import os import time import torch from collections import OrderedDict from copy import deepcopy from torch.nn.parallel import DataParallel, DistributedDataParallel from basicsr.models import lr_scheduler as lr_scheduler from basicsr.utils import get_root_logger from basicsr.utils.dist_util import master_only class BaseModel(): """Base model.""" def __init__(self, opt): self.opt = opt self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu') self.is_train = opt['is_train'] self.schedulers = [] self.optimizers = [] def feed_data(self, data): pass def optimize_parameters(self): pass def get_current_visuals(self): pass def save(self, epoch, current_iter): """Save networks and training state.""" pass def validation(self, dataloader, current_iter, tb_logger, save_img=False): """Validation function. Args: dataloader (torch.utils.data.DataLoader): Validation dataloader. current_iter (int): Current iteration. tb_logger (tensorboard logger): Tensorboard logger. save_img (bool): Whether to save images. Default: False. """ if self.opt['dist']: self.dist_validation(dataloader, current_iter, tb_logger, save_img) else: self.nondist_validation(dataloader, current_iter, tb_logger, save_img) def _initialize_best_metric_results(self, dataset_name): """Initialize the best metric results dict for recording the best metric value and iteration.""" if hasattr(self, 'best_metric_results') and dataset_name in self.best_metric_results: return elif not hasattr(self, 'best_metric_results'): self.best_metric_results = dict() # add a dataset record record = dict() for metric, content in self.opt['val']['metrics'].items(): better = content.get('better', 'higher') init_val = float('-inf') if better == 'higher' else float('inf') record[metric] = dict(better=better, val=init_val, iter=-1) self.best_metric_results[dataset_name] = record def _update_best_metric_result(self, dataset_name, metric, val, current_iter): if self.best_metric_results[dataset_name][metric]['better'] == 'higher': if val >= self.best_metric_results[dataset_name][metric]['val']: self.best_metric_results[dataset_name][metric]['val'] = val self.best_metric_results[dataset_name][metric]['iter'] = current_iter else: if val <= self.best_metric_results[dataset_name][metric]['val']: self.best_metric_results[dataset_name][metric]['val'] = val self.best_metric_results[dataset_name][metric]['iter'] = current_iter def model_ema(self, decay=0.999): net_g = self.get_bare_model(self.net_g) net_g_params = dict(net_g.named_parameters()) net_g_ema_params = dict(self.net_g_ema.named_parameters()) for k in net_g_ema_params.keys(): net_g_ema_params[k].data.mul_(decay).add_(net_g_params[k].data, alpha=1 - decay) def get_current_log(self): return self.log_dict def model_to_device(self, net): """Model to device. It also warps models with DistributedDataParallel or DataParallel. Args: net (nn.Module) """ net = net.to(self.device) if self.opt['dist']: find_unused_parameters = self.opt.get('find_unused_parameters', False) net = DistributedDataParallel( net, device_ids=[torch.cuda.current_device()], find_unused_parameters=find_unused_parameters) elif self.opt['num_gpu'] > 1: net = DataParallel(net) return net def get_optimizer(self, optim_type, params, lr, **kwargs): if optim_type == 'Adam': optimizer = torch.optim.Adam(params, lr, **kwargs) elif optim_type == 'AdamW': optimizer = torch.optim.AdamW(params, lr, **kwargs) elif optim_type == 'Adamax': optimizer = torch.optim.Adamax(params, lr, **kwargs) elif optim_type == 'SGD': optimizer = torch.optim.SGD(params, lr, **kwargs) elif optim_type == 'ASGD': optimizer = torch.optim.ASGD(params, lr, **kwargs) elif optim_type == 'RMSprop': optimizer = torch.optim.RMSprop(params, lr, **kwargs) elif optim_type == 'Rprop': optimizer = torch.optim.Rprop(params, lr, **kwargs) else: raise NotImplementedError(f'optimizer {optim_type} is not supported yet.') return optimizer def setup_schedulers(self): """Set up schedulers.""" train_opt = self.opt['train'] scheduler_type = train_opt['scheduler'].pop('type') if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']: for optimizer in self.optimizers: self.schedulers.append(lr_scheduler.MultiStepRestartLR(optimizer, **train_opt['scheduler'])) elif scheduler_type == 'CosineAnnealingRestartLR': for optimizer in self.optimizers: self.schedulers.append(lr_scheduler.CosineAnnealingRestartLR(optimizer, **train_opt['scheduler'])) else: raise NotImplementedError(f'Scheduler {scheduler_type} is not implemented yet.') def get_bare_model(self, net): """Get bare model, especially under wrapping with DistributedDataParallel or DataParallel. """ if isinstance(net, (DataParallel, DistributedDataParallel)): net = net.module return net @master_only def print_network(self, net): """Print the str and parameter number of a network. Args: net (nn.Module) """ if isinstance(net, (DataParallel, DistributedDataParallel)): net_cls_str = f'{net.__class__.__name__} - {net.module.__class__.__name__}' else: net_cls_str = f'{net.__class__.__name__}' net = self.get_bare_model(net) net_str = str(net) net_params = sum(map(lambda x: x.numel(), net.parameters())) logger = get_root_logger() logger.info(f'Network: {net_cls_str}, with parameters: {net_params:,d}') logger.info(net_str) def _set_lr(self, lr_groups_l): """Set learning rate for warm-up. Args: lr_groups_l (list): List for lr_groups, each for an optimizer. """ for optimizer, lr_groups in zip(self.optimizers, lr_groups_l): for param_group, lr in zip(optimizer.param_groups, lr_groups): param_group['lr'] = lr def _get_init_lr(self): """Get the initial lr, which is set by the scheduler. """ init_lr_groups_l = [] for optimizer in self.optimizers: init_lr_groups_l.append([v['initial_lr'] for v in optimizer.param_groups]) return init_lr_groups_l def update_learning_rate(self, current_iter, warmup_iter=-1): """Update learning rate. Args: current_iter (int): Current iteration. warmup_iter (int)： Warm-up iter numbers. -1 for no warm-up. Default： -1. """ if current_iter > 1: for scheduler in self.schedulers: scheduler.step() # set up warm-up learning rate if current_iter < warmup_iter: # get initial lr for each group init_lr_g_l = self._get_init_lr() # modify warming-up learning rates # currently only support linearly warm up warm_up_lr_l = [] for init_lr_g in init_lr_g_l: warm_up_lr_l.append([v / warmup_iter * current_iter for v in init_lr_g]) # set learning rate self._set_lr(warm_up_lr_l) def get_current_learning_rate(self): return [param_group['lr'] for param_group in self.optimizers[0].param_groups] @master_only def save_network(self, net, net_label, current_iter, param_key='params'): """Save networks. Args: net (nn.Module | list[nn.Module]): Network(s) to be saved. net_label (str): Network label. current_iter (int): Current iter number. param_key (str | list[str]): The parameter key(s) to save network. Default: 'params'. """ if current_iter == -1: current_iter = 'latest' save_filename = f'{net_label}_{current_iter}.pth' save_path = os.path.join(self.opt['path']['models'], save_filename) net = net if isinstance(net, list) else [net] param_key = param_key if isinstance(param_key, list) else [param_key] assert len(net) == len(param_key), 'The lengths of net and param_key should be the same.' save_dict = {} for net_, param_key_ in zip(net, param_key): net_ = self.get_bare_model(net_) state_dict = net_.state_dict() for key, param in state_dict.items(): if key.startswith('module.'): # remove unnecessary 'module.' key = key[7:] state_dict[key] = param.cpu() save_dict[param_key_] = state_dict # avoid occasional writing errors retry = 3 while retry > 0: try: torch.save(save_dict, save_path) except Exception as e: logger = get_root_logger() logger.warning(f'Save model error: {e}, remaining retry times: {retry - 1}') time.sleep(1) else: break finally: retry -= 1 if retry == 0: logger.warning(f'Still cannot save {save_path}. Just ignore it.') # raise IOError(f'Cannot save {save_path}.') def _print_different_keys_loading(self, crt_net, load_net, strict=True): """Print keys with different name or different size when loading models. 1. Print keys with different names. 2. If strict=False, print the same key but with different tensor size. It also ignore these keys with different sizes (not load). Args: crt_net (torch model): Current network. load_net (dict): Loaded network. strict (bool): Whether strictly loaded. Default: True. """ crt_net = self.get_bare_model(crt_net) crt_net = crt_net.state_dict() crt_net_keys = set(crt_net.keys()) load_net_keys = set(load_net.keys()) logger = get_root_logger() if crt_net_keys != load_net_keys: logger.warning('Current net - loaded net:') for v in sorted(list(crt_net_keys - load_net_keys)): logger.warning(f' {v}') logger.warning('Loaded net - current net:') for v in sorted(list(load_net_keys - crt_net_keys)): logger.warning(f' {v}') # check the size for the same keys if not strict: common_keys = crt_net_keys & load_net_keys for k in common_keys: if crt_net[k].size() != load_net[k].size(): logger.warning(f'Size different, ignore [{k}]: crt_net: ' f'{crt_net[k].shape}; load_net: {load_net[k].shape}') load_net[k + '.ignore'] = load_net.pop(k) def load_network(self, net, load_path, strict=True, param_key='params'): """Load network. Args: load_path (str): The path of networks to be loaded. net (nn.Module): Network. strict (bool): Whether strictly loaded. param_key (str): The parameter key of loaded network. If set to None, use the root 'path'. Default: 'params'. """ logger = get_root_logger() net = self.get_bare_model(net) load_net = torch.load(load_path, map_location=lambda storage, loc: storage) if param_key is not None: if param_key not in load_net and 'params' in load_net: param_key = 'params' logger.info('Loading: params_ema does not exist, use params.') load_net = load_net[param_key] logger.info(f'Loading {net.__class__.__name__} model from {load_path}, with param key: [{param_key}].') # remove unnecessary 'module.' for k, v in deepcopy(load_net).items(): if k.startswith('module.'): load_net[k[7:]] = v load_net.pop(k) self._print_different_keys_loading(net, load_net, strict) net.load_state_dict(load_net, strict=strict) @master_only def save_training_state(self, epoch, current_iter): """Save training states during training, which will be used for resuming. Args: epoch (int): Current epoch. current_iter (int): Current iteration. """ if current_iter != -1: state = {'epoch': epoch, 'iter': current_iter, 'optimizers': [], 'schedulers': []} for o in self.optimizers: state['optimizers'].append(o.state_dict()) for s in self.schedulers: state['schedulers'].append(s.state_dict()) save_filename = f'{current_iter}.state' save_path = os.path.join(self.opt['path']['training_states'], save_filename) # avoid occasional writing errors retry = 3 while retry > 0: try: torch.save(state, save_path) except Exception as e: logger = get_root_logger() logger.warning(f'Save training state error: {e}, remaining retry times: {retry - 1}') time.sleep(1) else: break finally: retry -= 1 if retry == 0: logger.warning(f'Still cannot save {save_path}. Just ignore it.') # raise IOError(f'Cannot save {save_path}.') def resume_training(self, resume_state): """Reload the optimizers and schedulers for resumed training. Args: resume_state (dict): Resume state. """ resume_optimizers = resume_state['optimizers'] resume_schedulers = resume_state['schedulers'] assert len(resume_optimizers) == len(self.optimizers), 'Wrong lengths of optimizers' assert len(resume_schedulers) == len(self.schedulers), 'Wrong lengths of schedulers' for i, o in enumerate(resume_optimizers): self.optimizers[i].load_state_dict(o) for i, s in enumerate(resume_schedulers): self.schedulers[i].load_state_dict(s) def reduce_loss_dict(self, loss_dict): """reduce loss dict. In distributed training, it averages the losses among different GPUs . Args: loss_dict (OrderedDict): Loss dict. """ with torch.no_grad(): if self.opt['dist']: keys = [] losses = [] for name, value in loss_dict.items(): keys.append(name) losses.append(value) losses = torch.stack(losses, 0) torch.distributed.reduce(losses, dst=0) if self.opt['rank'] == 0: losses /= self.opt['world_size'] loss_dict = {key: loss for key, loss in zip(keys, losses)} log_dict = OrderedDict() for name, value in loss_dict.items(): log_dict[name] = value.mean().item() return log_dict