forked from mrq/DL-Art-School
44b89330c2
This is a checkpoint of a set of long tests with reduced-complexity networks. Some takeaways:
1) A full GAN using the resnet discriminator does appear to converge, but the quality is capped.
2) Likewise, a combination GAN/feature loss does not converge. The feature loss is optimized but
the model appears unable to fight the discriminator, so the G-loss steadily increases.
Going forwards, I want to try some bigger models. In particular, I want to change the generator
to increase complexity and capacity. I also want to add skip connections between the
disc and generator.
324 lines
16 KiB
Python
324 lines
16 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 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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# 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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# 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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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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self.print_network() # print network
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self.load() # load G and D if needed
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def feed_data(self, data, need_GT=True):
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self.var_L = torch.chunk(data['LQ'], chunks=self.mega_batch_factor, dim=0) # LQ
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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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# 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.fake_H = []
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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_H = self.netG(var_L)
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self.fake_H.append(fake_H.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(fake_H, pix)
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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(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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# 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.opt['train']['gan_type'] == 'gan':
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pred_g_fake = self.netD(fake_H)
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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_H)
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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_total += l_g_gan
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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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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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for var_L, var_H, var_ref, pix, fake_H in zip(self.var_L, self.var_H, self.var_ref, self.pix, self.fake_H):
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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)
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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.detach()) # detach to avoid BP to G
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l_d_fake = self.cri_gan(pred_d_fake, False)
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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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elif self.opt['train']['gan_type'] == 'ragan':
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# pred_d_real = self.netD(var_ref)
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# pred_d_fake = self.netD(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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# l_d_total.backward()
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pred_d_fake = self.netD(fake_H.detach()).detach()
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pred_d_real = self.netD(var_ref)
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l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True) * 0.5
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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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pred_d_fake = self.netD(fake_H.detach())
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l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real.detach()), False) * 0.5
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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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self.optimizer_D.step()
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# Log sample images from first microbatch.
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if step % 50 == 0:
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os.makedirs("temp/hr", exist_ok=True)
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os.makedirs("temp/lr", exist_ok=True)
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os.makedirs("temp/gen", exist_ok=True)
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os.makedirs("temp/pix", exist_ok=True)
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for i in range(self.var_L[0].shape[0]):
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utils.save_image(self.var_H[0][i].cpu().detach(), os.path.join("temp/hr", "%05i_%02i.png" % (step, i)))
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utils.save_image(self.var_L[0][i].cpu().detach(), os.path.join("temp/lr", "%05i_%02i.png" % (step, i)))
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utils.save_image(self.pix[0][i].cpu().detach(), os.path.join("temp/pix", "%05i_%02i.png" % (step, i)))
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utils.save_image(self.fake_H[0][i].cpu().detach(), os.path.join("temp/gen", "%05i_%02i.png" % (step, i)))
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# set log TODO(handle mega-batches?)
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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:
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self.log_dict['l_g_pix'] = l_g_pix.item()
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if self.cri_fea:
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self.log_dict['feature_weight'] = self.l_fea_w
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self.log_dict['l_g_fea'] = l_g_fea.item()
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self.log_dict['l_g_gan'] = l_g_gan.item()
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self.log_dict['l_g_total'] = l_g_total.item()
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self.log_dict['l_d_real'] = l_d_real.item()
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self.log_dict['l_d_fake'] = l_d_fake.item()
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self.log_dict['D_fake'] = torch.mean(pred_d_fake.detach())
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def test(self):
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self.netG.eval()
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with torch.no_grad():
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self.fake_H = [self.netG(self.var_L[0])]
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self.netG.train()
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def get_current_log(self):
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return self.log_dict
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def get_current_visuals(self, need_GT=True):
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out_dict = OrderedDict()
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out_dict['LQ'] = self.var_L[0].detach()[0].float().cpu()
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out_dict['rlt'] = self.fake_H[0].detach()[0].float().cpu()
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if need_GT:
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out_dict['GT'] = self.var_H[0].detach()[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) or isinstance(self.netG, DistributedDataParallel):
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net_struc_str = '{} - {}'.format(self.netG.__class__.__name__,
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self.netG.module.__class__.__name__)
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else:
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net_struc_str = '{}'.format(self.netG.__class__.__name__)
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if self.rank <= 0:
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logger.info('Network G structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
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logger.info(s)
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if self.is_train:
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# Discriminator
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s, n = self.get_network_description(self.netD)
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if isinstance(self.netD, nn.DataParallel) or isinstance(self.netD,
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DistributedDataParallel):
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net_struc_str = '{} - {}'.format(self.netD.__class__.__name__,
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self.netD.module.__class__.__name__)
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else:
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net_struc_str = '{}'.format(self.netD.__class__.__name__)
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if self.rank <= 0:
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logger.info('Network D structure: {}, with parameters: {:,d}'.format(
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net_struc_str, n))
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logger.info(s)
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if self.cri_fea: # F, Perceptual Network
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s, n = self.get_network_description(self.netF)
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if isinstance(self.netF, nn.DataParallel) or isinstance(
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self.netF, DistributedDataParallel):
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net_struc_str = '{} - {}'.format(self.netF.__class__.__name__,
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self.netF.module.__class__.__name__)
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else:
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net_struc_str = '{}'.format(self.netF.__class__.__name__)
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if self.rank <= 0:
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logger.info('Network F structure: {}, with parameters: {:,d}'.format(
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net_struc_str, n))
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logger.info(s)
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def load(self):
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load_path_G = self.opt['path']['pretrain_model_G']
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if load_path_G is not None:
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logger.info('Loading model for G [{:s}] ...'.format(load_path_G))
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self.load_network(load_path_G, self.netG, self.opt['path']['strict_load'])
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load_path_D = self.opt['path']['pretrain_model_D']
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if self.opt['is_train'] and load_path_D is not None:
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logger.info('Loading model for D [{:s}] ...'.format(load_path_D))
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self.load_network(load_path_D, self.netD, self.opt['path']['strict_load'])
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def save(self, iter_step):
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self.save_network(self.netG, 'G', iter_step)
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self.save_network(self.netD, 'D', iter_step)
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