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 glob
import random
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)
if 'network_C' in opt.keys():
self.netC = networks.define_G(opt, net_key='network_C').to(self.device)
# The corruptor net is fixed. Lock 'her down.
self.netC.eval()
for p in self.netC.parameters():
p.requires_grad = True
else:
self.netC = None
# 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
self.G_warmup = train_opt['G_warmup'] if train_opt['G_warmup'] else 0
self.D_noise_theta = train_opt['D_noise_theta_init'] if train_opt['D_noise_theta_init'] else 0
self.D_noise_final = train_opt['D_noise_final_it'] if train_opt['D_noise_final_it'] else 0
self.D_noise_theta_floor = train_opt['D_noise_theta_floor'] if train_opt['D_noise_theta_floor'] else 0
self.corruptor_swapout_steps = train_opt['corruptor_swapout_steps'] if train_opt['corruptor_swapout_steps'] else 500
self.corruptor_usage_prob = train_opt['corruptor_usage_probability'] if train_opt['corruptor_usage_probability'] else .5
# 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'],
force_lr=train_opt['force_lr']))
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()
# Swapout params
self.swapout_G_freq = train_opt['swapout_G_freq'] if train_opt['swapout_G_freq'] else 0
self.swapout_G_duration = 0
self.swapout_D_freq = train_opt['swapout_D_freq'] if train_opt['swapout_D_freq'] else 0
self.swapout_D_duration = 0
self.swapout_duration = train_opt['swapout_duration'] if train_opt['swapout_duration'] else 0
self.print_network() # print network
self.load() # load G and D if needed
self.load_random_corruptor()
def feed_data(self, data, need_GT=True):
_profile = True
if _profile:
from time import time
_t = time()
# Corrupt the data with the given corruptor, if specified.
self.fed_LQ = data['LQ'].to(self.device)
if self.netC and random.random() < self.corruptor_usage_prob:
with torch.no_grad():
corrupted_L = self.netC(self.fed_LQ)[0].detach()
else:
corrupted_L = self.fed_LQ
self.var_L = torch.chunk(corrupted_L, chunks=self.mega_batch_factor, dim=0)
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):
_profile = False
if _profile:
from time import time
_t = time()
# Some generators have variants depending on the current step.
if hasattr(self.netG.module, "update_for_step"):
self.netG.module.update_for_step(step, os.path.join(self.opt['path']['models'], ".."))
# G
for p in self.netD.parameters():
p.requires_grad = False
if step > self.D_init_iters:
self.optimizer_G.zero_grad()
self.swapout_D(step)
self.swapout_G(step)
# 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
# Calculate a standard deviation for the gaussian noise to be applied to the discriminator, termed noise-theta.
if self.D_noise_final == 0:
noise_theta = 0
else:
noise_theta = (self.D_noise_theta - self.D_noise_theta_floor) * (self.D_noise_final - min(step, self.D_noise_final)) / self.D_noise_final + self.D_noise_theta_floor
if _profile:
print("Misc setup %f" % (time() - _t,))
_t = time()
self.fake_GenOut = []
var_ref_skips = []
for var_L, var_H, var_ref, pix in zip(self.var_L, self.var_H, self.var_ref, self.pix):
#from utils import gpu_mem_track
#import inspect
#gpu_tracker = gpu_mem_track.MemTracker(inspect.currentframe())
#gpu_tracker.track()
fake_GenOut = self.netG(var_L)
#gpu_tracker.track()
if _profile:
print("Gen forward %f" % (time() - _t,))
_t = time()
# 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]
# The following line detaches all generator outputs that are not None.
self.fake_GenOut.append(tuple([(x.detach() if x is not None else None) for x in list(fake_GenOut)]))
var_ref = (var_ref,) + self.create_artificial_skips(var_H)
else:
gen_img = fake_GenOut
self.fake_GenOut.append(fake_GenOut.detach())
var_ref_skips.append(var_ref)
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
if _profile:
print("Fea forward %f" % (time() - _t,))
_t = time()
# 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.l_gan_w > 0:
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()
if _profile:
print("Gen backward %f" % (time() - _t,))
_t = time()
self.optimizer_G.step()
if _profile:
print("Gen step %f" % (time() - _t,))
_t = time()
# D
if self.l_gan_w > 0 and step > self.G_warmup:
for p in self.netD.parameters():
p.requires_grad = True
noise = torch.randn_like(var_ref[0]) * noise_theta
noise.to(self.device)
self.optimizer_D.zero_grad()
for var_L, var_H, var_ref, pix in zip(self.var_L, self.var_H, var_ref_skips, self.pix):
# Re-compute generator outputs (post-update).
with torch.no_grad():
fake_H = self.netG(var_L)
# The following line detaches all generator outputs that are not None.
fake_H = tuple([(x.detach() if x is not None else None) for x in list(fake_H)])
if _profile:
print("Gen forward for disc %f" % (time() - _t,))
_t = time()
# Apply noise to the inputs to slow discriminator convergence.
var_ref = (var_ref[0] + noise,) + var_ref[1:]
fake_H = (fake_H[0] + noise,) + fake_H[1:]
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)
if _profile:
print("Double disc forward (RAGAN) %f" % (time() - _t,))
_t = time()
l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True) * 0.5 / self.mega_batch_factor
with amp.scale_loss(l_d_real, self.optimizer_D, loss_id=2) as l_d_real_scaled:
l_d_real_scaled.backward()
if _profile:
print("Disc backward 1 (RAGAN) %f" % (time() - _t,))
_t = time()
pred_d_fake = self.netD(fake_H)
l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real.detach()), False) * 0.5 / self.mega_batch_factor
with amp.scale_loss(l_d_fake, self.optimizer_D, loss_id=1) as l_d_fake_scaled:
l_d_fake_scaled.backward()
if _profile:
print("Disc forward/backward 2 (RAGAN) %f" % (time() - _t,))
_t = time()
self.optimizer_D.step()
if _profile:
print("Disc step %f" % (time() - _t,))
_t = time()
# Log sample images from first microbatch.
if step % 50 == 0:
sample_save_path = os.path.join(self.opt['path']['models'], "..", "temp")
os.makedirs(os.path.join(sample_save_path, "hr"), exist_ok=True)
os.makedirs(os.path.join(sample_save_path, "lr"), exist_ok=True)
os.makedirs(os.path.join(sample_save_path, "lr_precorrupt"), exist_ok=True)
os.makedirs(os.path.join(sample_save_path, "gen"), exist_ok=True)
os.makedirs(os.path.join(sample_save_path, "pix"), exist_ok=True)
multi_gen = False
if isinstance(self.fake_GenOut[0], tuple):
os.makedirs(os.path.join(sample_save_path, "genlr"), exist_ok=True)
os.makedirs(os.path.join(sample_save_path, "genmr"), exist_ok=True)
os.makedirs(os.path.join(sample_save_path, "ref"), exist_ok=True)
multi_gen = True
# fed_LQ is not chunked.
utils.save_image(self.fed_LQ.cpu().detach(), os.path.join(sample_save_path, "lr_precorrupt", "%05i.png" % (step,)))
for i in range(self.mega_batch_factor):
utils.save_image(self.var_H[i].cpu().detach(), os.path.join(sample_save_path, "hr", "%05i_%02i.png" % (step, i)))
utils.save_image(self.var_L[i].cpu().detach(), os.path.join(sample_save_path, "lr", "%05i_%02i.png" % (step, i)))
utils.save_image(self.pix[i].cpu().detach(), os.path.join(sample_save_path, "pix", "%05i_%02i.png" % (step, i)))
if multi_gen:
utils.save_image(self.fake_GenOut[i][0].cpu().detach(), os.path.join(sample_save_path, "gen", "%05i_%02i.png" % (step, i)))
if len(self.fake_GenOut[i]) > 1:
if self.fake_GenOut[i][1] is not None:
utils.save_image(self.fake_GenOut[i][1].cpu().detach(), os.path.join(sample_save_path, "genmr", "%05i_%02i.png" % (step, i)))
if self.fake_GenOut[i][2] is not None:
utils.save_image(self.fake_GenOut[i][2].cpu().detach(), os.path.join(sample_save_path, "genlr", "%05i_%02i.png" % (step, i)))
utils.save_image(var_ref_skips[i][0].cpu().detach(), os.path.join(sample_save_path, "ref", "hi_%05i_%02i.png" % (step, i)))
utils.save_image(var_ref_skips[i][1].cpu().detach(), os.path.join(sample_save_path, "ref", "med_%05i_%02i.png" % (step, i)))
utils.save_image(var_ref_skips[i][2].cpu().detach(), os.path.join(sample_save_path, "ref", "low_%05i_%02i.png" % (step, i)))
else:
utils.save_image(self.fake_GenOut[i].cpu().detach(), os.path.join(sample_save_path, "gen", "%05i_%02i.png" % (step, i)))
# Log metrics
if step % self.D_update_ratio == 0 and step > self.D_init_iters:
if self.cri_pix:
self.add_log_entry('l_g_pix', l_g_pix.item())
if self.cri_fea:
self.add_log_entry('feature_weight', self.l_fea_w)
self.add_log_entry('l_g_fea', l_g_fea.item())
if self.l_gan_w > 0:
self.add_log_entry('l_g_gan', l_g_gan.item())
self.add_log_entry('l_g_total', l_g_total.item() * self.mega_batch_factor)
if self.l_gan_w > 0 and step > self.G_warmup:
self.add_log_entry('l_d_real', l_d_real.item() * self.mega_batch_factor)
self.add_log_entry('l_d_fake', l_d_fake.item() * self.mega_batch_factor)
self.add_log_entry('D_fake', torch.mean(pred_d_fake.detach()))
self.add_log_entry('D_diff', torch.mean(pred_d_fake) - torch.mean(pred_d_real))
self.add_log_entry('noise_theta', noise_theta)
if step % self.corruptor_swapout_steps == 0 and step > 0:
self.load_random_corruptor()
# Allows the log to serve as an easy-to-use rotating buffer.
def add_log_entry(self, key, value):
key_it = "%s_it" % (key,)
log_rotating_buffer_size = 50
if key not in self.log_dict.keys():
self.log_dict[key] = []
self.log_dict[key_it] = 0
if len(self.log_dict[key]) < log_rotating_buffer_size:
self.log_dict[key].append(value)
else:
self.log_dict[key][self.log_dict[key_it] % log_rotating_buffer_size] = value
self.log_dict[key_it] += 1
def 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 pick_rand_prev_model(self, model_suffix):
previous_models = glob.glob(os.path.join(self.opt['path']['models'], "*_%s.pth" % (model_suffix,)))
if len(previous_models) <= 1:
return None
# Just a note: this intentionally includes the swap model in the list of possibilities.
return previous_models[random.randint(0, len(previous_models)-1)]
def compute_fea_loss(self, real, fake):
with torch.no_grad():
real = real.unsqueeze(dim=0)
fake = fake.unsqueeze(dim=0)
real_fea = self.netF(real).detach()
fake_fea = self.netF(fake)
return self.cri_fea(fake_fea, real_fea).item()
# Called before verification/checkpoint to ensure we're using the real models and not a swapout variant.
def force_restore_swapout(self):
if self.swapout_D_duration > 0:
logger.info("Swapping back to current D model: %s" % (self.stashed_D,))
self.load_network(self.stashed_D, self.netD, self.opt['path']['strict_load'])
self.stashed_D = None
self.swapout_D_duration = 0
if self.swapout_G_duration > 0:
logger.info("Swapping back to current G model: %s" % (self.stashed_G,))
self.load_network(self.stashed_G, self.netG, self.opt['path']['strict_load'])
self.stashed_G = None
self.swapout_G_duration = 0
def swapout_D(self, step):
if self.swapout_D_duration > 0:
self.swapout_D_duration -= 1
if self.swapout_D_duration == 0:
# Swap back.
logger.info("Swapping back to current D model: %s" % (self.stashed_D,))
self.load_network(self.stashed_D, self.netD, self.opt['path']['strict_load'])
self.stashed_D = None
elif self.swapout_D_freq != 0 and step % self.swapout_D_freq == 0:
swapped_model = self.pick_rand_prev_model('D')
if swapped_model is not None:
logger.info("Swapping to previous D model: %s" % (swapped_model,))
self.stashed_D = self.save_network(self.netD, 'D', 'swap_model')
self.load_network(swapped_model, self.netD, self.opt['path']['strict_load'])
self.swapout_D_duration = self.swapout_duration
def swapout_G(self, step):
if self.swapout_G_duration > 0:
self.swapout_G_duration -= 1
if self.swapout_G_duration == 0:
# Swap back.
logger.info("Swapping back to current G model: %s" % (self.stashed_G,))
self.load_network(self.stashed_G, self.netG, self.opt['path']['strict_load'])
self.stashed_G = None
elif self.swapout_G_freq != 0 and step % self.swapout_G_freq == 0:
swapped_model = self.pick_rand_prev_model('G')
if swapped_model is not None:
logger.info("Swapping to previous G model: %s" % (swapped_model,))
self.stashed_G = self.save_network(self.netG, 'G', 'swap_model')
self.load_network(swapped_model, self.netG, self.opt['path']['strict_load'])
self.swapout_G_duration = self.swapout_duration
def test(self):
self.netG.eval()
with torch.no_grad():
self.fake_GenOut = [self.netG(self.var_L[0])]
self.netG.train()
# Fetches a summary of the log.
def get_current_log(self, step):
return_log = {}
for k in self.log_dict.keys():
if not isinstance(self.log_dict[k], list):
continue
return_log[k] = sum(self.log_dict[k]) / len(self.log_dict[k])
# Some generators can do their own metric logging.
if hasattr(self.netG.module, "get_debug_values"):
return_log.update(self.netG.module.get_debug_values(step))
return return_log
def get_current_visuals(self, need_GT=True):
out_dict = OrderedDict()
out_dict['LQ'] = self.var_L[0].detach().float().cpu()
gen_batch = self.fake_GenOut[0]
if isinstance(gen_batch, tuple):
gen_batch = gen_batch[0]
out_dict['rlt'] = gen_batch.detach().float().cpu()
if need_GT:
out_dict['GT'] = self.var_H[0].detach().float().cpu()
return out_dict
def print_network(self):
# Generator
s, n = self.get_network_description(self.netG)
if isinstance(self.netG, nn.DataParallel) or isinstance(self.netG, DistributedDataParallel):
net_struc_str = '{} - {}'.format(self.netG.__class__.__name__,
self.netG.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netG.__class__.__name__)
if self.rank <= 0:
logger.info('Network G structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
logger.info(s)
if self.is_train:
# Discriminator
s, n = self.get_network_description(self.netD)
if isinstance(self.netD, nn.DataParallel) or isinstance(self.netD,
DistributedDataParallel):
net_struc_str = '{} - {}'.format(self.netD.__class__.__name__,
self.netD.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netD.__class__.__name__)
if self.rank <= 0:
logger.info('Network D structure: {}, with parameters: {:,d}'.format(
net_struc_str, n))
logger.info(s)
if self.cri_fea: # F, Perceptual Network
s, n = self.get_network_description(self.netF)
if isinstance(self.netF, nn.DataParallel) or isinstance(
self.netF, DistributedDataParallel):
net_struc_str = '{} - {}'.format(self.netF.__class__.__name__,
self.netF.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netF.__class__.__name__)
if self.rank <= 0:
logger.info('Network F structure: {}, with parameters: {:,d}'.format(
net_struc_str, n))
logger.info(s)
def load(self):
load_path_G = self.opt['path']['pretrain_model_G']
if load_path_G is not None:
logger.info('Loading model for G [{:s}] ...'.format(load_path_G))
self.load_network(load_path_G, self.netG, self.opt['path']['strict_load'])
load_path_D = self.opt['path']['pretrain_model_D']
if self.opt['is_train'] and load_path_D is not None:
logger.info('Loading model for D [{:s}] ...'.format(load_path_D))
self.load_network(load_path_D, self.netD, self.opt['path']['strict_load'])
def load_random_corruptor(self):
if self.netC is None:
return
corruptor_files = glob.glob(os.path.join(self.opt['path']['pretrained_corruptors_dir'], "*.pth"))
corruptor_to_load = corruptor_files[random.randint(0, len(corruptor_files)-1)]
logger.info('Swapping corruptor to: %s' % (corruptor_to_load,))
self.load_network(corruptor_to_load, self.netC, self.opt['path']['strict_load'])
def save(self, iter_step):
self.save_network(self.netG, 'G', iter_step)
self.save_network(self.netD, 'D', iter_step)