import logging
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn.parallel import DataParallel, DistributedDataParallel
import models.networks as networks
import models.lr_scheduler as lr_scheduler
from models.base_model import BaseModel
from models.loss import GANLoss
from apex import amp
import torch.nn.functional as F
import torchvision.utils as utils
import os
logger = logging.getLogger('base')
class SRGANModel(BaseModel):
def __init__(self, opt):
super(SRGANModel, self).__init__(opt)
if opt['dist']:
self.rank = torch.distributed.get_rank()
else:
self.rank = -1 # non dist training
train_opt = opt['train']
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device)
if self.is_train:
self.netD = networks.define_D(opt).to(self.device)
# define losses, optimizer and scheduler
if self.is_train:
self.mega_batch_factor = train_opt['mega_batch_factor']
if self.mega_batch_factor is None:
self.mega_batch_factor = 1
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
self.l_fea_w_decay = train_opt['feature_weight_decay']
self.l_fea_w_decay_steps = train_opt['feature_weight_decay_steps']
self.l_fea_w_minimum = train_opt['feature_weight_minimum']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt, use_bn=False).to(self.device)
if opt['dist']:
pass # do not need to use DistributedDataParallel for netF
else:
self.netF = DataParallel(self.netF)
# GD gan loss
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
# D_update_ratio and D_init_iters
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt['D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt['D_init_iters'] else 0
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
if self.rank <= 0:
logger.warning('Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'],
weight_decay=wd_G,
betas=(train_opt['beta1_G'], train_opt['beta2_G']))
self.optimizers.append(self.optimizer_G)
# D
wd_D = train_opt['weight_decay_D'] if train_opt['weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'],
weight_decay=wd_D,
betas=(train_opt['beta1_D'], train_opt['beta2_D']))
self.optimizers.append(self.optimizer_D)
# AMP
[self.netG, self.netD], [self.optimizer_G, self.optimizer_D] = \
amp.initialize([self.netG, self.netD], [self.optimizer_G, self.optimizer_D], opt_level=self.amp_level, num_losses=3)
# DataParallel
if opt['dist']:
self.netG = DistributedDataParallel(self.netG, device_ids=[torch.cuda.current_device()])
else:
self.netG = DataParallel(self.netG)
if self.is_train:
if opt['dist']:
self.netD = DistributedDataParallel(self.netD,
device_ids=[torch.cuda.current_device()])
else:
self.netD = DataParallel(self.netD)
self.netG.train()
self.netD.train()
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepLR_Restart(optimizer, train_opt['lr_steps'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights'],
gamma=train_opt['lr_gamma'],
clear_state=train_opt['clear_state']))
elif train_opt['lr_scheme'] == 'CosineAnnealingLR_Restart':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.CosineAnnealingLR_Restart(
optimizer, train_opt['T_period'], eta_min=train_opt['eta_min'],
restarts=train_opt['restarts'], weights=train_opt['restart_weights']))
else:
raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
self.print_network() # print network
self.load() # load G and D if needed
def feed_data(self, data, need_GT=True):
self.var_L = torch.chunk(data['LQ'], chunks=self.mega_batch_factor, dim=0) # LQ
if need_GT:
self.var_H = [t.to(self.device) for t in torch.chunk(data['GT'], chunks=self.mega_batch_factor, dim=0)]
input_ref = data['ref'] if 'ref' in data else data['GT']
self.var_ref = [t.to(self.device) for t in torch.chunk(input_ref, chunks=self.mega_batch_factor, dim=0)]
self.pix = [t.to(self.device) for t in torch.chunk(data['PIX'], chunks=self.mega_batch_factor, dim=0)]
def optimize_parameters(self, step):
# G
for p in self.netD.parameters():
p.requires_grad = False
if step > self.D_init_iters:
self.optimizer_G.zero_grad()
# Turning off G-grad is required to enable mega-batching and D_update_ratio to work together for some reason.
if step % self.D_update_ratio == 0 and step > self.D_init_iters:
for p in self.netG.parameters():
p.requires_grad = True
else:
for p in self.netG.parameters():
p.requires_grad = False
self.fake_GenOut = []
for var_L, var_H, var_ref, pix in zip(self.var_L, self.var_H, self.var_ref, self.pix):
fake_GenOut = self.netG(var_L)
# Extract the image output. For generators that output skip-through connections, the master output is always
# the first element of the tuple.
if isinstance(fake_GenOut, tuple):
gen_img = fake_GenOut[0]
# TODO: Fix this.
self.fake_GenOut.append((fake_GenOut[0].detach(),
fake_GenOut[1].detach(),
fake_GenOut[2].detach()))
else:
gen_img = fake_GenOut
self.fake_GenOut.append(fake_GenOut.detach())
l_g_total = 0
if step % self.D_update_ratio == 0 and step > self.D_init_iters:
if self.cri_pix: # pixel loss
l_g_pix = self.l_pix_w * self.cri_pix(gen_img, pix)
l_g_total += l_g_pix
if self.cri_fea: # feature loss
real_fea = self.netF(pix).detach()
fake_fea = self.netF(gen_img)
l_g_fea = self.l_fea_w * self.cri_fea(fake_fea, real_fea)
l_g_total += l_g_fea
# Decay the influence of the feature loss. As the model trains, the GAN will play a stronger role
# in the resultant image.
if step % self.l_fea_w_decay_steps == 0:
self.l_fea_w = max(self.l_fea_w_minimum, self.l_fea_w * self.l_fea_w_decay)
if self.opt['train']['gan_type'] == 'gan':
pred_g_fake = self.netD(fake_GenOut)
l_g_gan = self.l_gan_w * self.cri_gan(pred_g_fake, True)
elif self.opt['train']['gan_type'] == 'ragan':
pred_d_real = self.netD(var_ref).detach()
pred_g_fake = self.netD(fake_GenOut)
l_g_gan = self.l_gan_w * (
self.cri_gan(pred_d_real - torch.mean(pred_g_fake), False) +
self.cri_gan(pred_g_fake - torch.mean(pred_d_real), True)) / 2
l_g_total += l_g_gan
# Scale the loss down by the batch factor.
l_g_total = l_g_total / self.mega_batch_factor
with amp.scale_loss(l_g_total, self.optimizer_G, loss_id=0) as l_g_total_scaled:
l_g_total_scaled.backward()
self.optimizer_G.step()
# D
for p in self.netD.parameters():
p.requires_grad = True
# Convert var_ref to have the same output format as the generator. This generally means interpolating the
# HR images to have the same output dimensions as each generator skip connection.
if isinstance(self.fake_GenOut[0], tuple):
var_ref_skips = []
for ref, hi_res in zip(self.var_ref, self.var_H):
var_ref_skips.append((ref,) + self.create_artificial_skips(hi_res))
else:
var_ref_skips = self.var_ref
self.optimizer_D.zero_grad()
for var_L, var_H, var_ref, pix, fake_H in zip(self.var_L, self.var_H, var_ref_skips, self.pix, self.fake_GenOut):
if self.opt['train']['gan_type'] == 'gan':
# need to forward and backward separately, since batch norm statistics differ
# real
pred_d_real = self.netD(var_ref)
l_d_real = self.cri_gan(pred_d_real, True) / self.mega_batch_factor
with amp.scale_loss(l_d_real, self.optimizer_D, loss_id=2) as l_d_real_scaled:
l_d_real_scaled.backward()
# fake
pred_d_fake = self.netD(fake_H)
l_d_fake = self.cri_gan(pred_d_fake, False) / self.mega_batch_factor
with amp.scale_loss(l_d_fake, self.optimizer_D, loss_id=1) as l_d_fake_scaled:
l_d_fake_scaled.backward()
elif self.opt['train']['gan_type'] == 'ragan':
# pred_d_real = self.netD(var_ref)
# pred_d_fake = self.netD(fake_H.detach()) # detach to avoid BP to G
# l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True)
# l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real), False)
# l_d_total = (l_d_real + l_d_fake) / 2
# l_d_total.backward()
pred_d_fake = self.netD(fake_H).detach()
pred_d_real = self.netD(var_ref)
l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True) * 0.5 / self.mega_batch_factor
with amp.scale_loss(l_d_real, self.optimizer_D, loss_id=2) as l_d_real_scaled:
l_d_real_scaled.backward()
pred_d_fake = self.netD(fake_H)
l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real.detach()), False) * 0.5 / self.mega_batch_factor
with amp.scale_loss(l_d_fake, self.optimizer_D, loss_id=1) as l_d_fake_scaled:
l_d_fake_scaled.backward()
self.optimizer_D.step()
# Log sample images from first microbatch.
if step % 50 == 0:
os.makedirs("temp/hr", exist_ok=True)
os.makedirs("temp/lr", exist_ok=True)
os.makedirs("temp/gen", exist_ok=True)
os.makedirs("temp/pix", exist_ok=True)
multi_gen = False
if isinstance(self.fake_GenOut[0], tuple):
os.makedirs("temp/genlr", exist_ok=True)
os.makedirs("temp/genmr", exist_ok=True)
os.makedirs("temp/ref", exist_ok=True)
multi_gen = True
for i in range(self.mega_batch_factor):
utils.save_image(self.var_H[i].cpu().detach(), os.path.join("temp/hr", "%05i_%02i.png" % (step, i)))
utils.save_image(self.var_L[i].cpu().detach(), os.path.join("temp/lr", "%05i_%02i.png" % (step, i)))
utils.save_image(self.pix[i].cpu().detach(), os.path.join("temp/pix", "%05i_%02i.png" % (step, i)))
if multi_gen:
utils.save_image(self.fake_GenOut[i][0].cpu().detach(), os.path.join("temp/gen", "%05i_%02i.png" % (step, i)))
utils.save_image(self.fake_GenOut[i][1].cpu().detach(), os.path.join("temp/genmr", "%05i_%02i.png" % (step, i)))
utils.save_image(self.fake_GenOut[i][2].cpu().detach(), os.path.join("temp/genlr", "%05i_%02i.png" % (step, i)))
utils.save_image(var_ref_skips[i][1].cpu().detach(), os.path.join("temp/ref", "med_%05i_%02i.png" % (step, i)))
utils.save_image(var_ref_skips[i][2].cpu().detach(), os.path.join("temp/ref", "low_%05i_%02i.png" % (step, i)))
else:
utils.save_image(self.fake_GenOut[i].cpu().detach(), os.path.join("temp/gen", "%05i_%02i.png" % (step, i)))
# set log TODO(handle mega-batches?)
if step % self.D_update_ratio == 0 and step > self.D_init_iters:
if self.cri_pix:
self.log_dict['l_g_pix'] = l_g_pix.item()
if self.cri_fea:
self.log_dict['feature_weight'] = self.l_fea_w
self.log_dict['l_g_fea'] = l_g_fea.item()
self.log_dict['l_g_gan'] = l_g_gan.item()
self.log_dict['l_g_total'] = l_g_total.item() * self.mega_batch_factor
self.log_dict['l_d_real'] = l_d_real.item() * self.mega_batch_factor
self.log_dict['l_d_fake'] = l_d_fake.item() * self.mega_batch_factor
self.log_dict['D_fake'] = torch.mean(pred_d_fake.detach())
def create_artificial_skips(self, truth_img):
med_skip = F.interpolate(truth_img, scale_factor=.5)
lo_skip = F.interpolate(truth_img, scale_factor=.25)
return med_skip, lo_skip
def test(self):
self.netG.eval()
with torch.no_grad():
self.fake_GenOut = [self.netG(self.var_L[0])]
self.netG.train()
def get_current_log(self):
return self.log_dict
def get_current_visuals(self, need_GT=True):
out_dict = OrderedDict()
out_dict['LQ'] = self.var_L[0].detach()[0].float().cpu()
gen_batch = self.fake_GenOut[0]
if isinstance(gen_batch, tuple):
gen_batch = gen_batch[0]
out_dict['rlt'] = gen_batch.detach()[0].float().cpu()
if need_GT:
out_dict['GT'] = self.var_H[0].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) or isinstance(self.netG, DistributedDataParallel):
net_struc_str = '{} - {}'.format(self.netG.__class__.__name__,
self.netG.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netG.__class__.__name__)
if self.rank <= 0:
logger.info('Network G structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
logger.info(s)
if self.is_train:
# Discriminator
s, n = self.get_network_description(self.netD)
if isinstance(self.netD, nn.DataParallel) or isinstance(self.netD,
DistributedDataParallel):
net_struc_str = '{} - {}'.format(self.netD.__class__.__name__,
self.netD.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netD.__class__.__name__)
if self.rank <= 0:
logger.info('Network D structure: {}, with parameters: {:,d}'.format(
net_struc_str, n))
logger.info(s)
if self.cri_fea: # F, Perceptual Network
s, n = self.get_network_description(self.netF)
if isinstance(self.netF, nn.DataParallel) or isinstance(
self.netF, DistributedDataParallel):
net_struc_str = '{} - {}'.format(self.netF.__class__.__name__,
self.netF.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netF.__class__.__name__)
if self.rank <= 0:
logger.info('Network F structure: {}, with parameters: {:,d}'.format(
net_struc_str, n))
logger.info(s)
def load(self):
load_path_G = self.opt['path']['pretrain_model_G']
if load_path_G is not None:
logger.info('Loading model for G [{:s}] ...'.format(load_path_G))
self.load_network(load_path_G, self.netG, self.opt['path']['strict_load'])
load_path_D = self.opt['path']['pretrain_model_D']
if self.opt['is_train'] and load_path_D is not None:
logger.info('Loading model for D [{:s}] ...'.format(load_path_D))
self.load_network(load_path_D, self.netD, self.opt['path']['strict_load'])
def save(self, iter_step):
self.save_network(self.netG, 'G', iter_step)
self.save_network(self.netD, 'D', iter_step)