641 lines
32 KiB
Python
641 lines
32 KiB
Python
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 models.base_model import BaseModel
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from models.loss import GANLoss
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from apex import amp
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import torch.nn.functional as F
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import glob
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import random
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import torchvision.utils as utils
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import os
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logger = logging.getLogger('base')
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class SRGANModel(BaseModel):
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def __init__(self, opt):
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super(SRGANModel, 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 networks and load pretrained models
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self.netG = networks.define_G(opt).to(self.device)
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if self.is_train:
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self.netD = networks.define_D(opt).to(self.device)
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if 'network_C' in opt.keys():
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self.netC = networks.define_G(opt, net_key='network_C').to(self.device)
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# The corruptor net is fixed. Lock 'her down.
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self.netC.eval()
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for p in self.netC.parameters():
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p.requires_grad = True
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else:
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self.netC = None
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# define losses, optimizer and scheduler
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if self.is_train:
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self.mega_batch_factor = train_opt['mega_batch_factor']
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if self.mega_batch_factor is None:
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self.mega_batch_factor = 1
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# G pixel loss
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if train_opt['pixel_weight'] > 0:
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l_pix_type = train_opt['pixel_criterion']
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if l_pix_type == 'l1':
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self.cri_pix = nn.L1Loss().to(self.device)
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elif l_pix_type == 'l2':
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self.cri_pix = nn.MSELoss().to(self.device)
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else:
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raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_pix_type))
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self.l_pix_w = train_opt['pixel_weight']
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else:
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logger.info('Remove pixel loss.')
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self.cri_pix = None
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# G feature loss
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if train_opt['feature_weight'] > 0:
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l_fea_type = train_opt['feature_criterion']
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if l_fea_type == 'l1':
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self.cri_fea = nn.L1Loss().to(self.device)
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elif l_fea_type == 'l2':
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self.cri_fea = nn.MSELoss().to(self.device)
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else:
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raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_fea_type))
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self.l_fea_w = train_opt['feature_weight']
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self.l_fea_w_decay = train_opt['feature_weight_decay']
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self.l_fea_w_decay_steps = train_opt['feature_weight_decay_steps']
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self.l_fea_w_minimum = train_opt['feature_weight_minimum']
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else:
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logger.info('Remove feature loss.')
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self.cri_fea = None
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if self.cri_fea: # load VGG perceptual loss
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self.netF = networks.define_F(opt, use_bn=False).to(self.device)
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if opt['dist']:
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pass # do not need to use DistributedDataParallel for netF
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else:
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self.netF = DataParallel(self.netF)
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# GD gan loss
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self.cri_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device)
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self.l_gan_w = train_opt['gan_weight']
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# D_update_ratio and D_init_iters
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self.D_update_ratio = train_opt['D_update_ratio'] if train_opt['D_update_ratio'] else 1
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self.D_init_iters = train_opt['D_init_iters'] if train_opt['D_init_iters'] else 0
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self.G_warmup = train_opt['G_warmup'] if train_opt['G_warmup'] else 0
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self.D_noise_theta = train_opt['D_noise_theta_init'] if train_opt['D_noise_theta_init'] else 0
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self.D_noise_final = train_opt['D_noise_final_it'] if train_opt['D_noise_final_it'] else 0
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self.D_noise_theta_floor = train_opt['D_noise_theta_floor'] if train_opt['D_noise_theta_floor'] else 0
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self.corruptor_swapout_steps = train_opt['corruptor_swapout_steps'] if train_opt['corruptor_swapout_steps'] else 500
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self.corruptor_usage_prob = train_opt['corruptor_usage_probability'] if train_opt['corruptor_usage_probability'] else .5
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# optimizers
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# G
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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_G'], train_opt['beta2_G']))
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self.optimizers.append(self.optimizer_G)
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# D
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wd_D = train_opt['weight_decay_D'] if train_opt['weight_decay_D'] else 0
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self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'],
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weight_decay=wd_D,
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betas=(train_opt['beta1_D'], train_opt['beta2_D']))
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self.optimizers.append(self.optimizer_D)
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# AMP
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[self.netG, self.netD], [self.optimizer_G, self.optimizer_D] = \
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amp.initialize([self.netG, self.netD], [self.optimizer_G, self.optimizer_D], opt_level=self.amp_level, num_losses=3)
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# DataParallel
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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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if self.is_train:
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if opt['dist']:
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self.netD = DistributedDataParallel(self.netD,
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device_ids=[torch.cuda.current_device()])
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else:
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self.netD = DataParallel(self.netD)
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self.netG.train()
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self.netD.train()
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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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force_lr=train_opt['force_lr']))
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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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# Swapout params
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self.swapout_G_freq = train_opt['swapout_G_freq'] if train_opt['swapout_G_freq'] else 0
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self.swapout_G_duration = 0
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self.swapout_D_freq = train_opt['swapout_D_freq'] if train_opt['swapout_D_freq'] else 0
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self.swapout_D_duration = 0
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self.swapout_duration = train_opt['swapout_duration'] if train_opt['swapout_duration'] else 0
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self.print_network() # print network
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self.load() # load G and D if needed
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self.load_random_corruptor()
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def feed_data(self, data, need_GT=True):
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_profile = True
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if _profile:
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from time import time
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_t = time()
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# Corrupt the data with the given corruptor, if specified.
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self.fed_LQ = data['LQ'].to(self.device)
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if self.netC and random.random() < self.corruptor_usage_prob:
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with torch.no_grad():
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corrupted_L = self.netC(self.fed_LQ)[0].detach()
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else:
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corrupted_L = self.fed_LQ
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self.var_L = torch.chunk(corrupted_L, chunks=self.mega_batch_factor, dim=0)
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if need_GT:
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self.var_H = [t.to(self.device) for t in torch.chunk(data['GT'], chunks=self.mega_batch_factor, dim=0)]
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input_ref = data['ref'] if 'ref' in data else data['GT']
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self.var_ref = [t.to(self.device) for t in torch.chunk(input_ref, chunks=self.mega_batch_factor, dim=0)]
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self.pix = [t.to(self.device) for t in torch.chunk(data['PIX'], chunks=self.mega_batch_factor, dim=0)]
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def optimize_parameters(self, step):
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_profile = False
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if _profile:
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from time import time
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_t = time()
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# Some generators have variants depending on the current step.
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if hasattr(self.netG.module, "update_for_step"):
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self.netG.module.update_for_step(step, os.path.join(self.opt['path']['models'], ".."))
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# G
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for p in self.netD.parameters():
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p.requires_grad = False
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if step > self.D_init_iters:
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self.optimizer_G.zero_grad()
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self.swapout_D(step)
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self.swapout_G(step)
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# Turning off G-grad is required to enable mega-batching and D_update_ratio to work together for some reason.
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if step % self.D_update_ratio == 0 and step > self.D_init_iters:
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for p in self.netG.parameters():
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p.requires_grad = True
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else:
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for p in self.netG.parameters():
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p.requires_grad = False
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# Calculate a standard deviation for the gaussian noise to be applied to the discriminator, termed noise-theta.
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if self.D_noise_final == 0:
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noise_theta = 0
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else:
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noise_theta = (self.D_noise_theta - self.D_noise_theta_floor) * (self.D_noise_final - min(step, self.D_noise_final)) / self.D_noise_final + self.D_noise_theta_floor
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if _profile:
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print("Misc setup %f" % (time() - _t,))
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_t = time()
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self.fake_GenOut = []
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self.fake_H = []
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var_ref_skips = []
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for var_L, var_H, var_ref, pix in zip(self.var_L, self.var_H, self.var_ref, self.pix):
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fake_GenOut = self.netG(var_L)
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if _profile:
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print("Gen forward %f" % (time() - _t,))
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_t = time()
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# Extract the image output. For generators that output skip-through connections, the master output is always
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# the first element of the tuple.
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if isinstance(fake_GenOut, tuple):
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gen_img = fake_GenOut[0]
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# The following line detaches all generator outputs that are not None.
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self.fake_GenOut.append(tuple([(x.detach() if x is not None else None) for x in list(fake_GenOut)]))
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var_ref = (var_ref,) # This is a tuple for legacy reasons.
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else:
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gen_img = fake_GenOut
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self.fake_GenOut.append(fake_GenOut.detach())
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l_g_total = 0
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if step % self.D_update_ratio == 0 and step > self.D_init_iters:
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if self.cri_pix: # pixel loss
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l_g_pix = self.l_pix_w * self.cri_pix(gen_img, pix)
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l_g_pix_log = l_g_pix / self.l_pix_w
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l_g_total += l_g_pix
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if self.cri_fea: # feature loss
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real_fea = self.netF(pix).detach()
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fake_fea = self.netF(gen_img)
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l_g_fea = self.l_fea_w * self.cri_fea(fake_fea, real_fea)
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l_g_fea_log = l_g_fea / self.l_fea_w
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l_g_total += l_g_fea
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if _profile:
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print("Fea forward %f" % (time() - _t,))
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_t = time()
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# Decay the influence of the feature loss. As the model trains, the GAN will play a stronger role
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# in the resultant image.
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if step % self.l_fea_w_decay_steps == 0:
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self.l_fea_w = max(self.l_fea_w_minimum, self.l_fea_w * self.l_fea_w_decay)
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if self.l_gan_w > 0:
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if self.opt['train']['gan_type'] == 'gan' or self.opt['train']['gan_type'] == 'pixgan':
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pred_g_fake = self.netD(fake_GenOut)
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l_g_gan = self.l_gan_w * self.cri_gan(pred_g_fake, True)
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elif self.opt['train']['gan_type'] == 'ragan':
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pred_d_real = self.netD(var_ref).detach()
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pred_g_fake = self.netD(fake_GenOut)
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l_g_gan = self.l_gan_w * (
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self.cri_gan(pred_d_real - torch.mean(pred_g_fake), False) +
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self.cri_gan(pred_g_fake - torch.mean(pred_d_real), True)) / 2
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l_g_gan_log = l_g_gan / self.l_gan_w
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l_g_total += l_g_gan
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# Scale the loss down by the batch factor.
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l_g_total_log = l_g_total
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l_g_total = l_g_total / self.mega_batch_factor
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with amp.scale_loss(l_g_total, self.optimizer_G, loss_id=0) as l_g_total_scaled:
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l_g_total_scaled.backward()
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if _profile:
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print("Gen backward %f" % (time() - _t,))
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_t = time()
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self.optimizer_G.step()
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if _profile:
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print("Gen step %f" % (time() - _t,))
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_t = time()
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# D
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if self.l_gan_w > 0 and step > self.G_warmup:
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for p in self.netD.parameters():
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p.requires_grad = True
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noise = torch.randn_like(var_ref[0]) * noise_theta
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noise.to(self.device)
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self.optimizer_D.zero_grad()
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real_disc_images = []
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fake_disc_images = []
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for var_L, var_H, var_ref, pix in zip(self.var_L, self.var_H, self.var_ref, self.pix):
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# Re-compute generator outputs (post-update).
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with torch.no_grad():
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fake_H = self.netG(var_L)
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# The following line detaches all generator outputs that are not None.
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fake_H = tuple([(x.detach() if x is not None else None) for x in list(fake_H)])
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if _profile:
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print("Gen forward for disc %f" % (time() - _t,))
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_t = time()
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# Apply noise to the inputs to slow discriminator convergence.
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var_ref = (var_ref + noise,)
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fake_H = (fake_H[0] + noise,) + fake_H[1:]
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if self.opt['train']['gan_type'] == 'gan':
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# need to forward and backward separately, since batch norm statistics differ
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# real
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pred_d_real = self.netD(var_ref)
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l_d_real = self.cri_gan(pred_d_real, True) / self.mega_batch_factor
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l_d_real_log = l_d_real * self.mega_batch_factor
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with amp.scale_loss(l_d_real, self.optimizer_D, loss_id=2) as l_d_real_scaled:
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l_d_real_scaled.backward()
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# fake
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pred_d_fake = self.netD(fake_H)
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l_d_fake = self.cri_gan(pred_d_fake, False) / self.mega_batch_factor
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l_d_fake_log = l_d_fake * self.mega_batch_factor
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with amp.scale_loss(l_d_fake, self.optimizer_D, loss_id=1) as l_d_fake_scaled:
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l_d_fake_scaled.backward()
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if self.opt['train']['gan_type'] == 'pixgan':
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# randomly determine portions of the image to swap to keep the discriminator honest.
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pixdisc_channels, pixdisc_output_reduction = self.netD.module.pixgan_parameters()
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disc_output_shape = (var_ref[0].shape[0], pixdisc_channels, var_ref[0].shape[2] // pixdisc_output_reduction, var_ref[0].shape[3] // pixdisc_output_reduction)
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b, _, w, h = var_ref[0].shape
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real = torch.ones((b, pixdisc_channels, w, h), device=var_ref[0].device)
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fake = torch.zeros((b, pixdisc_channels, w, h), device=var_ref[0].device)
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SWAP_MAX_DIM = w // 4
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SWAP_MIN_DIM = 16
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assert SWAP_MAX_DIM > 0
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random_swap_count = random.randint(0, 4)
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for i in range(random_swap_count):
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# Make the swap across fake_H and var_ref
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swap_x, swap_y = random.randint(0, w - SWAP_MIN_DIM), random.randint(0, h - SWAP_MIN_DIM)
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swap_w, swap_h = random.randint(SWAP_MIN_DIM, SWAP_MAX_DIM), random.randint(SWAP_MIN_DIM, SWAP_MAX_DIM)
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if swap_x + swap_w > w:
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swap_w = w - swap_x
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if swap_y + swap_h > h:
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swap_h = h - swap_y
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t = fake_H[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)].clone()
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fake_H[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = var_ref[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)]
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var_ref[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = t
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real[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = 0.0
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fake[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = 1.0
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# Interpolate down to the dimensionality that the discriminator uses.
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real = F.interpolate(real, size=disc_output_shape[2:], mode="bilinear")
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fake = F.interpolate(fake, size=disc_output_shape[2:], mode="bilinear")
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# We're also assuming that this is exactly how the flattened discriminator output is generated.
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real = real.view(-1, 1)
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fake = fake.view(-1, 1)
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# real
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pred_d_real = self.netD(var_ref)
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l_d_real = self.cri_gan(pred_d_real, real) / self.mega_batch_factor
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l_d_real_log = l_d_real * self.mega_batch_factor
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with amp.scale_loss(l_d_real, self.optimizer_D, loss_id=2) as l_d_real_scaled:
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l_d_real_scaled.backward()
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# fake
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pred_d_fake = self.netD(fake_H)
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l_d_fake = self.cri_gan(pred_d_fake, fake) / self.mega_batch_factor
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l_d_fake_log = l_d_fake * self.mega_batch_factor
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with amp.scale_loss(l_d_fake, self.optimizer_D, loss_id=1) as l_d_fake_scaled:
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l_d_fake_scaled.backward()
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pdr = pred_d_real.detach() + torch.abs(torch.min(pred_d_real))
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pdr = pdr / torch.max(pdr)
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|
real_disc_images.append(pdr.view(disc_output_shape))
|
|
pdf = pred_d_fake.detach() + torch.abs(torch.min(pred_d_fake))
|
|
pdf = pdf / torch.max(pdf)
|
|
fake_disc_images.append(pdf.view(disc_output_shape))
|
|
|
|
elif self.opt['train']['gan_type'] == 'ragan':
|
|
pred_d_fake = self.netD(fake_H).detach()
|
|
pred_d_real = self.netD(var_ref)
|
|
|
|
if _profile:
|
|
print("Double disc forward (RAGAN) %f" % (time() - _t,))
|
|
_t = time()
|
|
|
|
l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True) * 0.5 / self.mega_batch_factor
|
|
l_d_real_log = l_d_real * self.mega_batch_factor * 2
|
|
with amp.scale_loss(l_d_real, self.optimizer_D, loss_id=2) as l_d_real_scaled:
|
|
l_d_real_scaled.backward()
|
|
|
|
if _profile:
|
|
print("Disc backward 1 (RAGAN) %f" % (time() - _t,))
|
|
_t = time()
|
|
|
|
pred_d_fake = self.netD(fake_H)
|
|
l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real.detach()), False) * 0.5 / self.mega_batch_factor
|
|
l_d_fake_log = l_d_fake * self.mega_batch_factor * 2
|
|
with amp.scale_loss(l_d_fake, self.optimizer_D, loss_id=1) as l_d_fake_scaled:
|
|
l_d_fake_scaled.backward()
|
|
|
|
if _profile:
|
|
print("Disc forward/backward 2 (RAGAN) %f" % (time() - _t,))
|
|
_t = time()
|
|
|
|
# Append var_ref here, so that we can inspect the alterations the disc made if pixgan
|
|
var_ref_skips.append(var_ref[0].detach())
|
|
self.fake_H.append(fake_H[0].detach())
|
|
self.optimizer_D.step()
|
|
|
|
|
|
if _profile:
|
|
print("Disc step %f" % (time() - _t,))
|
|
_t = time()
|
|
|
|
# Log sample images from first microbatch.
|
|
if step % 50 == 0:
|
|
sample_save_path = os.path.join(self.opt['path']['models'], "..", "temp")
|
|
os.makedirs(os.path.join(sample_save_path, "hr"), exist_ok=True)
|
|
os.makedirs(os.path.join(sample_save_path, "lr"), exist_ok=True)
|
|
os.makedirs(os.path.join(sample_save_path, "gen"), exist_ok=True)
|
|
os.makedirs(os.path.join(sample_save_path, "disc_fake"), exist_ok=True)
|
|
os.makedirs(os.path.join(sample_save_path, "pix"), exist_ok=True)
|
|
os.makedirs(os.path.join(sample_save_path, "disc"), exist_ok=True)
|
|
multi_gen = False
|
|
if isinstance(self.fake_GenOut[0], tuple):
|
|
os.makedirs(os.path.join(sample_save_path, "ref"), exist_ok=True)
|
|
multi_gen = True
|
|
|
|
# fed_LQ is not chunked.
|
|
for i in range(self.mega_batch_factor):
|
|
utils.save_image(self.var_H[i].cpu(), os.path.join(sample_save_path, "hr", "%05i_%02i.png" % (step, i)))
|
|
utils.save_image(self.var_L[i].cpu(), os.path.join(sample_save_path, "lr", "%05i_%02i.png" % (step, i)))
|
|
utils.save_image(self.pix[i].cpu(), os.path.join(sample_save_path, "pix", "%05i_%02i.png" % (step, i)))
|
|
if multi_gen:
|
|
utils.save_image(self.fake_GenOut[i][0].cpu(), os.path.join(sample_save_path, "gen", "%05i_%02i.png" % (step, i)))
|
|
if self.l_gan_w > 0 and step > self.G_warmup and self.opt['train']['gan_type'] == 'pixgan':
|
|
utils.save_image(var_ref_skips[i].cpu(), os.path.join(sample_save_path, "ref", "%05i_%02i.png" % (step, i)))
|
|
utils.save_image(self.fake_H[i], os.path.join(sample_save_path, "disc_fake", "fake%05i_%02i.png" % (step, i)))
|
|
utils.save_image(F.interpolate(fake_disc_images[i], scale_factor=4), os.path.join(sample_save_path, "disc", "fake%05i_%02i.png" % (step, i)))
|
|
utils.save_image(F.interpolate(real_disc_images[i], scale_factor=4), os.path.join(sample_save_path, "disc", "real%05i_%02i.png" % (step, i)))
|
|
else:
|
|
utils.save_image(self.fake_GenOut[i].cpu(), os.path.join(sample_save_path, "gen", "%05i_%02i.png" % (step, i)))
|
|
|
|
# Log metrics
|
|
if step % self.D_update_ratio == 0 and step > self.D_init_iters:
|
|
if self.cri_pix:
|
|
self.add_log_entry('l_g_pix', l_g_pix_log.item())
|
|
if self.cri_fea:
|
|
self.add_log_entry('feature_weight', self.l_fea_w)
|
|
self.add_log_entry('l_g_fea', l_g_fea_log.item())
|
|
if self.l_gan_w > 0:
|
|
self.add_log_entry('l_g_gan', l_g_gan_log.item())
|
|
self.add_log_entry('l_g_total', l_g_total_log.item())
|
|
if self.l_gan_w > 0 and step > self.G_warmup:
|
|
self.add_log_entry('l_d_real', l_d_real_log.item())
|
|
self.add_log_entry('l_d_fake', l_d_fake_log.item())
|
|
self.add_log_entry('D_fake', torch.mean(pred_d_fake.detach()))
|
|
self.add_log_entry('D_diff', torch.mean(pred_d_fake) - torch.mean(pred_d_real))
|
|
|
|
if step % self.corruptor_swapout_steps == 0 and step > 0:
|
|
self.load_random_corruptor()
|
|
|
|
# Allows the log to serve as an easy-to-use rotating buffer.
|
|
def add_log_entry(self, key, value):
|
|
key_it = "%s_it" % (key,)
|
|
log_rotating_buffer_size = 50
|
|
if key not in self.log_dict.keys():
|
|
self.log_dict[key] = []
|
|
self.log_dict[key_it] = 0
|
|
if len(self.log_dict[key]) < log_rotating_buffer_size:
|
|
self.log_dict[key].append(value)
|
|
else:
|
|
self.log_dict[key][self.log_dict[key_it] % log_rotating_buffer_size] = value
|
|
self.log_dict[key_it] += 1
|
|
|
|
def pick_rand_prev_model(self, model_suffix):
|
|
previous_models = glob.glob(os.path.join(self.opt['path']['models'], "*_%s.pth" % (model_suffix,)))
|
|
if len(previous_models) <= 1:
|
|
return None
|
|
# Just a note: this intentionally includes the swap model in the list of possibilities.
|
|
return previous_models[random.randint(0, len(previous_models)-1)]
|
|
|
|
def compute_fea_loss(self, real, fake):
|
|
with torch.no_grad():
|
|
real = real.unsqueeze(dim=0)
|
|
fake = fake.unsqueeze(dim=0)
|
|
real_fea = self.netF(real).detach()
|
|
fake_fea = self.netF(fake)
|
|
return self.cri_fea(fake_fea, real_fea).item()
|
|
|
|
# Called before verification/checkpoint to ensure we're using the real models and not a swapout variant.
|
|
def force_restore_swapout(self):
|
|
if self.swapout_D_duration > 0:
|
|
logger.info("Swapping back to current D model: %s" % (self.stashed_D,))
|
|
self.load_network(self.stashed_D, self.netD, self.opt['path']['strict_load'])
|
|
self.stashed_D = None
|
|
self.swapout_D_duration = 0
|
|
if self.swapout_G_duration > 0:
|
|
logger.info("Swapping back to current G model: %s" % (self.stashed_G,))
|
|
self.load_network(self.stashed_G, self.netG, self.opt['path']['strict_load'])
|
|
self.stashed_G = None
|
|
self.swapout_G_duration = 0
|
|
|
|
def swapout_D(self, step):
|
|
if self.swapout_D_duration > 0:
|
|
self.swapout_D_duration -= 1
|
|
if self.swapout_D_duration == 0:
|
|
# Swap back.
|
|
logger.info("Swapping back to current D model: %s" % (self.stashed_D,))
|
|
self.load_network(self.stashed_D, self.netD, self.opt['path']['strict_load'])
|
|
self.stashed_D = None
|
|
elif self.swapout_D_freq != 0 and step % self.swapout_D_freq == 0:
|
|
swapped_model = self.pick_rand_prev_model('D')
|
|
if swapped_model is not None:
|
|
logger.info("Swapping to previous D model: %s" % (swapped_model,))
|
|
self.stashed_D = self.save_network(self.netD, 'D', 'swap_model')
|
|
self.load_network(swapped_model, self.netD, self.opt['path']['strict_load'])
|
|
self.swapout_D_duration = self.swapout_duration
|
|
|
|
def swapout_G(self, step):
|
|
if self.swapout_G_duration > 0:
|
|
self.swapout_G_duration -= 1
|
|
if self.swapout_G_duration == 0:
|
|
# Swap back.
|
|
logger.info("Swapping back to current G model: %s" % (self.stashed_G,))
|
|
self.load_network(self.stashed_G, self.netG, self.opt['path']['strict_load'])
|
|
self.stashed_G = None
|
|
elif self.swapout_G_freq != 0 and step % self.swapout_G_freq == 0:
|
|
swapped_model = self.pick_rand_prev_model('G')
|
|
if swapped_model is not None:
|
|
logger.info("Swapping to previous G model: %s" % (swapped_model,))
|
|
self.stashed_G = self.save_network(self.netG, 'G', 'swap_model')
|
|
self.load_network(swapped_model, self.netG, self.opt['path']['strict_load'])
|
|
self.swapout_G_duration = self.swapout_duration
|
|
|
|
def test(self):
|
|
self.netG.eval()
|
|
with torch.no_grad():
|
|
self.fake_GenOut = [self.netG(self.var_L[0])]
|
|
self.netG.train()
|
|
|
|
# Fetches a summary of the log.
|
|
def get_current_log(self, step):
|
|
return_log = {}
|
|
for k in self.log_dict.keys():
|
|
if not isinstance(self.log_dict[k], list):
|
|
continue
|
|
return_log[k] = sum(self.log_dict[k]) / len(self.log_dict[k])
|
|
|
|
# Some generators can do their own metric logging.
|
|
if hasattr(self.netG.module, "get_debug_values"):
|
|
return_log.update(self.netG.module.get_debug_values(step))
|
|
|
|
return return_log
|
|
|
|
def get_current_visuals(self, need_GT=True):
|
|
out_dict = OrderedDict()
|
|
out_dict['LQ'] = self.var_L[0].detach().float().cpu()
|
|
gen_batch = self.fake_GenOut[0]
|
|
if isinstance(gen_batch, tuple):
|
|
gen_batch = gen_batch[0]
|
|
out_dict['rlt'] = gen_batch.detach().float().cpu()
|
|
if need_GT:
|
|
out_dict['GT'] = self.var_H[0].detach().float().cpu()
|
|
return out_dict
|
|
|
|
def print_network(self):
|
|
# Generator
|
|
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)
|
|
if self.is_train:
|
|
# Discriminator
|
|
s, n = self.get_network_description(self.netD)
|
|
if isinstance(self.netD, nn.DataParallel) or isinstance(self.netD,
|
|
DistributedDataParallel):
|
|
net_struc_str = '{} - {}'.format(self.netD.__class__.__name__,
|
|
self.netD.module.__class__.__name__)
|
|
else:
|
|
net_struc_str = '{}'.format(self.netD.__class__.__name__)
|
|
if self.rank <= 0:
|
|
logger.info('Network D structure: {}, with parameters: {:,d}'.format(
|
|
net_struc_str, n))
|
|
logger.info(s)
|
|
|
|
if self.cri_fea: # F, Perceptual Network
|
|
s, n = self.get_network_description(self.netF)
|
|
if isinstance(self.netF, nn.DataParallel) or isinstance(
|
|
self.netF, DistributedDataParallel):
|
|
net_struc_str = '{} - {}'.format(self.netF.__class__.__name__,
|
|
self.netF.module.__class__.__name__)
|
|
else:
|
|
net_struc_str = '{}'.format(self.netF.__class__.__name__)
|
|
if self.rank <= 0:
|
|
logger.info('Network F 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'])
|
|
load_path_D = self.opt['path']['pretrain_model_D']
|
|
if self.opt['is_train'] and load_path_D is not None:
|
|
logger.info('Loading model for D [{:s}] ...'.format(load_path_D))
|
|
self.load_network(load_path_D, self.netD, self.opt['path']['strict_load'])
|
|
|
|
def load_random_corruptor(self):
|
|
if self.netC is None:
|
|
return
|
|
corruptor_files = glob.glob(os.path.join(self.opt['path']['pretrained_corruptors_dir'], "*.pth"))
|
|
corruptor_to_load = corruptor_files[random.randint(0, len(corruptor_files)-1)]
|
|
logger.info('Swapping corruptor to: %s' % (corruptor_to_load,))
|
|
self.load_network(corruptor_to_load, self.netC, self.opt['path']['strict_load'])
|
|
|
|
def save(self, iter_step):
|
|
self.save_network(self.netG, 'G', iter_step)
|
|
self.save_network(self.netD, 'D', iter_step)
|