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import functools
import torch
from torch.nn import init
"""
# --------------------------------------------
# select the network of G, D and F
# --------------------------------------------
"""
# --------------------------------------------
# Generator, netG, G
# --------------------------------------------
def define_G(opt):
opt_net = opt['netG']
net_type = opt_net['net_type']
# ----------------------------------------
# denoising task
# ----------------------------------------
# ----------------------------------------
# DnCNN
# ----------------------------------------
if net_type == 'dncnn':
from models.network_dncnn import DnCNN as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'], # total number of conv layers
act_mode=opt_net['act_mode'])
# ----------------------------------------
# Flexible DnCNN
# ----------------------------------------
elif net_type == 'fdncnn':
from models.network_dncnn import FDnCNN as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'], # total number of conv layers
act_mode=opt_net['act_mode'])
# ----------------------------------------
# FFDNet
# ----------------------------------------
elif net_type == 'ffdnet':
from models.network_ffdnet import FFDNet as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
act_mode=opt_net['act_mode'])
# ----------------------------------------
# others
# ----------------------------------------
# ----------------------------------------
# super-resolution task
# ----------------------------------------
# ----------------------------------------
# SRMD
# ----------------------------------------
elif net_type == 'srmd':
from models.network_srmd import SRMD as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
upscale=opt_net['scale'],
act_mode=opt_net['act_mode'],
upsample_mode=opt_net['upsample_mode'])
# ----------------------------------------
# super-resolver prior of DPSR
# ----------------------------------------
elif net_type == 'dpsr':
from models.network_dpsr import MSRResNet_prior as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
upscale=opt_net['scale'],
act_mode=opt_net['act_mode'],
upsample_mode=opt_net['upsample_mode'])
# ----------------------------------------
# modified SRResNet v0.0
# ----------------------------------------
elif net_type == 'msrresnet0':
from models.network_msrresnet import MSRResNet0 as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
upscale=opt_net['scale'],
act_mode=opt_net['act_mode'],
upsample_mode=opt_net['upsample_mode'])
# ----------------------------------------
# modified SRResNet v0.1
# ----------------------------------------
elif net_type == 'msrresnet1':
from models.network_msrresnet import MSRResNet1 as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
upscale=opt_net['scale'],
act_mode=opt_net['act_mode'],
upsample_mode=opt_net['upsample_mode'])
# ----------------------------------------
# RRDB
# ----------------------------------------
elif net_type == 'rrdb': # RRDB
from models.network_rrdb import RRDB as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
gc=opt_net['gc'],
upscale=opt_net['scale'],
act_mode=opt_net['act_mode'],
upsample_mode=opt_net['upsample_mode'])
# ----------------------------------------
# RRDBNet
# ----------------------------------------
elif net_type == 'rrdbnet': # RRDBNet
from models.network_rrdbnet import RRDBNet as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
gc=opt_net['gc'],
sf=opt_net['scale'])
# ----------------------------------------
# IMDB
# ----------------------------------------
elif net_type == 'imdn': # IMDB
from models.network_imdn import IMDN as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
upscale=opt_net['scale'],
act_mode=opt_net['act_mode'],
upsample_mode=opt_net['upsample_mode'])
# ----------------------------------------
# USRNet
# ----------------------------------------
elif net_type == 'usrnet': # USRNet
from models.network_usrnet import USRNet as net
netG = net(n_iter=opt_net['n_iter'],
h_nc=opt_net['h_nc'],
in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
act_mode=opt_net['act_mode'],
downsample_mode=opt_net['downsample_mode'],
upsample_mode=opt_net['upsample_mode']
)
# ----------------------------------------
# Deep Residual U-Net (drunet)
# ----------------------------------------
elif net_type == 'drunet':
from models.network_unet import UNetRes as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
act_mode=opt_net['act_mode'],
downsample_mode=opt_net['downsample_mode'],
upsample_mode=opt_net['upsample_mode'],
bias=opt_net['bias'])
# ----------------------------------------
# SwinIR
# ----------------------------------------
elif net_type == 'swinir':
from models.network_swinir import SwinIR as net
netG = net(upscale=opt_net['upscale'],
in_chans=opt_net['in_chans'],
img_size=opt_net['img_size'],
window_size=opt_net['window_size'],
img_range=opt_net['img_range'],
depths=opt_net['depths'],
embed_dim=opt_net['embed_dim'],
num_heads=opt_net['num_heads'],
mlp_ratio=opt_net['mlp_ratio'],
upsampler=opt_net['upsampler'],
resi_connection=opt_net['resi_connection'])
# ----------------------------------------
# VRT
# ----------------------------------------
elif net_type == 'vrt':
from models.network_vrt import VRT as net
netG = net(upscale=opt_net['upscale'],
img_size=opt_net['img_size'],
window_size=opt_net['window_size'],
depths=opt_net['depths'],
indep_reconsts=opt_net['indep_reconsts'],
embed_dims=opt_net['embed_dims'],
num_heads=opt_net['num_heads'],
spynet_path=opt_net['spynet_path'],
pa_frames=opt_net['pa_frames'],
deformable_groups=opt_net['deformable_groups'],
nonblind_denoising=opt_net['nonblind_denoising'],
use_checkpoint_attn=opt_net['use_checkpoint_attn'],
use_checkpoint_ffn=opt_net['use_checkpoint_ffn'],
no_checkpoint_attn_blocks=opt_net['no_checkpoint_attn_blocks'],
no_checkpoint_ffn_blocks=opt_net['no_checkpoint_ffn_blocks'])
# ----------------------------------------
# others
# ----------------------------------------
# TODO
else:
raise NotImplementedError('netG [{:s}] is not found.'.format(net_type))
# ----------------------------------------
# initialize weights
# ----------------------------------------
if opt['is_train']:
init_weights(netG,
init_type=opt_net['init_type'],
init_bn_type=opt_net['init_bn_type'],
gain=opt_net['init_gain'])
return netG
# --------------------------------------------
# Discriminator, netD, D
# --------------------------------------------
def define_D(opt):
opt_net = opt['netD']
net_type = opt_net['net_type']
# ----------------------------------------
# discriminator_vgg_96
# ----------------------------------------
if net_type == 'discriminator_vgg_96':
from models.network_discriminator import Discriminator_VGG_96 as discriminator
netD = discriminator(in_nc=opt_net['in_nc'],
base_nc=opt_net['base_nc'],
ac_type=opt_net['act_mode'])
# ----------------------------------------
# discriminator_vgg_128
# ----------------------------------------
elif net_type == 'discriminator_vgg_128':
from models.network_discriminator import Discriminator_VGG_128 as discriminator
netD = discriminator(in_nc=opt_net['in_nc'],
base_nc=opt_net['base_nc'],
ac_type=opt_net['act_mode'])
# ----------------------------------------
# discriminator_vgg_192
# ----------------------------------------
elif net_type == 'discriminator_vgg_192':
from models.network_discriminator import Discriminator_VGG_192 as discriminator
netD = discriminator(in_nc=opt_net['in_nc'],
base_nc=opt_net['base_nc'],
ac_type=opt_net['act_mode'])
# ----------------------------------------
# discriminator_vgg_128_SN
# ----------------------------------------
elif net_type == 'discriminator_vgg_128_SN':
from models.network_discriminator import Discriminator_VGG_128_SN as discriminator
netD = discriminator()
elif net_type == 'discriminator_patchgan':
from models.network_discriminator import Discriminator_PatchGAN as discriminator
netD = discriminator(input_nc=opt_net['in_nc'],
ndf=opt_net['base_nc'],
n_layers=opt_net['n_layers'],
norm_type=opt_net['norm_type'])
elif net_type == 'discriminator_unet':
from models.network_discriminator import Discriminator_UNet as discriminator
netD = discriminator(input_nc=opt_net['in_nc'],
ndf=opt_net['base_nc'])
else:
raise NotImplementedError('netD [{:s}] is not found.'.format(net_type))
# ----------------------------------------
# initialize weights
# ----------------------------------------
init_weights(netD,
init_type=opt_net['init_type'],
init_bn_type=opt_net['init_bn_type'],
gain=opt_net['init_gain'])
return netD
# --------------------------------------------
# VGGfeature, netF, F
# --------------------------------------------
def define_F(opt, use_bn=False):
device = torch.device('cuda' if opt['gpu_ids'] else 'cpu')
from models.network_feature import VGGFeatureExtractor
# pytorch pretrained VGG19-54, before ReLU.
if use_bn:
feature_layer = 49
else:
feature_layer = 34
netF = VGGFeatureExtractor(feature_layer=feature_layer,
use_bn=use_bn,
use_input_norm=True,
device=device)
netF.eval() # No need to train, but need BP to input
return netF
"""
# --------------------------------------------
# weights initialization
# --------------------------------------------
"""
def init_weights(net, init_type='xavier_uniform', init_bn_type='uniform', gain=1):
"""
# Kai Zhang, https://github.com/cszn/KAIR
#
# Args:
# init_type:
# default, none: pass init_weights
# normal; normal; xavier_normal; xavier_uniform;
# kaiming_normal; kaiming_uniform; orthogonal
# init_bn_type:
# uniform; constant
# gain:
# 0.2
"""
def init_fn(m, init_type='xavier_uniform', init_bn_type='uniform', gain=1):
classname = m.__class__.__name__
if classname.find('Conv') != -1 or classname.find('Linear') != -1:
if init_type == 'normal':
init.normal_(m.weight.data, 0, 0.1)
m.weight.data.clamp_(-1, 1).mul_(gain)
elif init_type == 'uniform':
init.uniform_(m.weight.data, -0.2, 0.2)
m.weight.data.mul_(gain)
elif init_type == 'xavier_normal':
init.xavier_normal_(m.weight.data, gain=gain)
m.weight.data.clamp_(-1, 1)
elif init_type == 'xavier_uniform':
init.xavier_uniform_(m.weight.data, gain=gain)
elif init_type == 'kaiming_normal':
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in', nonlinearity='relu')
m.weight.data.clamp_(-1, 1).mul_(gain)
elif init_type == 'kaiming_uniform':
init.kaiming_uniform_(m.weight.data, a=0, mode='fan_in', nonlinearity='relu')
m.weight.data.mul_(gain)
elif init_type == 'orthogonal':
init.orthogonal_(m.weight.data, gain=gain)
else:
raise NotImplementedError('Initialization method [{:s}] is not implemented'.format(init_type))
if m.bias is not None:
m.bias.data.zero_()
elif classname.find('BatchNorm2d') != -1:
if init_bn_type == 'uniform': # preferred
if m.affine:
init.uniform_(m.weight.data, 0.1, 1.0)
init.constant_(m.bias.data, 0.0)
elif init_bn_type == 'constant':
if m.affine:
init.constant_(m.weight.data, 1.0)
init.constant_(m.bias.data, 0.0)
else:
raise NotImplementedError('Initialization method [{:s}] is not implemented'.format(init_bn_type))
if init_type not in ['default', 'none']:
print('Initialization method [{:s} + {:s}], gain is [{:.2f}]'.format(init_type, init_bn_type, gain))
fn = functools.partial(init_fn, init_type=init_type, init_bn_type=init_bn_type, gain=gain)
net.apply(fn)
else:
print('Pass this initialization! Initialization was done during network definition!')
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