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DeepRFT_MIMO.py
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from layers import *
class EBlock(nn.Module):
def __init__(self, out_channel, num_res=8, ResBlock=ResBlock):
super(EBlock, self).__init__()
layers = [ResBlock(out_channel) for _ in range(num_res)]
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
class DBlock(nn.Module):
def __init__(self, channel, num_res=8, ResBlock=ResBlock):
super(DBlock, self).__init__()
layers = [ResBlock(channel) for _ in range(num_res)]
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
class AFF(nn.Module):
def __init__(self, in_channel, out_channel, BasicConv=BasicConv):
super(AFF, self).__init__()
self.conv = nn.Sequential(
BasicConv(in_channel, out_channel, kernel_size=1, stride=1, relu=True),
BasicConv(out_channel, out_channel, kernel_size=3, stride=1, relu=False)
)
def forward(self, x1, x2, x4):
x = torch.cat([x1, x2, x4], dim=1)
return self.conv(x)
class SCM(nn.Module):
def __init__(self, out_plane, BasicConv=BasicConv, inchannel=3):
super(SCM, self).__init__()
self.main = nn.Sequential(
BasicConv(inchannel, out_plane//4, kernel_size=3, stride=1, relu=True),
BasicConv(out_plane // 4, out_plane // 2, kernel_size=1, stride=1, relu=True),
BasicConv(out_plane // 2, out_plane // 2, kernel_size=3, stride=1, relu=True),
BasicConv(out_plane // 2, out_plane-inchannel, kernel_size=1, stride=1, relu=True)
)
self.conv = BasicConv(out_plane, out_plane, kernel_size=1, stride=1, relu=False)
def forward(self, x):
x = torch.cat([x, self.main(x)], dim=1)
return self.conv(x)
class FAM(nn.Module):
def __init__(self, channel, BasicConv=BasicConv):
super(FAM, self).__init__()
self.merge = BasicConv(channel, channel, kernel_size=3, stride=1, relu=False)
def forward(self, x1, x2):
x = x1 * x2
out = x1 + self.merge(x)
return out
class DeepRFT_Small(nn.Module):
def __init__(self, num_res=4, inference=False):
super(DeepRFT_Small, self).__init__()
self.inference = inference
if not inference:
BasicConv = BasicConv_do
ResBlock = ResBlock_do_fft_bench
else:
BasicConv = BasicConv_do_eval
ResBlock = ResBlock_do_fft_bench_eval
base_channel = 32
self.Encoder = nn.ModuleList([
EBlock(base_channel, num_res, ResBlock=ResBlock),
EBlock(base_channel*2, num_res, ResBlock=ResBlock),
EBlock(base_channel*4, num_res, ResBlock=ResBlock),
])
self.feat_extract = nn.ModuleList([
BasicConv(3, base_channel, kernel_size=3, relu=True, stride=1),
BasicConv(base_channel, base_channel*2, kernel_size=3, relu=True, stride=2),
BasicConv(base_channel*2, base_channel*4, kernel_size=3, relu=True, stride=2),
BasicConv(base_channel*4, base_channel*2, kernel_size=4, relu=True, stride=2, transpose=True),
BasicConv(base_channel*2, base_channel, kernel_size=4, relu=True, stride=2, transpose=True),
BasicConv(base_channel, 3, kernel_size=3, relu=False, stride=1)
])
self.Decoder = nn.ModuleList([
DBlock(base_channel * 4, num_res, ResBlock=ResBlock),
DBlock(base_channel * 2, num_res, ResBlock=ResBlock),
DBlock(base_channel, num_res, ResBlock=ResBlock)
])
self.Convs = nn.ModuleList([
BasicConv(base_channel * 4, base_channel * 2, kernel_size=1, relu=True, stride=1),
BasicConv(base_channel * 2, base_channel, kernel_size=1, relu=True, stride=1),
])
self.ConvsOut = nn.ModuleList(
[
BasicConv(base_channel * 4, 3, kernel_size=3, relu=False, stride=1),
BasicConv(base_channel * 2, 3, kernel_size=3, relu=False, stride=1),
]
)
self.AFFs = nn.ModuleList([
AFF(base_channel * 7, base_channel*1, BasicConv=BasicConv),
AFF(base_channel * 7, base_channel*2, BasicConv=BasicConv)
])
self.FAM1 = FAM(base_channel * 4, BasicConv=BasicConv)
self.SCM1 = SCM(base_channel * 4, BasicConv=BasicConv)
self.FAM2 = FAM(base_channel * 2, BasicConv=BasicConv)
self.SCM2 = SCM(base_channel * 2, BasicConv=BasicConv)
def forward(self, x):
x_2 = F.interpolate(x, scale_factor=0.5)
x_4 = F.interpolate(x_2, scale_factor=0.5)
z2 = self.SCM2(x_2)
z4 = self.SCM1(x_4)
outputs = list()
x_ = self.feat_extract[0](x)
res1 = self.Encoder[0](x_)
z = self.feat_extract[1](res1)
z = self.FAM2(z, z2)
res2 = self.Encoder[1](z)
z = self.feat_extract[2](res2)
z = self.FAM1(z, z4)
z = self.Encoder[2](z)
z12 = F.interpolate(res1, scale_factor=0.5)
z21 = F.interpolate(res2, scale_factor=2)
z42 = F.interpolate(z, scale_factor=2)
z41 = F.interpolate(z42, scale_factor=2)
res2 = self.AFFs[1](z12, res2, z42)
res1 = self.AFFs[0](res1, z21, z41)
z = self.Decoder[0](z)
z_ = self.ConvsOut[0](z)
z = self.feat_extract[3](z)
if not self.inference:
outputs.append(z_+x_4)
z = torch.cat([z, res2], dim=1)
z = self.Convs[0](z)
z = self.Decoder[1](z)
z_ = self.ConvsOut[1](z)
z = self.feat_extract[4](z)
if not self.inference:
outputs.append(z_+x_2)
z = torch.cat([z, res1], dim=1)
z = self.Convs[1](z)
z = self.Decoder[2](z)
z = self.feat_extract[5](z)
if not self.inference:
outputs.append(z + x)
return outputs[::-1]
else:
return z + x
class DeepRFT_flops(nn.Module):
def __init__(self, num_res=8, inference=True):
super(DeepRFT_flops, self).__init__()
self.inference = inference
ResBlock = ResBlock_fft_bench
base_channel = 32
self.Encoder = nn.ModuleList([
EBlock(base_channel, num_res, ResBlock=ResBlock),
EBlock(base_channel*2, num_res, ResBlock=ResBlock),
EBlock(base_channel*4, num_res, ResBlock=ResBlock),
])
self.feat_extract = nn.ModuleList([
BasicConv(3, base_channel, kernel_size=3, relu=True, stride=1),
BasicConv(base_channel, base_channel*2, kernel_size=3, relu=True, stride=2),
BasicConv(base_channel*2, base_channel*4, kernel_size=3, relu=True, stride=2),
BasicConv(base_channel*4, base_channel*2, kernel_size=4, relu=True, stride=2, transpose=True),
BasicConv(base_channel*2, base_channel, kernel_size=4, relu=True, stride=2, transpose=True),
BasicConv(base_channel, 3, kernel_size=3, relu=False, stride=1)
])
self.Decoder = nn.ModuleList([
DBlock(base_channel * 4, num_res, ResBlock=ResBlock),
DBlock(base_channel * 2, num_res, ResBlock=ResBlock),
DBlock(base_channel, num_res, ResBlock=ResBlock)
])
self.Convs = nn.ModuleList([
BasicConv(base_channel * 4, base_channel * 2, kernel_size=1, relu=True, stride=1),
BasicConv(base_channel * 2, base_channel, kernel_size=1, relu=True, stride=1),
])
self.ConvsOut = nn.ModuleList(
[
BasicConv(base_channel * 4, 3, kernel_size=3, relu=False, stride=1),
BasicConv(base_channel * 2, 3, kernel_size=3, relu=False, stride=1),
]
)
self.AFFs = nn.ModuleList([
AFF(base_channel * 7, base_channel*1, BasicConv=BasicConv),
AFF(base_channel * 7, base_channel*2, BasicConv=BasicConv)
])
self.FAM1 = FAM(base_channel * 4, BasicConv=BasicConv)
self.SCM1 = SCM(base_channel * 4, BasicConv=BasicConv)
self.FAM2 = FAM(base_channel * 2, BasicConv=BasicConv)
self.SCM2 = SCM(base_channel * 2, BasicConv=BasicConv)
def forward(self, x):
x_2 = F.interpolate(x, scale_factor=0.5)
x_4 = F.interpolate(x_2, scale_factor=0.5)
z2 = self.SCM2(x_2)
z4 = self.SCM1(x_4)
outputs = list()
x_ = self.feat_extract[0](x)
res1 = self.Encoder[0](x_)
z = self.feat_extract[1](res1)
z = self.FAM2(z, z2)
res2 = self.Encoder[1](z)
z = self.feat_extract[2](res2)
z = self.FAM1(z, z4)
z = self.Encoder[2](z)
z12 = F.interpolate(res1, scale_factor=0.5)
z21 = F.interpolate(res2, scale_factor=2)
z42 = F.interpolate(z, scale_factor=2)
z41 = F.interpolate(z42, scale_factor=2)
res2 = self.AFFs[1](z12, res2, z42)
res1 = self.AFFs[0](res1, z21, z41)
z = self.Decoder[0](z)
z_ = self.ConvsOut[0](z)
z = self.feat_extract[3](z)
if not self.inference:
outputs.append(z_+x_4)
z = torch.cat([z, res2], dim=1)
z = self.Convs[0](z)
z = self.Decoder[1](z)
z_ = self.ConvsOut[1](z)
z = self.feat_extract[4](z)
if not self.inference:
outputs.append(z_+x_2)
z = torch.cat([z, res1], dim=1)
z = self.Convs[1](z)
z = self.Decoder[2](z)
z = self.feat_extract[5](z)
if not self.inference:
outputs.append(z+x)
# print(outputs)
return outputs[::-1]
else:
return z+x
class DeepRFT(nn.Module):
def __init__(self, num_res=8, inference=False):
super(DeepRFT, self).__init__()
self.inference = inference
if not inference:
BasicConv = BasicConv_do
ResBlock = ResBlock_do_fft_bench
else:
BasicConv = BasicConv_do_eval
ResBlock = ResBlock_do_fft_bench_eval
base_channel = 32
self.Encoder = nn.ModuleList([
EBlock(base_channel, num_res, ResBlock=ResBlock),
EBlock(base_channel*2, num_res, ResBlock=ResBlock),
EBlock(base_channel*4, num_res, ResBlock=ResBlock),
])
self.feat_extract = nn.ModuleList([
BasicConv(3, base_channel, kernel_size=3, relu=True, stride=1),
BasicConv(base_channel, base_channel*2, kernel_size=3, relu=True, stride=2),
BasicConv(base_channel*2, base_channel*4, kernel_size=3, relu=True, stride=2),
BasicConv(base_channel*4, base_channel*2, kernel_size=4, relu=True, stride=2, transpose=True),
BasicConv(base_channel*2, base_channel, kernel_size=4, relu=True, stride=2, transpose=True),
BasicConv(base_channel, 3, kernel_size=3, relu=False, stride=1)
])
self.Decoder = nn.ModuleList([
DBlock(base_channel * 4, num_res, ResBlock=ResBlock),
DBlock(base_channel * 2, num_res, ResBlock=ResBlock),
DBlock(base_channel, num_res, ResBlock=ResBlock)
])
self.Convs = nn.ModuleList([
BasicConv(base_channel * 4, base_channel * 2, kernel_size=1, relu=True, stride=1),
BasicConv(base_channel * 2, base_channel, kernel_size=1, relu=True, stride=1),
])
self.ConvsOut = nn.ModuleList(
[
BasicConv(base_channel * 4, 3, kernel_size=3, relu=False, stride=1),
BasicConv(base_channel * 2, 3, kernel_size=3, relu=False, stride=1),
]
)
self.AFFs = nn.ModuleList([
AFF(base_channel * 7, base_channel*1, BasicConv=BasicConv),
AFF(base_channel * 7, base_channel*2, BasicConv=BasicConv)
])
self.FAM1 = FAM(base_channel * 4, BasicConv=BasicConv)
self.SCM1 = SCM(base_channel * 4, BasicConv=BasicConv)
self.FAM2 = FAM(base_channel * 2, BasicConv=BasicConv)
self.SCM2 = SCM(base_channel * 2, BasicConv=BasicConv)
def forward(self, x):
x_2 = F.interpolate(x, scale_factor=0.5)
x_4 = F.interpolate(x_2, scale_factor=0.5)
z2 = self.SCM2(x_2)
z4 = self.SCM1(x_4)
outputs = list()
x_ = self.feat_extract[0](x)
res1 = self.Encoder[0](x_)
z = self.feat_extract[1](res1)
z = self.FAM2(z, z2)
res2 = self.Encoder[1](z)
z = self.feat_extract[2](res2)
z = self.FAM1(z, z4)
z = self.Encoder[2](z)
z12 = F.interpolate(res1, scale_factor=0.5)
z21 = F.interpolate(res2, scale_factor=2)
z42 = F.interpolate(z, scale_factor=2)
z41 = F.interpolate(z42, scale_factor=2)
res2 = self.AFFs[1](z12, res2, z42)
res1 = self.AFFs[0](res1, z21, z41)
z = self.Decoder[0](z)
z_ = self.ConvsOut[0](z)
z = self.feat_extract[3](z)
if not self.inference:
outputs.append(z_+x_4)
z = torch.cat([z, res2], dim=1)
z = self.Convs[0](z)
z = self.Decoder[1](z)
z_ = self.ConvsOut[1](z)
z = self.feat_extract[4](z)
if not self.inference:
outputs.append(z_+x_2)
z = torch.cat([z, res1], dim=1)
z = self.Convs[1](z)
z = self.Decoder[2](z)
z = self.feat_extract[5](z)
if not self.inference:
outputs.append(z+x)
# print(outputs)
return outputs[::-1]
else:
return z+x
class DeepRFTPLUS(nn.Module):
def __init__(self, num_res=20, inference=False):
super(DeepRFTPLUS, self).__init__()
# ResBlock = ResBlock_fft_bench
self.inference = inference
if not inference:
BasicConv = BasicConv_do
ResBlock = ResBlock_do_fft_bench
else:
BasicConv = BasicConv_do_eval
ResBlock = ResBlock_do_fft_bench_eval
base_channel = 32
self.Encoder = nn.ModuleList([
EBlock(base_channel, num_res, ResBlock=ResBlock),
EBlock(base_channel*2, num_res, ResBlock=ResBlock),
EBlock(base_channel*4, num_res, ResBlock=ResBlock),
])
self.feat_extract = nn.ModuleList([
BasicConv(3, base_channel, kernel_size=3, relu=True, stride=1),
BasicConv(base_channel, base_channel*2, kernel_size=3, relu=True, stride=2),
BasicConv(base_channel*2, base_channel*4, kernel_size=3, relu=True, stride=2),
BasicConv(base_channel*4, base_channel*2, kernel_size=4, relu=True, stride=2, transpose=True),
BasicConv(base_channel*2, base_channel, kernel_size=4, relu=True, stride=2, transpose=True),
BasicConv(base_channel, 3, kernel_size=3, relu=False, stride=1)
])
self.Decoder = nn.ModuleList([
DBlock(base_channel * 4, num_res, ResBlock=ResBlock),
DBlock(base_channel * 2, num_res, ResBlock=ResBlock),
DBlock(base_channel, num_res, ResBlock=ResBlock)
])
self.Convs = nn.ModuleList([
BasicConv(base_channel * 4, base_channel * 2, kernel_size=1, relu=True, stride=1),
BasicConv(base_channel * 2, base_channel, kernel_size=1, relu=True, stride=1),
])
self.ConvsOut = nn.ModuleList(
[
BasicConv(base_channel * 4, 3, kernel_size=3, relu=False, stride=1),
BasicConv(base_channel * 2, 3, kernel_size=3, relu=False, stride=1),
]
)
self.AFFs = nn.ModuleList([
AFF(base_channel * 7, base_channel*1, BasicConv=BasicConv),
AFF(base_channel * 7, base_channel*2, BasicConv=BasicConv)
])
self.FAM1 = FAM(base_channel * 4, BasicConv=BasicConv)
self.SCM1 = SCM(base_channel * 4, BasicConv=BasicConv)
self.FAM2 = FAM(base_channel * 2, BasicConv=BasicConv)
self.SCM2 = SCM(base_channel * 2, BasicConv=BasicConv)
def forward(self, x):
x_2 = F.interpolate(x, scale_factor=0.5)
x_4 = F.interpolate(x_2, scale_factor=0.5)
z2 = self.SCM2(x_2)
z4 = self.SCM1(x_4)
outputs = list()
x_ = self.feat_extract[0](x)
res1 = self.Encoder[0](x_)
z = self.feat_extract[1](res1)
z = self.FAM2(z, z2)
res2 = self.Encoder[1](z)
z = self.feat_extract[2](res2)
z = self.FAM1(z, z4)
z = self.Encoder[2](z)
z12 = F.interpolate(res1, scale_factor=0.5)
z21 = F.interpolate(res2, scale_factor=2)
z42 = F.interpolate(z, scale_factor=2)
z41 = F.interpolate(z42, scale_factor=2)
res2 = self.AFFs[1](z12, res2, z42)
res1 = self.AFFs[0](res1, z21, z41)
z = self.Decoder[0](z)
z_ = self.ConvsOut[0](z)
z = self.feat_extract[3](z)
if not self.inference:
outputs.append(z_+x_4)
z = torch.cat([z, res2], dim=1)
z = self.Convs[0](z)
z = self.Decoder[1](z)
z_ = self.ConvsOut[1](z)
z = self.feat_extract[4](z)
if not self.inference:
outputs.append(z_+x_2)
z = torch.cat([z, res1], dim=1)
z = self.Convs[1](z)
z = self.Decoder[2](z)
z = self.feat_extract[5](z)
if not self.inference:
outputs.append(z+x)
# print(outputs)
return outputs[::-1]
else:
return z+x