Wav2Lip-HD / basicsr /models /base_model.py
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import logging
import os
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.dist_util import master_only
logger = logging.getLogger('basicsr')
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 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)
else:
raise NotImplementedError(f'optimizer {optim_type} is not supperted 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__} - ' f'{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.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 warmup.
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): Warmup iter numbers. -1 for no warmup.
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
torch.save(save_dict, save_path)
def _print_different_keys_loading(self, crt_net, load_net, strict=True):
"""Print keys with differnet name or different size when loading models.
1. Print keys with differnet 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())
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'.
"""
net = self.get_bare_model(net)
logger.info(f'Loading {net.__class__.__name__} model from {load_path}.')
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]
# 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)
torch.save(state, 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