import os from collections import OrderedDict import torch import torch.nn as nn from torch.nn.parallel import DistributedDataParallel class BaseModel(): def __init__(self, opt): self.opt = opt self.device = torch.device('cuda' if opt['gpu_ids'] is not None else 'cpu') self.amp_level = 'O0' if opt['amp_opt_level'] is None else opt['amp_opt_level'] 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 get_current_losses(self): pass def print_network(self): pass def save(self, label): pass def load(self): pass def _set_lr(self, lr_groups_l): """Set learning rate for warmup lr_groups_l: list for lr_groups. each for a 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, cur_iter, warmup_iter=-1): for scheduler in self.schedulers: scheduler.step() # set up warm-up learning rate if cur_iter < warmup_iter: # get initial lr for each group init_lr_g_l = self._get_init_lr() # modify warming-up learning rates warm_up_lr_l = [] for init_lr_g in init_lr_g_l: warm_up_lr_l.append([v / warmup_iter * cur_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] def get_network_description(self, network): """Get the string and total parameters of the network""" if isinstance(network, nn.DataParallel) or isinstance(network, DistributedDataParallel): network = network.module return str(network), sum(map(lambda x: x.numel(), network.parameters())) def save_network(self, network, network_label, iter_label): save_filename = '{}_{}.pth'.format(iter_label, network_label) save_path = os.path.join(self.opt['path']['models'], save_filename) if isinstance(network, nn.DataParallel) or isinstance(network, DistributedDataParallel): network = network.module state_dict = network.state_dict() for key, param in state_dict.items(): state_dict[key] = param.cpu() torch.save(state_dict, save_path) def load_network(self, load_path, network, strict=True): if isinstance(network, nn.DataParallel) or isinstance(network, DistributedDataParallel): network = network.module load_net = torch.load(load_path) load_net_clean = OrderedDict() # remove unnecessary 'module.' for k, v in load_net.items(): if k.startswith('module.'): load_net_clean[k[7:]] = v else: load_net_clean[k] = v network.load_state_dict(load_net_clean, strict=strict) def save_training_state(self, epoch, iter_step): """Save training state during training, which will be used for resuming""" state = {'epoch': epoch, 'iter': iter_step, 'schedulers': [], 'optimizers': []} for s in self.schedulers: state['schedulers'].append(s.state_dict()) for o in self.optimizers: state['optimizers'].append(o.state_dict()) save_filename = '{}.state'.format(iter_step) save_path = os.path.join(self.opt['path']['training_state'], save_filename) torch.save(state, save_path) def resume_training(self, resume_state): """Resume the optimizers and schedulers for training""" 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)