forked from mrq/DL-Art-School
101 lines
3.8 KiB
Python
101 lines
3.8 KiB
Python
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import logging
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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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import models.networks as networks
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import models.lr_scheduler as lr_scheduler
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from .base_model import BaseModel
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logger = logging.getLogger('base')
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class FeatureModel(BaseModel):
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def __init__(self, opt):
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super(FeatureModel, self).__init__(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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train_opt = opt['train']
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self.fea_train = networks.define_F(opt, for_training=True).to(self.device)
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self.net_ref = networks.define_F(opt).to(self.device)
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self.load()
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if self.is_train:
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self.fea_train.train()
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# loss
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self.cri_fea = nn.MSELoss().to(self.device)
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# optimizers
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wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
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optim_params = []
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for k, v in self.fea_train.named_parameters(): # can optimize for a part of the model
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if v.requires_grad:
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optim_params.append(v)
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else:
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if self.rank <= 0:
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logger.warning('Params [{:s}] will not optimize.'.format(k))
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self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'],
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weight_decay=wd_G,
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betas=(train_opt['beta1_G'], train_opt['beta2_G']))
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self.optimizers.append(self.optimizer_G)
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# schedulers
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if train_opt['lr_scheme'] == 'MultiStepLR':
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for optimizer in self.optimizers:
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self.schedulers.append(
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lr_scheduler.MultiStepLR_Restart(optimizer, train_opt['gen_lr_steps'],
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restarts=train_opt['restarts'],
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weights=train_opt['restart_weights'],
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gamma=train_opt['lr_gamma'],
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clear_state=train_opt['clear_state']))
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elif train_opt['lr_scheme'] == 'CosineAnnealingLR_Restart':
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for optimizer in self.optimizers:
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self.schedulers.append(
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lr_scheduler.CosineAnnealingLR_Restart(
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optimizer, train_opt['T_period'], eta_min=train_opt['eta_min'],
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restarts=train_opt['restarts'], weights=train_opt['restart_weights']))
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else:
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raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
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self.log_dict = OrderedDict()
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def feed_data(self, data, need_GT=True):
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self.var_L = data['LQ'].to(self.device) # LQ
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if need_GT:
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self.real_H = data['GT'].to(self.device) # GT
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def optimize_parameters(self, step):
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self.optimizer_G.zero_grad()
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self.fake_H = self.fea_train(self.var_L, interpolate_factor=2)
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ref_H = self.net_ref(self.real_H)
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l_fea = self.cri_fea(self.fake_H, ref_H)
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l_fea.backward()
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self.optimizer_G.step()
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# set log
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self.log_dict['l_fea'] = l_fea.item()
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def test(self):
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pass
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def get_current_log(self, step):
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return self.log_dict
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def get_current_visuals(self, need_GT=True):
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return None
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def load(self):
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load_path_G = self.opt['path']['pretrain_model_G']
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if load_path_G is not None:
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logger.info('Loading model for F [{:s}] ...'.format(load_path_G))
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self.load_network(load_path_G, self.fea_train, self.opt['path']['strict_load'])
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def save(self, iter_label):
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self.save_network(self.fea_train, 'G', iter_label)
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