DL-Art-School/codes/models/feature_model.py
2020-08-30 08:08:49 -06:00

106 lines
3.9 KiB
Python

import logging
from collections import OrderedDict
import torch
import torch.nn as nn
import models.networks as networks
import models.lr_scheduler as lr_scheduler
from .base_model import BaseModel
logger = logging.getLogger('base')
class FeatureModel(BaseModel):
def __init__(self, opt):
super(FeatureModel, self).__init__(opt)
if opt['dist']:
self.rank = torch.distributed.get_rank()
else:
self.rank = -1 # non dist training
train_opt = opt['train']
self.fea_train = networks.define_F(for_training=True).to(self.device)
self.net_ref = networks.define_F().to(self.device)
self.load()
if self.is_train:
self.fea_train.train()
# loss
self.cri_fea = nn.MSELoss().to(self.device)
# optimizers
wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
optim_params = []
for k, v in self.fea_train.named_parameters(): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
if self.rank <= 0:
logger.warning('Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'],
weight_decay=wd_G,
betas=(train_opt['beta1_G'], train_opt['beta2_G']))
self.optimizers.append(self.optimizer_G)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepLR_Restart(optimizer, train_opt['gen_lr_steps'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights'],
gamma=train_opt['lr_gamma'],
clear_state=train_opt['clear_state']))
elif train_opt['lr_scheme'] == 'CosineAnnealingLR_Restart':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.CosineAnnealingLR_Restart(
optimizer, train_opt['T_period'], eta_min=train_opt['eta_min'],
restarts=train_opt['restarts'], weights=train_opt['restart_weights']))
else:
raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
def feed_data(self, data, need_GT=True):
self.var_L = data['LQ'].to(self.device) # LQ
if need_GT:
self.real_H = data['GT'].to(self.device) # GT
def optimize_parameters(self, step):
self.optimizer_G.zero_grad()
# grey out the LR image but keep 3 channels.
lr = torch.mean(self.real_H, dim=1, keepdim=True)
lr = lr.repeat(1, 3, 1, 1)
self.fake_H = self.fea_train(lr, interpolate_factor=1)
ref_H = self.net_ref(self.real_H)
l_fea = self.cri_fea(self.fake_H, ref_H)
l_fea.backward()
self.optimizer_G.step()
# set log
self.log_dict['l_fea'] = l_fea.item()
def test(self):
pass
def get_current_log(self, step):
return self.log_dict
def get_current_visuals(self, need_GT=True):
return None
def load(self):
load_path_G = self.opt['path']['pretrain_model_G']
if load_path_G is not None:
logger.info('Loading model for F [{:s}] ...'.format(load_path_G))
self.load_network(load_path_G, self.fea_train, self.opt['path']['strict_load'])
def save(self, iter_label):
self.save_network(self.fea_train, 'G', iter_label)