463 lines
19 KiB
Python
463 lines
19 KiB
Python
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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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import models.SPSR_networks as networks
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from .base_model import BaseModel
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from models.SPSR_modules.loss import GANLoss, GradientPenaltyLoss
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logger = logging.getLogger('base')
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import torch.nn.functional as F
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class Get_gradient(nn.Module):
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def __init__(self):
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super(Get_gradient, self).__init__()
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kernel_v = [[0, -1, 0],
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[0, 0, 0],
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[0, 1, 0]]
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kernel_h = [[0, 0, 0],
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[-1, 0, 1],
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[0, 0, 0]]
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kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
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kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
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self.weight_h = nn.Parameter(data = kernel_h, requires_grad = False).cuda()
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self.weight_v = nn.Parameter(data = kernel_v, requires_grad = False).cuda()
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def forward(self, x):
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x0 = x[:, 0]
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x1 = x[:, 1]
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x2 = x[:, 2]
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x0_v = F.conv2d(x0.unsqueeze(1), self.weight_v, padding=2)
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x0_h = F.conv2d(x0.unsqueeze(1), self.weight_h, padding=2)
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x1_v = F.conv2d(x1.unsqueeze(1), self.weight_v, padding=2)
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x1_h = F.conv2d(x1.unsqueeze(1), self.weight_h, padding=2)
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x2_v = F.conv2d(x2.unsqueeze(1), self.weight_v, padding=2)
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x2_h = F.conv2d(x2.unsqueeze(1), self.weight_h, padding=2)
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x0 = torch.sqrt(torch.pow(x0_v, 2) + torch.pow(x0_h, 2) + 1e-6)
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x1 = torch.sqrt(torch.pow(x1_v, 2) + torch.pow(x1_h, 2) + 1e-6)
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x2 = torch.sqrt(torch.pow(x2_v, 2) + torch.pow(x2_h, 2) + 1e-6)
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x = torch.cat([x0, x1, x2], dim=1)
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return x
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class Get_gradient_nopadding(nn.Module):
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def __init__(self):
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super(Get_gradient_nopadding, self).__init__()
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kernel_v = [[0, -1, 0],
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[0, 0, 0],
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[0, 1, 0]]
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kernel_h = [[0, 0, 0],
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[-1, 0, 1],
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[0, 0, 0]]
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kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
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kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
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self.weight_h = nn.Parameter(data = kernel_h, requires_grad = False).cuda()
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self.weight_v = nn.Parameter(data = kernel_v, requires_grad = False).cuda()
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def forward(self, x):
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x0 = x[:, 0]
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x1 = x[:, 1]
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x2 = x[:, 2]
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x0_v = F.conv2d(x0.unsqueeze(1), self.weight_v, padding = 1)
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x0_h = F.conv2d(x0.unsqueeze(1), self.weight_h, padding = 1)
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x1_v = F.conv2d(x1.unsqueeze(1), self.weight_v, padding = 1)
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x1_h = F.conv2d(x1.unsqueeze(1), self.weight_h, padding = 1)
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x2_v = F.conv2d(x2.unsqueeze(1), self.weight_v, padding = 1)
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x2_h = F.conv2d(x2.unsqueeze(1), self.weight_h, padding = 1)
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x0 = torch.sqrt(torch.pow(x0_v, 2) + torch.pow(x0_h, 2) + 1e-6)
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x1 = torch.sqrt(torch.pow(x1_v, 2) + torch.pow(x1_h, 2) + 1e-6)
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x2 = torch.sqrt(torch.pow(x2_v, 2) + torch.pow(x2_h, 2) + 1e-6)
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x = torch.cat([x0, x1, x2], dim=1)
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return x
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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_grad(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.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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# 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(opt, 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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if train_opt['gan_type'] == 'wgan-gp':
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self.random_pt = torch.Tensor(1, 1, 1, 1).to(self.device)
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# gradient penalty loss
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self.cri_gp = GradientPenaltyLoss(device=self.device).to(self.device)
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self.l_gp_w = train_opt['gp_weigth']
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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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# 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 = Get_gradient()
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self.get_grad_nopadding = Get_gradient_nopadding()
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def feed_data(self, data, need_HR=True):
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# LR
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self.var_L = data['LQ'].to(self.device)
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if need_HR: # train or val
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self.var_H = data['GT'].to(self.device)
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input_ref = data['ref'] if 'ref' in data else data['GT']
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self.var_ref = input_ref.to(self.device)
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def optimize_parameters(self, step):
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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, self.fake_H, self.grad_LR = self.netG(self.var_L)
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self.fake_H_grad = self.get_grad(self.fake_H)
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self.var_H_grad = self.get_grad(self.var_H)
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self.var_ref_grad = self.get_grad(self.var_ref)
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self.var_H_grad_nopadding = self.get_grad_nopadding(self.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(self.fake_H, self.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(self.var_H).detach()
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fake_fea = self.netF(self.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(self.fake_H_grad, self.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(self.fake_H_branch, self.var_H_grad_nopadding)
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l_g_total += l_g_pix_grad_branch
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# G gan + cls loss
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pred_g_fake = self.netD(self.fake_H)
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pred_d_real = self.netD(self.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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# grad G gan + cls loss
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pred_g_fake_grad = self.netD_grad(self.fake_H_grad)
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pred_d_real_grad = self.netD_grad(self.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.backward()
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self.optimizer_G.step()
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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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l_d_total = 0
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pred_d_real = self.netD(self.var_ref)
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pred_d_fake = self.netD(self.fake_H.detach()) # 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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||
|
|
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||
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|
if self.opt['train']['gan_type'] == 'wgan-gp':
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||
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|
batch_size = self.var_ref.size(0)
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||
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|
if self.random_pt.size(0) != batch_size:
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||
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|
self.random_pt.resize_(batch_size, 1, 1, 1)
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||
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self.random_pt.uniform_() # Draw random interpolation points
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||
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|
interp = self.random_pt * self.fake_H.detach() + (1 - self.random_pt) * self.var_ref
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||
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|
interp.requires_grad = True
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||
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|
interp_crit, _ = self.netD(interp)
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||
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|
l_d_gp = self.l_gp_w * self.cri_gp(interp, interp_crit)
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||
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|
l_d_total += l_d_gp
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||
|
|
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||
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|
l_d_total.backward()
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||
|
|
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||
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|
self.optimizer_D.step()
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||
|
|
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||
|
|
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||
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for p in self.netD_grad.parameters():
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|
p.requires_grad = True
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||
|
|
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||
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|
self.optimizer_D_grad.zero_grad()
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||
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|
l_d_total_grad = 0
|
||
|
|
|
||
|
|
|
||
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|
pred_d_real_grad = self.netD_grad(self.var_ref_grad)
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||
|
|
pred_d_fake_grad = self.netD_grad(self.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)
|
||
|
|
l_d_fake_grad = self.cri_grad_gan(pred_d_fake_grad - torch.mean(pred_d_real_grad), False)
|
||
|
|
|
||
|
|
l_d_total_grad = (l_d_real_grad + l_d_fake_grad) / 2
|
||
|
|
|
||
|
|
|
||
|
|
l_d_total_grad.backward()
|
||
|
|
|
||
|
|
self.optimizer_D_grad.step()
|
||
|
|
|
||
|
|
# Log sample images from first microbatch.
|
||
|
|
if step % 50 == 0:
|
||
|
|
import torchvision.utils as utils
|
||
|
|
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)
|
||
|
|
# fed_LQ is not chunked.
|
||
|
|
utils.save_image(self.var_H.cpu(), os.path.join(sample_save_path, "hr", "%05i.png" % (step,)))
|
||
|
|
utils.save_image(self.var_L.cpu(), os.path.join(sample_save_path, "lr", "%05i.png" % (step,)))
|
||
|
|
utils.save_image(self.fake_H.cpu(), os.path.join(sample_save_path, "gen", "%05i.png" % (step,)))
|
||
|
|
|
||
|
|
|
||
|
|
# set log
|
||
|
|
if step % self.D_update_ratio == 0 and step > self.D_init_iters:
|
||
|
|
# G
|
||
|
|
if self.cri_pix:
|
||
|
|
self.log_dict['l_g_pix'] = l_g_pix.item()
|
||
|
|
if self.cri_fea:
|
||
|
|
self.log_dict['l_g_fea'] = l_g_fea.item()
|
||
|
|
self.log_dict['l_g_gan'] = l_g_gan.item()
|
||
|
|
|
||
|
|
if self.cri_pix_branch: #branch pixel loss
|
||
|
|
self.log_dict['l_g_pix_grad_branch'] = l_g_pix_grad_branch.item()
|
||
|
|
|
||
|
|
# D
|
||
|
|
self.log_dict['l_d_real'] = l_d_real.item()
|
||
|
|
self.log_dict['l_d_fake'] = l_d_fake.item()
|
||
|
|
|
||
|
|
# D_grad
|
||
|
|
self.log_dict['l_d_real_grad'] = l_d_real_grad.item()
|
||
|
|
self.log_dict['l_d_fake_grad'] = l_d_fake_grad.item()
|
||
|
|
|
||
|
|
if self.opt['train']['gan_type'] == 'wgan-gp':
|
||
|
|
self.log_dict['l_d_gp'] = l_d_gp.item()
|
||
|
|
# D outputs
|
||
|
|
self.log_dict['D_real'] = torch.mean(pred_d_real.detach())
|
||
|
|
self.log_dict['D_fake'] = torch.mean(pred_d_fake.detach())
|
||
|
|
|
||
|
|
# D_grad outputs
|
||
|
|
self.log_dict['D_real_grad'] = torch.mean(pred_d_real_grad.detach())
|
||
|
|
self.log_dict['D_fake_grad'] = torch.mean(pred_d_fake_grad.detach())
|
||
|
|
|
||
|
|
def test(self):
|
||
|
|
self.netG.eval()
|
||
|
|
with torch.no_grad():
|
||
|
|
self.fake_H_branch, self.fake_H, self.grad_LR = self.netG(self.var_L)
|
||
|
|
|
||
|
|
self.netG.train()
|
||
|
|
|
||
|
|
def get_current_log(self, step):
|
||
|
|
return self.log_dict
|
||
|
|
|
||
|
|
def get_current_visuals(self, need_HR=True):
|
||
|
|
out_dict = OrderedDict()
|
||
|
|
out_dict['LR'] = self.var_L.detach()[0].float().cpu()
|
||
|
|
|
||
|
|
out_dict['SR'] = self.fake_H.detach()[0].float().cpu()
|
||
|
|
out_dict['SR_branch'] = self.fake_H_branch.detach()[0].float().cpu()
|
||
|
|
out_dict['LR_grad'] = self.grad_LR.detach()[0].float().cpu()
|
||
|
|
if need_HR:
|
||
|
|
out_dict['HR'] = self.var_H.detach()[0].float().cpu()
|
||
|
|
return out_dict
|
||
|
|
|
||
|
|
def print_network(self):
|
||
|
|
# Generator
|
||
|
|
s, n = self.get_network_description(self.netG)
|
||
|
|
if isinstance(self.netG, nn.DataParallel):
|
||
|
|
net_struc_str = '{} - {}'.format(self.netG.__class__.__name__,
|
||
|
|
self.netG.module.__class__.__name__)
|
||
|
|
else:
|
||
|
|
net_struc_str = '{}'.format(self.netG.__class__.__name__)
|
||
|
|
|
||
|
|
logger.info('Network G structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
|
||
|
|
logger.info(s)
|
||
|
|
if self.is_train:
|
||
|
|
# Disriminator
|
||
|
|
s, n = self.get_network_description(self.netD)
|
||
|
|
if isinstance(self.netD, nn.DataParallel):
|
||
|
|
net_struc_str = '{} - {}'.format(self.netD.__class__.__name__,
|
||
|
|
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)
|
||
|
|
|
||
|
|
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)
|