458 lines
20 KiB
Python
458 lines
20 KiB
Python
import os
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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.optim import lr_scheduler
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from apex import amp
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import models.networks as networks
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from .base_model import BaseModel
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from models.loss import GANLoss
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import torchvision.utils as utils
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from .archs.SPSR_arch import ImageGradient, ImageGradientNoPadding
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logger = logging.getLogger('base')
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class SPSRModel(BaseModel):
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def __init__(self, opt):
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super(SPSRModel, self).__init__(opt)
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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) # G
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if self.is_train:
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self.netD = networks.define_D(opt).to(self.device) # D
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self.netD_grad = networks.define_D(opt).to(self.device) # D_grad
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self.netG.train()
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self.netD.train()
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self.netD_grad.train()
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self.mega_batch_factor = 1
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self.load() # load G and D if needed
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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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# 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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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(use_bn=False).to(self.device)
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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 are for WGAN
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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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# Branch_init_iters
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self.branch_pretrain = train_opt['branch_pretrain'] if train_opt['branch_pretrain'] else 0
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self.branch_init_iters = train_opt['branch_init_iters'] if train_opt['branch_init_iters'] else 1
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# gradient_pixel_loss
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if train_opt['gradient_pixel_weight'] > 0:
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self.cri_pix_grad = nn.MSELoss().to(self.device)
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self.l_pix_grad_w = train_opt['gradient_pixel_weight']
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else:
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self.cri_pix_grad = None
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# gradient_gan_loss
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if train_opt['gradient_gan_weight'] > 0:
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self.cri_grad_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device)
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self.l_gan_grad_w = train_opt['gradient_gan_weight']
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else:
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self.cri_grad_gan = None
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# G_grad pixel loss
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if train_opt['pixel_branch_weight'] > 0:
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l_pix_type = train_opt['pixel_branch_criterion']
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if l_pix_type == 'l1':
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self.cri_pix_branch = nn.L1Loss().to(self.device)
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elif l_pix_type == 'l2':
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self.cri_pix_branch = 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_branch_w = train_opt['pixel_branch_weight']
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else:
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logger.info('Remove G_grad pixel loss.')
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self.cri_pix_branch = None
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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(): # optimize 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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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, betas=(train_opt['beta1_G'], 0.999))
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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, betas=(train_opt['beta1_D'], 0.999))
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self.optimizers.append(self.optimizer_D)
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# D_grad
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wd_D_grad = train_opt['weight_decay_D'] if train_opt['weight_decay_D'] else 0
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self.optimizer_D_grad = torch.optim.Adam(self.netD_grad.parameters(), lr=train_opt['lr_D'], \
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weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
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self.optimizers.append(self.optimizer_D_grad)
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# AMP
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[self.netG, self.netD, self.netD_grad], [self.optimizer_G, self.optimizer_D, self.optimizer_D_grad] = \
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amp.initialize([self.netG, self.netD, self.netD_grad],
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[self.optimizer_G, self.optimizer_D, self.optimizer_D_grad],
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opt_level=self.amp_level, num_losses=3)
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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(lr_scheduler.MultiStepLR(optimizer, \
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train_opt['lr_steps'], train_opt['lr_gamma']))
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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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self.get_grad = ImageGradient()
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self.get_grad_nopadding = ImageGradientNoPadding()
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def feed_data(self, data, need_HR=True):
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# LR
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self.var_L = [t.to(self.device) for t in torch.chunk(data['LQ'], chunks=self.mega_batch_factor, dim=0)]
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if need_HR: # train or val
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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.to(self.device), chunks=self.mega_batch_factor, dim=0)]
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def optimize_parameters(self, step):
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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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if hasattr(self.netD.module, "update_for_step"):
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self.netD.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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for p in self.netD_grad.parameters():
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p.requires_grad = False
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if(self.branch_pretrain):
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if(step < self.branch_init_iters):
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for k,v in self.netG.named_parameters():
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if 'f_' not in k :
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v.requires_grad=False
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else:
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for k,v in self.netG.named_parameters():
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if 'f_' not in k :
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v.requires_grad=True
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self.optimizer_G.zero_grad()
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self.fake_H_branch = []
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self.fake_H = []
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self.grad_LR = []
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for var_L, var_H, var_ref in zip(self.var_L, self.var_H, self.var_ref):
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fake_H_branch, fake_H, grad_LR = self.netG(var_L)
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self.fake_H_branch.append(fake_H_branch.detach())
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self.fake_H.append(fake_H.detach())
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self.grad_LR.append(grad_LR.detach())
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fake_H_grad = self.get_grad(fake_H)
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var_H_grad = self.get_grad(var_H)
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var_ref_grad = self.get_grad(var_ref)
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var_H_grad_nopadding = self.get_grad_nopadding(var_H)
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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(fake_H, var_H)
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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(var_H).detach()
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fake_fea = self.netF(fake_H)
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l_g_fea = self.l_fea_w * self.cri_fea(fake_fea, real_fea)
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l_g_total += l_g_fea
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if self.cri_pix_grad: #gradient pixel loss
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l_g_pix_grad = self.l_pix_grad_w * self.cri_pix_grad(fake_H_grad, var_H_grad)
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l_g_total += l_g_pix_grad
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if self.cri_pix_branch: #branch pixel loss
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l_g_pix_grad_branch = self.l_pix_branch_w * self.cri_pix_branch(fake_H_branch, var_H_grad_nopadding)
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l_g_total += l_g_pix_grad_branch
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if self.l_gan_w > 0:
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# G gan + cls loss
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pred_g_fake = self.netD(fake_H)
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pred_d_real = self.netD(var_ref).detach()
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l_g_gan = self.l_gan_w * (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_total += l_g_gan
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if self.cri_grad_gan:
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# grad G gan + cls loss
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pred_g_fake_grad = self.netD_grad(fake_H_grad)
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pred_d_real_grad = self.netD_grad(var_ref_grad).detach()
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l_g_gan_grad = self.l_gan_grad_w * (self.cri_grad_gan(pred_d_real_grad - torch.mean(pred_g_fake_grad), False) +
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self.cri_grad_gan(pred_g_fake_grad - torch.mean(pred_d_real_grad), True)) /2
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l_g_total += l_g_gan_grad
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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 step % self.D_update_ratio == 0 and step > self.D_init_iters:
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self.optimizer_G.step()
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if self.l_gan_w > 0:
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# D
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for p in self.netD.parameters():
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p.requires_grad = True
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self.optimizer_D.zero_grad()
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for var_ref, fake_H in zip(self.var_ref, self.fake_H):
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pred_d_real = self.netD(var_ref)
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pred_d_fake = self.netD(fake_H) # detach to avoid BP to G
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l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True)
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l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real), False)
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l_d_total = (l_d_real + l_d_fake) / 2
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l_d_total /= self.mega_batch_factor
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with amp.scale_loss(l_d_total, self.optimizer_D, loss_id=1) as l_d_total_scaled:
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l_d_total_scaled.backward()
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self.optimizer_D.step()
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if self.cri_grad_gan:
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for p in self.netD_grad.parameters():
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p.requires_grad = True
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self.optimizer_D_grad.zero_grad()
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for var_ref, fake_H in zip(self.var_ref, self.fake_H):
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fake_H_grad = self.get_grad(fake_H)
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var_ref_grad = self.get_grad(var_ref)
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pred_d_real_grad = self.netD_grad(var_ref_grad)
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pred_d_fake_grad = self.netD_grad(fake_H_grad.detach()) # detach to avoid BP to G
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l_d_real_grad = self.cri_grad_gan(pred_d_real_grad - torch.mean(pred_d_fake_grad), True)
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l_d_fake_grad = self.cri_grad_gan(pred_d_fake_grad - torch.mean(pred_d_real_grad), False)
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l_d_total_grad = (l_d_real_grad + l_d_fake_grad) / 2
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l_d_total_grad /= self.mega_batch_factor
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with amp.scale_loss(l_d_total_grad, self.optimizer_D_grad, loss_id=2) as l_d_total_grad_scaled:
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l_d_total_grad_scaled.backward()
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self.optimizer_D_grad.step()
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# Log sample images from first microbatch.
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if step % 50 == 0:
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sample_save_path = os.path.join(self.opt['path']['models'], "..", "temp")
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os.makedirs(os.path.join(sample_save_path, "hr"), exist_ok=True)
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os.makedirs(os.path.join(sample_save_path, "lr"), exist_ok=True)
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os.makedirs(os.path.join(sample_save_path, "gen"), exist_ok=True)
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os.makedirs(os.path.join(sample_save_path, "gen_grad"), exist_ok=True)
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# fed_LQ is not chunked.
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utils.save_image(self.var_H[0].cpu(), os.path.join(sample_save_path, "hr", "%05i.png" % (step,)))
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utils.save_image(self.var_L[0].cpu(), os.path.join(sample_save_path, "lr", "%05i.png" % (step,)))
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utils.save_image(self.fake_H[0].cpu(), os.path.join(sample_save_path, "gen", "%05i.png" % (step,)))
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utils.save_image(self.grad_LR[0].cpu(), os.path.join(sample_save_path, "gen_grad", "%05i.png" % (step,)))
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# set log
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if step % self.D_update_ratio == 0 and step > self.D_init_iters:
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# G
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if self.cri_pix:
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self.add_log_entry('l_g_pix', l_g_pix.item())
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if self.cri_fea:
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self.add_log_entry('l_g_fea', l_g_fea.item())
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if self.l_gan_w > 0:
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self.add_log_entry('l_g_gan', l_g_gan.item())
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if self.cri_pix_branch: #branch pixel loss
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self.add_log_entry('l_g_pix_grad_branch', l_g_pix_grad_branch.item())
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if self.l_gan_w > 0:
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self.add_log_entry('l_d_real', l_d_real.item())
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self.add_log_entry('l_d_fake', l_d_fake.item())
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self.add_log_entry('l_d_real_grad', l_d_real_grad.item())
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self.add_log_entry('l_d_fake_grad', l_d_fake_grad.item())
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self.add_log_entry('D_real', torch.mean(pred_d_real.detach()))
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self.add_log_entry('D_fake', torch.mean(pred_d_fake.detach()))
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self.add_log_entry('D_real_grad', torch.mean(pred_d_real_grad.detach()))
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self.add_log_entry('D_fake_grad', torch.mean(pred_d_fake_grad.detach()))
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# Allows the log to serve as an easy-to-use rotating buffer.
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def add_log_entry(self, key, value):
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key_it = "%s_it" % (key,)
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log_rotating_buffer_size = 50
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if key not in self.log_dict.keys():
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self.log_dict[key] = []
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self.log_dict[key_it] = 0
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if len(self.log_dict[key]) < log_rotating_buffer_size:
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self.log_dict[key].append(value)
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else:
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self.log_dict[key][self.log_dict[key_it] % log_rotating_buffer_size] = value
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self.log_dict[key_it] += 1
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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_branch = []
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self.fake_H = []
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self.grad_LR = []
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for var_L in self.var_L:
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fake_H_branch, fake_H, grad_LR = self.netG(var_L)
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self.fake_H_branch.append(fake_H_branch)
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self.fake_H.append(fake_H)
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self.grad_LR.append(grad_LR)
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self.netG.train()
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# Fetches a summary of the log.
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def get_current_log(self, step):
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return_log = {}
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for k in self.log_dict.keys():
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if not isinstance(self.log_dict[k], list):
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continue
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return_log[k] = sum(self.log_dict[k]) / len(self.log_dict[k])
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# Some generators can do their own metric logging.
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if hasattr(self.netG.module, "get_debug_values"):
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return_log.update(self.netG.module.get_debug_values(step))
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if hasattr(self.netD.module, "get_debug_values"):
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return_log.update(self.netD.module.get_debug_values(step))
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return return_log
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def get_current_visuals(self, need_HR=True):
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out_dict = OrderedDict()
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out_dict['LR'] = self.var_L[0].float().cpu()
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out_dict['rlt'] = self.fake_H[0].float().cpu()
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out_dict['SR_branch'] = self.fake_H_branch[0].float().cpu()
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out_dict['LR_grad'] = self.grad_LR[0].float().cpu()
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if need_HR:
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out_dict['GT'] = self.var_H[0].float().cpu()
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return out_dict
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def print_network(self):
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# Generator
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s, n = self.get_network_description(self.netG)
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if isinstance(self.netG, nn.DataParallel):
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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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logger.info('Network G structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
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logger.info(s)
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if self.is_train:
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# Disriminator
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s, n = self.get_network_description(self.netD)
|
|
if isinstance(self.netD, nn.DataParallel):
|
|
net_struc_str = '{} - {}'.format(self.netD.__class__.__name__,
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|
self.netD.module.__class__.__name__)
|
|
else:
|
|
net_struc_str = '{}'.format(self.netD.__class__.__name__)
|
|
|
|
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):
|
|
net_struc_str = '{} - {}'.format(self.netF.__class__.__name__,
|
|
self.netF.module.__class__.__name__)
|
|
else:
|
|
net_struc_str = '{}'.format(self.netF.__class__.__name__)
|
|
|
|
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 pretrained model for G [{:s}] ...'.format(load_path_G))
|
|
self.load_network(load_path_G, self.netG)
|
|
load_path_D = self.opt['path']['pretrain_model_D']
|
|
if self.opt['is_train'] and load_path_D is not None:
|
|
logger.info('Loading pretrained model for D [{:s}] ...'.format(load_path_D))
|
|
self.load_network(load_path_D, self.netD)
|
|
load_path_D_grad = self.opt['path']['pretrain_model_D_grad']
|
|
if self.opt['is_train'] and load_path_D_grad is not None:
|
|
logger.info('Loading pretrained model for D_grad [{:s}] ...'.format(load_path_D_grad))
|
|
self.load_network(load_path_D_grad, self.netD_grad)
|
|
|
|
def compute_fea_loss(self, real, fake):
|
|
if self.cri_fea is None:
|
|
return 0
|
|
with torch.no_grad():
|
|
real = real.unsqueeze(dim=0).to(self.device)
|
|
fake = fake.unsqueeze(dim=0).to(self.device)
|
|
real_fea = self.netF(real).detach()
|
|
fake_fea = self.netF(fake)
|
|
return self.cri_fea(fake_fea, real_fea).item()
|
|
|
|
def force_restore_swapout(self):
|
|
pass
|
|
|
|
def save(self, iter_step):
|
|
self.save_network(self.netG, 'G', iter_step)
|
|
self.save_network(self.netD, 'D', iter_step)
|
|
self.save_network(self.netD_grad, 'D_grad', iter_step)
|
|
|
|
# override of load_network that allows loading partial params (like RRDB_PSNR_x4)
|
|
def load_network(self, load_path, network, strict=True):
|
|
if isinstance(network, nn.DataParallel):
|
|
network = network.module
|
|
pretrained_dict = torch.load(load_path)
|
|
model_dict = network.state_dict()
|
|
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
|
|
|
|
model_dict.update(pretrained_dict)
|
|
network.load_state_dict(model_dict) |