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import math
import torch.nn as nn
import models.basicblock as B
"""
# --------------------------------------------
# SR network with Residual in Residual Dense Block (RRDB)
# "ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks"
# --------------------------------------------
"""
class RRDB(nn.Module):
"""
gc: number of growth channels
nb: number of RRDB
"""
def __init__(self, in_nc=3, out_nc=3, nc=64, nb=23, gc=32, upscale=4, act_mode='L', upsample_mode='upconv'):
super(RRDB, self).__init__()
assert 'R' in act_mode or 'L' in act_mode, 'Examples of activation function: R, L, BR, BL, IR, IL'
n_upscale = int(math.log(upscale, 2))
if upscale == 3:
n_upscale = 1
m_head = B.conv(in_nc, nc, mode='C')
m_body = [B.RRDB(nc, gc=32, mode='C'+act_mode) for _ in range(nb)]
m_body.append(B.conv(nc, nc, mode='C'))
if upsample_mode == 'upconv':
upsample_block = B.upsample_upconv
elif upsample_mode == 'pixelshuffle':
upsample_block = B.upsample_pixelshuffle
elif upsample_mode == 'convtranspose':
upsample_block = B.upsample_convtranspose
else:
raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
if upscale == 3:
m_uper = upsample_block(nc, nc, mode='3'+act_mode)
else:
m_uper = [upsample_block(nc, nc, mode='2'+act_mode) for _ in range(n_upscale)]
H_conv0 = B.conv(nc, nc, mode='C'+act_mode)
H_conv1 = B.conv(nc, out_nc, mode='C')
m_tail = B.sequential(H_conv0, H_conv1)
self.model = B.sequential(m_head, B.ShortcutBlock(B.sequential(*m_body)), *m_uper, m_tail)
def forward(self, x):
x = self.model(x)
return x
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