File size: 8,264 Bytes
6c60ccc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
import torch
from collections import OrderedDict
from os import path as osp
from tqdm import tqdm
from basicsr.archs import build_network
from basicsr.losses import build_loss
from basicsr.metrics import calculate_metric
from basicsr.utils import get_root_logger, imwrite, tensor2img
from basicsr.utils.registry import MODEL_REGISTRY
from .base_model import BaseModel
@MODEL_REGISTRY.register()
class SRModel(BaseModel):
"""Base SR model for single image super-resolution."""
def __init__(self, opt):
super(SRModel, self).__init__(opt)
# define network
self.net_g = build_network(opt['network_g'])
self.net_g = self.model_to_device(self.net_g)
self.print_network(self.net_g)
# load pretrained models
load_path = self.opt['path'].get('pretrain_network_g', None)
if load_path is not None:
param_key = self.opt['path'].get('param_key_g', 'params')
self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key)
if self.is_train:
self.init_training_settings()
def init_training_settings(self):
self.net_g.train()
train_opt = self.opt['train']
self.ema_decay = train_opt.get('ema_decay', 0)
if self.ema_decay > 0:
logger = get_root_logger()
logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}')
# define network net_g with Exponential Moving Average (EMA)
# net_g_ema is used only for testing on one GPU and saving
# There is no need to wrap with DistributedDataParallel
self.net_g_ema = build_network(self.opt['network_g']).to(self.device)
# load pretrained model
load_path = self.opt['path'].get('pretrain_network_g', None)
if load_path is not None:
self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema')
else:
self.model_ema(0) # copy net_g weight
self.net_g_ema.eval()
# define losses
if train_opt.get('pixel_opt'):
self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device)
else:
self.cri_pix = None
if train_opt.get('perceptual_opt'):
self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device)
else:
self.cri_perceptual = None
if self.cri_pix is None and self.cri_perceptual is None:
raise ValueError('Both pixel and perceptual losses are None.')
# set up optimizers and schedulers
self.setup_optimizers()
self.setup_schedulers()
def setup_optimizers(self):
train_opt = self.opt['train']
optim_params = []
for k, v in self.net_g.named_parameters():
if v.requires_grad:
optim_params.append(v)
else:
logger = get_root_logger()
logger.warning(f'Params {k} will not be optimized.')
optim_type = train_opt['optim_g'].pop('type')
self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g'])
self.optimizers.append(self.optimizer_g)
def feed_data(self, data):
self.lq = data['lq'].to(self.device)
if 'gt' in data:
self.gt = data['gt'].to(self.device)
def optimize_parameters(self, current_iter):
self.optimizer_g.zero_grad()
self.output = self.net_g(self.lq)
l_total = 0
loss_dict = OrderedDict()
# pixel loss
if self.cri_pix:
l_pix = self.cri_pix(self.output, self.gt)
l_total += l_pix
loss_dict['l_pix'] = l_pix
# perceptual loss
if self.cri_perceptual:
l_percep, l_style = self.cri_perceptual(self.output, self.gt)
if l_percep is not None:
l_total += l_percep
loss_dict['l_percep'] = l_percep
if l_style is not None:
l_total += l_style
loss_dict['l_style'] = l_style
l_total.backward()
self.optimizer_g.step()
self.log_dict = self.reduce_loss_dict(loss_dict)
if self.ema_decay > 0:
self.model_ema(decay=self.ema_decay)
def test(self):
if hasattr(self, 'ema_decay'):
self.net_g_ema.eval()
with torch.no_grad():
self.output = self.net_g_ema(self.lq)
else:
self.net_g.eval()
with torch.no_grad():
self.output = self.net_g(self.lq)
self.net_g.train()
def dist_validation(self, dataloader, current_iter, tb_logger, save_img):
if self.opt['rank'] == 0:
self.nondist_validation(dataloader, current_iter, tb_logger, save_img)
def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
dataset_name = dataloader.dataset.opt['name']
with_metrics = self.opt['val'].get('metrics') is not None
if with_metrics:
self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()}
pbar = tqdm(total=len(dataloader), unit='image')
for idx, val_data in enumerate(dataloader):
img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0]
self.feed_data(val_data)
self.test()
visuals = self.get_current_visuals()
sr_img = tensor2img([visuals['result']])
if 'gt' in visuals:
gt_img = tensor2img([visuals['gt']])
del self.gt
# tentative for out of GPU memory
del self.lq
del self.output
torch.cuda.empty_cache()
if save_img:
if self.opt['is_train']:
save_img_path = osp.join(self.opt['path']['visualization'], img_name,
f'{img_name}_{current_iter}.png')
else:
if self.opt['val']['suffix']:
save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
f'{img_name}_{self.opt["val"]["suffix"]}.png')
else:
save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
f'{img_name}_{self.opt["name"]}.png')
imwrite(sr_img, save_img_path)
if with_metrics:
# calculate metrics
for name, opt_ in self.opt['val']['metrics'].items():
metric_data = dict(img1=sr_img, img2=gt_img)
self.metric_results[name] += calculate_metric(metric_data, opt_)
pbar.update(1)
pbar.set_description(f'Test {img_name}')
pbar.close()
if with_metrics:
for metric in self.metric_results.keys():
self.metric_results[metric] /= (idx + 1)
self._log_validation_metric_values(current_iter, dataset_name, tb_logger)
def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger):
log_str = f'Validation {dataset_name}\n'
for metric, value in self.metric_results.items():
log_str += f'\t # {metric}: {value:.4f}\n'
logger = get_root_logger()
logger.info(log_str)
if tb_logger:
for metric, value in self.metric_results.items():
tb_logger.add_scalar(f'metrics/{metric}', value, current_iter)
def get_current_visuals(self):
out_dict = OrderedDict()
out_dict['lq'] = self.lq.detach().cpu()
out_dict['result'] = self.output.detach().cpu()
if hasattr(self, 'gt'):
out_dict['gt'] = self.gt.detach().cpu()
return out_dict
def save(self, epoch, current_iter):
if hasattr(self, 'ema_decay'):
self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema'])
else:
self.save_network(self.net_g, 'net_g', current_iter)
self.save_training_state(epoch, current_iter)
|