forked from mrq/DL-Art-School
171 lines
6.6 KiB
Python
171 lines
6.6 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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from torch.nn.parallel import DataParallel, DistributedDataParallel
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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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from models.loss import CharbonnierLoss
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logger = logging.getLogger('base')
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class SRModel(BaseModel):
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def __init__(self, opt):
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super(SRModel, 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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# define network and load pretrained models
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self.netG = networks.define_G(opt).to(self.device)
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if opt['dist']:
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self.netG = DistributedDataParallel(self.netG, device_ids=[torch.cuda.current_device()])
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else:
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self.netG = DataParallel(self.netG)
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# print network
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self.print_network()
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self.load()
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if self.is_train:
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self.netG.train()
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# loss
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loss_type = train_opt['pixel_criterion']
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if loss_type == 'l1':
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self.cri_pix = nn.L1Loss().to(self.device)
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elif loss_type == 'l2':
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self.cri_pix = nn.MSELoss().to(self.device)
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elif loss_type == 'cb':
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self.cri_pix = CharbonnierLoss().to(self.device)
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else:
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raise NotImplementedError('Loss type [{:s}] is not recognized.'.format(loss_type))
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self.l_pix_w = train_opt['pixel_weight']
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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.netG.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'], train_opt['beta2']))
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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['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.netG(self.var_L)
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l_pix = self.l_pix_w * self.cri_pix(self.fake_H, self.real_H)
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l_pix.backward()
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self.optimizer_G.step()
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# set log
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self.log_dict['l_pix'] = l_pix.item()
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def test(self):
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self.netG.eval()
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with torch.no_grad():
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self.fake_H = self.netG(self.var_L)
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self.netG.train()
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def test_x8(self):
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# from https://github.com/thstkdgus35/EDSR-PyTorch
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self.netG.eval()
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def _transform(v, op):
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# if self.precision != 'single': v = v.float()
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v2np = v.data.cpu().numpy()
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if op == 'v':
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tfnp = v2np[:, :, :, ::-1].copy()
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elif op == 'h':
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tfnp = v2np[:, :, ::-1, :].copy()
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elif op == 't':
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tfnp = v2np.transpose((0, 1, 3, 2)).copy()
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ret = torch.Tensor(tfnp).to(self.device)
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# if self.precision == 'half': ret = ret.half()
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return ret
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lr_list = [self.var_L]
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for tf in 'v', 'h', 't':
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lr_list.extend([_transform(t, tf) for t in lr_list])
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with torch.no_grad():
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sr_list = [self.netG(aug) for aug in lr_list]
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for i in range(len(sr_list)):
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if i > 3:
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sr_list[i] = _transform(sr_list[i], 't')
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if i % 4 > 1:
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sr_list[i] = _transform(sr_list[i], 'h')
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if (i % 4) % 2 == 1:
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sr_list[i] = _transform(sr_list[i], 'v')
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output_cat = torch.cat(sr_list, dim=0)
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self.fake_H = output_cat.mean(dim=0, keepdim=True)
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self.netG.train()
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def get_current_log(self):
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return self.log_dict
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def get_current_visuals(self, need_GT=True):
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out_dict = OrderedDict()
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out_dict['LQ'] = self.var_L.detach()[0].float().cpu()
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out_dict['rlt'] = self.fake_H.detach()[0].float().cpu()
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if need_GT:
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out_dict['GT'] = self.real_H.detach()[0].float().cpu()
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return out_dict
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def print_network(self):
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s, n = self.get_network_description(self.netG)
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if isinstance(self.netG, nn.DataParallel) or isinstance(self.netG, DistributedDataParallel):
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net_struc_str = '{} - {}'.format(self.netG.__class__.__name__,
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self.netG.module.__class__.__name__)
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else:
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net_struc_str = '{}'.format(self.netG.__class__.__name__)
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if self.rank <= 0:
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logger.info('Network G structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
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logger.info(s)
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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 G [{:s}] ...'.format(load_path_G))
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self.load_network(load_path_G, self.netG, self.opt['path']['strict_load'])
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def save(self, iter_label):
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self.save_network(self.netG, 'G', iter_label)
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