DL-Art-School/codes/models/archs/srflow/RRDBNet_arch.py

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2020-11-07 03:38:04 +00:00
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
import models.archs.srflow.module_util as mutil
from utils.util import opt_get
class ResidualDenseBlock_5C(nn.Module):
def __init__(self, nf=64, gc=32, bias=True):
super(ResidualDenseBlock_5C, self).__init__()
# gc: growth channel, i.e. intermediate channels
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
# initialization
mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
def forward(self, x):
x1 = self.lrelu(self.conv1(x))
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
return x5 * 0.2 + x
class RRDB(nn.Module):
'''Residual in Residual Dense Block'''
def __init__(self, nf, gc=32):
super(RRDB, self).__init__()
self.RDB1 = ResidualDenseBlock_5C(nf, gc)
self.RDB2 = ResidualDenseBlock_5C(nf, gc)
self.RDB3 = ResidualDenseBlock_5C(nf, gc)
def forward(self, x):
out = self.RDB1(x)
out = self.RDB2(out)
out = self.RDB3(out)
return out * 0.2 + x
class RRDBNet(nn.Module):
def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=4, opt=None):
self.opt = opt
super(RRDBNet, self).__init__()
RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
self.scale = scale
self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
self.RRDB_trunk = mutil.make_layer(RRDB_block_f, nb)
self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
#### upsampling
self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
if self.scale >= 8:
self.upconv3 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
if self.scale >= 16:
self.upconv4 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
if self.scale >= 32:
self.upconv5 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x, get_steps=False):
fea = self.conv_first(x)
block_idxs = opt_get(self.opt, ['network_G', 'flow', 'stackRRDB', 'blocks']) or []
block_results = {}
for idx, m in enumerate(self.RRDB_trunk.children()):
fea = m(fea)
for b in block_idxs:
if b == idx:
block_results["block_{}".format(idx)] = fea
trunk = self.trunk_conv(fea)
last_lr_fea = fea + trunk
fea_up2 = self.upconv1(F.interpolate(last_lr_fea, scale_factor=2, mode='nearest'))
fea = self.lrelu(fea_up2)
fea_up4 = self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest'))
fea = self.lrelu(fea_up4)
fea_up8 = None
fea_up16 = None
fea_up32 = None
if self.scale >= 8:
fea_up8 = self.upconv3(F.interpolate(fea, scale_factor=2, mode='nearest'))
fea = self.lrelu(fea_up8)
if self.scale >= 16:
fea_up16 = self.upconv4(F.interpolate(fea, scale_factor=2, mode='nearest'))
fea = self.lrelu(fea_up16)
if self.scale >= 32:
fea_up32 = self.upconv5(F.interpolate(fea, scale_factor=2, mode='nearest'))
fea = self.lrelu(fea_up32)
out = self.conv_last(self.lrelu(self.HRconv(fea)))
results = {'last_lr_fea': last_lr_fea,
'fea_up1': last_lr_fea,
'fea_up2': fea_up2,
'fea_up4': fea_up4,
'fea_up8': fea_up8,
'fea_up16': fea_up16,
'fea_up32': fea_up32,
'out': out}
fea_up0_en = opt_get(self.opt, ['network_G', 'flow', 'fea_up0']) or False
if fea_up0_en:
results['fea_up0'] = F.interpolate(last_lr_fea, scale_factor=1/2, mode='bilinear', align_corners=False, recompute_scale_factor=True)
fea_upn1_en = opt_get(self.opt, ['network_G', 'flow', 'fea_up-1']) or False
if fea_upn1_en:
results['fea_up-1'] = F.interpolate(last_lr_fea, scale_factor=1/4, mode='bilinear', align_corners=False, recompute_scale_factor=True)
if get_steps:
for k, v in block_results.items():
results[k] = v
return results
else:
return out