DL-Art-School/codes/models/SR_model.py

172 lines
6.7 KiB
Python

import logging
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn.parallel import DataParallel, DistributedDataParallel
import models.networks as networks
import models.lr_scheduler as lr_scheduler
from .base_model import BaseModel
from models.loss import CharbonnierLoss
from apex import amp
logger = logging.getLogger('base')
class SRModel(BaseModel):
def __init__(self, opt):
super(SRModel, self).__init__(opt)
if opt['dist']:
self.rank = torch.distributed.get_rank()
else:
self.rank = -1 # non dist training
train_opt = opt['train']
# define network and load pretrained models
self.netG = amp.initialize(networks.define_G(opt).to(self.device), opt_level=self.amp_level)
if opt['dist']:
self.netG = DistributedDataParallel(self.netG, device_ids=[torch.cuda.current_device()])
elif opt['gpu_ids'] is not None:
self.netG = DataParallel(self.netG)
# print network
self.print_network()
self.load()
if self.is_train:
self.netG.train()
# loss
loss_type = train_opt['pixel_criterion']
if loss_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif loss_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
elif loss_type == 'cb':
self.cri_pix = CharbonnierLoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] is not recognized.'.format(loss_type))
self.l_pix_w = train_opt['pixel_weight']
# optimizers
wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.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'], train_opt['beta2']))
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['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()
self.fake_H = self.netG(self.var_L)
l_pix = self.l_pix_w * self.cri_pix(self.fake_H, self.real_H)
l_pix.backward()
self.optimizer_G.step()
# set log
self.log_dict['l_pix'] = l_pix.item()
def test(self):
self.netG.eval()
with torch.no_grad():
self.fake_H = self.netG(self.var_L)
self.netG.train()
def test_x8(self):
# from https://github.com/thstkdgus35/EDSR-PyTorch
self.netG.eval()
def _transform(v, op):
# if self.precision != 'single': v = v.float()
v2np = v.data.cpu().numpy()
if op == 'v':
tfnp = v2np[:, :, :, ::-1].copy()
elif op == 'h':
tfnp = v2np[:, :, ::-1, :].copy()
elif op == 't':
tfnp = v2np.transpose((0, 1, 3, 2)).copy()
ret = torch.Tensor(tfnp).to(self.device)
# if self.precision == 'half': ret = ret.half()
return ret
lr_list = [self.var_L]
for tf in 'v', 'h', 't':
lr_list.extend([_transform(t, tf) for t in lr_list])
with torch.no_grad():
sr_list = [self.netG(aug) for aug in lr_list]
for i in range(len(sr_list)):
if i > 3:
sr_list[i] = _transform(sr_list[i], 't')
if i % 4 > 1:
sr_list[i] = _transform(sr_list[i], 'h')
if (i % 4) % 2 == 1:
sr_list[i] = _transform(sr_list[i], 'v')
output_cat = torch.cat(sr_list, dim=0)
self.fake_H = output_cat.mean(dim=0, keepdim=True)
self.netG.train()
def get_current_log(self):
return self.log_dict
def get_current_visuals(self, need_GT=True):
out_dict = OrderedDict()
out_dict['LQ'] = self.var_L.detach().float().cpu()
out_dict['rlt'] = self.fake_H.detach().float().cpu()
if need_GT:
out_dict['GT'] = self.real_H.detach().float().cpu()
return out_dict
def print_network(self):
s, n = self.get_network_description(self.netG)
if isinstance(self.netG, nn.DataParallel) or isinstance(self.netG, DistributedDataParallel):
net_struc_str = '{} - {}'.format(self.netG.__class__.__name__,
self.netG.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netG.__class__.__name__)
if self.rank <= 0:
logger.info('Network G structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
logger.info(s)
def load(self):
load_path_G = self.opt['path']['pretrain_model_G']
if load_path_G is not None:
logger.info('Loading model for G [{:s}] ...'.format(load_path_G))
self.load_network(load_path_G, self.netG, self.opt['path']['strict_load'])
def save(self, iter_label):
self.save_network(self.netG, 'G', iter_label)