losses_dict['loss_disc_real'] = self.gan_loss( out_dict['disc_pred_real'], target_is_real=True, is_disc=True) # disc auxiliary loss if self.disc_auxiliary_losses is not None: for loss_module in self.disc_auxiliary_losses: loss_ = loss_module(out_dict) if loss_ is None: continue # the `loss_name()` function return name as 'loss_xxx' if loss_module.loss_name() in losses_dict: losses_dict[loss_module.loss_name( )] = losses_dict[loss_module.loss_name()] + loss_ else: losses_dict[loss_module.loss_name()] = loss_ loss, log_var = self.parse_losses(losses_dict) return loss, log_var
[文档] def train_generator(self, inputs: dict, data_samples: List[EditDataSample], optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]: """Training function for discriminator. All GANs should implement this function by themselves. Args: inputs (dict): Inputs from dataloader. data_samples (List[EditDataSample]): Data samples from dataloader. optim_wrapper (OptimWrapper): OptimWrapper instance used to update model parameters. Returns: Dict[str, Tensor]: A ``dict`` of tensor for logging. """ num_batches = inputs['img'].shape[0] noise = self.noise_fn(num_batches=num_batches) fake_imgs = self.generator(noise=noise) disc_pred_fake_g = self.discriminator(fake_imgs) data_dict_ = dict( gen=self.generator, disc=self.discriminator, fake_imgs=fake_imgs, disc_pred_fake_g=disc_pred_fake_g, # iteration=curr_iter, batch_size=num_batches, loss_scaler=getattr(optimizer_wrapper, 'loss_scaler', None)) loss, log_vars = self._get_gen_loss(data_dict_) optimizer_wrapper.update_params(loss) return log_vars
[文档] def train_discriminator(self, inputs: dict, data_samples: List[EditDataSample], optimizer_wrapper: OptimWrapper ) -> Dict[str, Tensor]: """Training function for discriminator. All GANs should implement this function by themselves. Args: inputs (dict): Inputs from dataloader. data_samples (List[EditDataSample]): Data samples from dataloader. optim_wrapper (OptimWrapper): OptimWrapper instance used to update model parameters. Returns: Dict[str, Tensor]: A ``dict`` of tensor for logging. """ real_imgs, num_batches = inputs['img'], inputs['img'].shape[0] noise = self.noise_fn(num_batches=num_batches) fake_imgs = self.generator(noise=noise) # disc pred for fake imgs and real_imgs disc_pred_fake = self.discriminator(fake_imgs) disc_pred_real = self.discriminator(real_imgs) # get data dict to compute losses for disc 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=num_batches, loss_scaler=getattr(optimizer_wrapper, 'loss_scaler', None)) loss, log_vars = self._get_disc_loss(data_dict_) optimizer_wrapper.update_params(loss) return log_vars
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