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from torch import nn
import Deraining.model.common as common
import torch.nn.functional as F
from Deraining.moco.builder import MoCo
def make_model(args):
return BlindSR(args)
class DA_conv(nn.Module):
def __init__(self, channels_in, channels_out, kernel_size, reduction):
super(DA_conv, self).__init__()
self.channels_out = channels_out
self.channels_in = channels_in
self.kernel_size = kernel_size
self.kernel = nn.Sequential(
nn.Linear(64, 64, bias=False),
nn.LeakyReLU(0.1, True),
nn.Linear(64, 64 * self.kernel_size * self.kernel_size, bias=False)
)
self.conv = common.default_conv(channels_in, channels_out, 1)
self.ca = CA_layer(channels_in, channels_out, reduction)
self.relu = nn.LeakyReLU(0.1, True)
def forward(self, x):
'''
:param x[0]: feature map: B * C * H * W
:param x[1]: degradation representation: B * C
'''
b, c, h, w = x[0].size()
# branch 1
kernel = self.kernel(x[1]).view(-1, 1, self.kernel_size, self.kernel_size)
out = self.relu(F.conv2d(x[0].view(1, -1, h, w), kernel, groups=b*c, padding=(self.kernel_size-1)//2))
out = self.conv(out.view(b, -1, h, w))
# branch 2
out = out + self.ca(x)
return out
class CA_layer(nn.Module):
def __init__(self, channels_in, channels_out, reduction):
super(CA_layer, self).__init__()
self.conv_du = nn.Sequential(
nn.Conv2d(channels_in, channels_in//reduction, 1, 1, 0, bias=False),
nn.LeakyReLU(0.1, True),
nn.Conv2d(channels_in // reduction, channels_out, 1, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, x):
'''
:param x[0]: feature map: B * C * H * W
:param x[1]: degradation representation: B * C
'''
att = self.conv_du(x[1][:, :, None, None])
return x[0] * att
class DAB(nn.Module):
def __init__(self, conv, n_feat, kernel_size, reduction):
super(DAB, self).__init__()
self.da_conv1 = DA_conv(n_feat, n_feat, kernel_size, reduction)
self.da_conv2 = DA_conv(n_feat, n_feat, kernel_size, reduction)
self.conv1 = conv(n_feat, n_feat, kernel_size)
self.conv2 = conv(n_feat, n_feat, kernel_size)
self.relu = nn.LeakyReLU(0.1, True)
def forward(self, x):
'''
:param x[0]: feature map: B * C * H * W
:param x[1]: degradation representation: B * C
'''
out = self.relu(self.da_conv1(x))
out = self.relu(self.conv1(out))
out = self.relu(self.da_conv2([out, x[1]]))
out = self.conv2(out) + x[0]
return out
class DAG(nn.Module):
def __init__(self, conv, n_feat, kernel_size, reduction, n_blocks):
super(DAG, self).__init__()
self.n_blocks = n_blocks
modules_body = [
DAB(conv, n_feat, kernel_size, reduction) \
for _ in range(n_blocks)
]
modules_body.append(conv(n_feat, n_feat, kernel_size))
self.body = nn.Sequential(*modules_body)
def forward(self, x):
'''
:param x[0]: feature map: B * C * H * W
:param x[1]: degradation representation: B * C
'''
res = x[0]
for i in range(self.n_blocks):
res = self.body[i]([res, x[1]])
res = self.body[-1](res)
res = res + x[0]
return res
class DASR(nn.Module):
def __init__(self, conv=common.default_conv):
super(DASR, self).__init__()
self.n_groups = 5
n_blocks = 5
n_feats = 64
kernel_size = 3
reduction = 8
scale = 1
# RGB mean for DIV2K
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
self.sub_mean = common.MeanShift(255.0, rgb_mean, rgb_std)
self.add_mean = common.MeanShift(255.0, rgb_mean, rgb_std, 1)
# head module
modules_head = [conv(3, n_feats, kernel_size)]
self.head = nn.Sequential(*modules_head)
# compress
self.compress = nn.Sequential(
nn.Linear(256, 64, bias=False),
nn.LeakyReLU(0.1, True)
)
# body
modules_body = [
DAG(common.default_conv, n_feats, kernel_size, reduction, n_blocks) \
for _ in range(self.n_groups)
]
modules_body.append(conv(n_feats, n_feats, kernel_size))
self.body = nn.Sequential(*modules_body)
# tail
modules_tail = [common.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, 3, kernel_size)]
self.tail = nn.Sequential(*modules_tail)
def forward(self, x, k_v):
k_v = self.compress(k_v)
# sub mean
x = self.sub_mean(x)
# head 3-64
x = self.head(x)
# body
res = x
# 0-5
for i in range(self.n_groups):
res = self.body[i]([res, k_v])
res = self.body[-1](res)
res = res + x
# tail
x = self.tail(res)
# add mean
x = self.add_mean(x)
return x
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.E = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.1, True),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.1, True),
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.1, True),
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.1, True),
nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.1, True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.1, True),
nn.AdaptiveAvgPool2d(1),
)
self.mlp = nn.Sequential(
nn.Linear(256, 256),
nn.LeakyReLU(0.1, True),
nn.Linear(256, 256),
)
def forward(self, x):
# print(x.shape)
fea = self.E(x).squeeze(-1).squeeze(-1)
out = self.mlp(fea)
return fea, out
class BlindSR(nn.Module):
def __init__(self):
super(BlindSR, self).__init__()
self.E = MoCo(base_encoder=Encoder)
def forward(self, x, y):
if self.training:
x_query = x
x_key = y
fea, logits, labels = self.E(x_query, x_key)
return fea, logits, labels
else:
# degradation-aware represenetion learning
fea = self.E(x, x)
return fea
class Super(nn.Module):
def __init__(self):
super(Super, self).__init__()
self.G = DASR()
def forward(self, x, fea):
# if self.training:
sr = self.G(x, fea)
return sr |