forked from mrq/DL-Art-School
148 lines
6.2 KiB
Python
148 lines
6.2 KiB
Python
import os
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from collections import OrderedDict
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import torch
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import torch.nn as nn
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from torch.nn.parallel.distributed import DistributedDataParallel
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import utils.util
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from apex import amp
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class BaseModel():
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def __init__(self, opt):
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self.opt = opt
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if opt['dist']:
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self.rank = torch.distributed.get_rank()
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else:
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self.rank = -1 # non dist training
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self.device = torch.device('cuda' if opt['gpu_ids'] is not None else 'cpu')
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self.amp_level = 'O0' if opt['amp_opt_level'] is None else opt['amp_opt_level']
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self.is_train = opt['is_train']
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self.schedulers = []
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self.optimizers = []
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self.disc_optimizers = []
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def feed_data(self, data):
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pass
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def optimize_parameters(self):
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pass
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def get_current_visuals(self):
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pass
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def get_current_losses(self):
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pass
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def print_network(self):
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pass
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def save(self, label):
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pass
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def load(self):
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pass
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def _set_lr(self, lr_groups_l):
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"""Set learning rate for warmup
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lr_groups_l: list for lr_groups. each for a optimizer"""
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for optimizer, lr_groups in zip(self.optimizers, lr_groups_l):
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for param_group, lr in zip(optimizer.param_groups, lr_groups):
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param_group['lr'] = lr
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def _get_init_lr(self):
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"""Get the initial lr, which is set by the scheduler"""
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init_lr_groups_l = []
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for optimizer in self.optimizers:
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init_lr_groups_l.append([v['initial_lr'] for v in optimizer.param_groups])
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return init_lr_groups_l
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def update_learning_rate(self, cur_iter, warmup_iter=-1):
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for scheduler in self.schedulers:
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scheduler.step()
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# set up warm-up learning rate
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if cur_iter < warmup_iter:
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# get initial lr for each group
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init_lr_g_l = self._get_init_lr()
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# modify warming-up learning rates
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warm_up_lr_l = []
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for init_lr_g in init_lr_g_l:
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warm_up_lr_l.append([v / warmup_iter * cur_iter for v in init_lr_g])
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# set learning rate
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self._set_lr(warm_up_lr_l)
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def get_current_learning_rate(self):
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return [param_group['lr'] for param_group in self.optimizers[0].param_groups]
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def get_network_description(self, network):
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"""Get the string and total parameters of the network"""
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if isinstance(network, nn.DataParallel) or isinstance(network, DistributedDataParallel):
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network = network.module
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return str(network), sum(map(lambda x: x.numel(), network.parameters()))
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def save_network(self, network, network_label, iter_label):
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save_filename = '{}_{}.pth'.format(iter_label, network_label)
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save_path = os.path.join(self.opt['path']['models'], save_filename)
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if isinstance(network, nn.DataParallel) or isinstance(network, DistributedDataParallel):
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network = network.module
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state_dict = network.state_dict()
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for key, param in state_dict.items():
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state_dict[key] = param.cpu()
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torch.save(state_dict, save_path)
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# Also save to the 'alt_path' which is useful for caching to Google Drive in colab, for example.
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if 'alt_path' in self.opt['path'].keys():
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torch.save(state_dict, os.path.join(self.opt['path']['alt_path'], save_filename))
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if self.opt['colab_mode']:
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utils.util.copy_files_to_server(self.opt['ssh_server'], self.opt['ssh_username'], self.opt['ssh_password'],
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save_path, os.path.join(self.opt['remote_path'], 'models', save_filename))
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return save_path
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def load_network(self, load_path, network, strict=True):
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if isinstance(network, nn.DataParallel) or isinstance(network, DistributedDataParallel):
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network = network.module
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load_net = torch.load(load_path)
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# Support loading torch.save()s for whole models as well as just state_dicts.
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if 'state_dict' in load_net:
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load_net = load_net['state_dict']
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load_net_clean = OrderedDict() # remove unnecessary 'module.'
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for k, v in load_net.items():
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if k.startswith('module.'):
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load_net_clean[k[7:]] = v
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else:
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load_net_clean[k] = v
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network.load_state_dict(load_net_clean, strict=strict)
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def save_training_state(self, epoch, iter_step):
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"""Save training state during training, which will be used for resuming"""
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state = {'epoch': epoch, 'iter': iter_step, 'schedulers': [], 'optimizers': []}
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for s in self.schedulers:
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state['schedulers'].append(s.state_dict())
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for o in self.optimizers:
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state['optimizers'].append(o.state_dict())
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if 'amp_opt_level' in self.opt.keys():
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state['amp'] = amp.state_dict()
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save_filename = '{}.state'.format(iter_step)
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save_path = os.path.join(self.opt['path']['training_state'], save_filename)
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torch.save(state, save_path)
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# Also save to the 'alt_path' which is useful for caching to Google Drive in colab, for example.
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if 'alt_path' in self.opt['path'].keys():
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torch.save(state, os.path.join(self.opt['path']['alt_path'], 'latest.state'))
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if self.opt['colab_mode']:
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utils.util.copy_files_to_server(self.opt['ssh_server'], self.opt['ssh_username'], self.opt['ssh_password'],
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save_path, os.path.join(self.opt['remote_path'], 'training_state', save_filename))
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def resume_training(self, resume_state, load_amp=True):
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"""Resume the optimizers and schedulers for training"""
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resume_optimizers = resume_state['optimizers']
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resume_schedulers = resume_state['schedulers']
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assert len(resume_optimizers) == len(self.optimizers), 'Wrong lengths of optimizers'
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assert len(resume_schedulers) == len(self.schedulers), 'Wrong lengths of schedulers'
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for i, o in enumerate(resume_optimizers):
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self.optimizers[i].load_state_dict(o)
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for i, s in enumerate(resume_schedulers):
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self.schedulers[i].load_state_dict(s)
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if load_amp and 'amp' in resume_state.keys():
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amp.load_state_dict(resume_state['amp'])
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