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import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
import numpy as np
def get_activation(activation_func):
act_func = {
"relu":nn.ReLU(),
"sigmoid":nn.Sigmoid(),
"prelu":nn.PReLU(num_parameters=1),
"leaky_relu": nn.LeakyReLU(negative_slope=0.2, inplace=False),
"gelu":nn.GELU()
}
if activation_func is None:
return nn.Identity()
if activation_func not in act_func.keys():
raise ValueError("activation function({}) is not found".format(activation_func))
activation = act_func[activation_func]
return activation
def get_layer_info(out_channels, activation_func='relu'):
#act_func = {"relu":nn.ReLU(), "sigmoid":nn.Sigmoid(), "prelu":nn.PReLU(num_parameters=out_channels)}
# norm_layer = nn.BatchNorm2d(out_channels, momentum=0.9)
if out_channels >= 32:
groups = 32
else:
groups = 1
norm_layer = nn.GroupNorm(groups, out_channels)
activation = get_activation(activation_func)
return norm_layer, activation
class Conv(nn.Module):
""" (convolution => [BN] => ReLU) """
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
bias=True,
activation='leaky',
style=False,
resnet=True):
super().__init__()
self.style = style
norm_layer, act_func = get_layer_info(in_channels, activation)
if resnet and in_channels == out_channels:
self.resnet = True
else:
self.resnet = False
if style:
self.styleconv = Conv2DMod(in_channels, out_channels, kernel_size)
self.relu = nn.LeakyReLU(0.2, inplace=True)
else:
self.norm = norm_layer
self.conv = nn.Conv2d(in_channels, out_channels, stride=stride, kernel_size=kernel_size, padding=padding, bias=bias)
self.act = act_func
def forward(self, x, style_fea=None):
if self.style:
res = self.styleconv(x, style_fea)
res = self.relu(res)
else:
h = self.conv(self.act(self.norm(x)))
if self.resnet:
res = h + x
else:
res = h
return res
class Conv2DMod(nn.Module):
def __init__(self, in_chan, out_chan, kernel, demod=True, stride=1, dilation=1, eps=1e-8, **kwargs):
super().__init__()
self.filters = out_chan
self.demod = demod
self.kernel = kernel
self.stride = stride
self.dilation = dilation
self.weight = nn.Parameter(torch.randn((out_chan, in_chan, kernel, kernel)))
self.eps = eps
nn.init.kaiming_normal_(self.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')
def _get_same_padding(self, size, kernel, dilation, stride):
return ((size - 1) * (stride - 1) + dilation * (kernel - 1)) // 2
def forward(self, x, y):
b, c, h, w = x.shape
w1 = y[:, None, :, None, None]
w2 = self.weight[None, :, :, :, :]
weights = w2 * (w1 + 1)
if self.demod:
d = torch.rsqrt((weights ** 2).sum(dim=(2, 3, 4), keepdim=True) + self.eps)
weights = weights * d
x = x.reshape(1, -1, h, w)
_, _, *ws = weights.shape
weights = weights.reshape(b * self.filters, *ws)
padding = self._get_same_padding(h, self.kernel, self.dilation, self.stride)
x = F.conv2d(x, weights, padding=padding, groups=b)
x = x.reshape(-1, self.filters, h, w)
return x
class Up(nn.Module):
""" Upscaling then conv """
def __init__(self, in_channels, out_channels, activation='relu', resnet=True):
super().__init__()
self.up_layer = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.up = Conv(in_channels, out_channels, activation=activation, resnet=resnet)
def forward(self, x):
x = self.up_layer(x)
return self.up(x)
class DConv(nn.Module):
""" Double Conv Layer
"""
def __init__(self, in_channels, out_channels, activation='relu', resnet=True):
super().__init__()
self.conv1 = Conv(in_channels, out_channels, activation=activation, resnet=resnet)
self.conv2 = Conv(out_channels, out_channels, activation=activation, resnet=resnet)
def forward(self, x):
return self.conv2(self.conv1(x))
class Encoder(nn.Module):
def __init__(self, in_channels=3, mid_act='leaky', resnet=True):
super(Encoder, self).__init__()
self.in_conv = Conv(in_channels, 32-in_channels, stride=1, activation=mid_act, resnet=resnet)
self.down_32_64 = Conv(32, 64, stride=2, activation=mid_act, resnet=resnet)
self.down_64_64_1 = Conv(64, 64, activation=mid_act, resnet=resnet)
self.down_64_128 = Conv(64, 128, stride=2, activation=mid_act, resnet=resnet)
self.down_128_128_1 = Conv(128, 128, activation=mid_act, resnet=resnet)
self.down_128_256 = Conv(128, 256, stride=2, activation=mid_act, resnet=resnet)
self.down_256_256_1 = Conv(256, 256, activation=mid_act, resnet=resnet)
self.down_256_512 = Conv(256, 512, stride=2, activation=mid_act, resnet=resnet)
self.down_512_512_1 = Conv(512, 512, activation=mid_act, resnet=resnet)
self.down_512_512_2 = Conv(512, 512, activation=mid_act, resnet=resnet)
self.down_512_512_3 = Conv(512, 512, activation=mid_act, resnet=resnet)
def forward(self, x):
x1 = self.in_conv(x) # 32 x 256 x 256
x1 = torch.cat((x, x1), dim=1)
x2 = self.down_32_64(x1)
x3 = self.down_64_64_1(x2)
x4 = self.down_64_128(x3)
x5 = self.down_128_128_1(x4)
x6 = self.down_128_256(x5)
x7 = self.down_256_256_1(x6)
x8 = self.down_256_512(x7)
x9 = self.down_512_512_1(x8)
x10 = self.down_512_512_2(x9)
x11 = self.down_512_512_3(x10)
return x11, x10, x9, x8, x7, x6, x5, x4, x3, x2, x1
class Decoder(nn.Module):
def __init__(self,
out_channels=3,
mid_act='relu',
out_act='sigmoid',
resnet = True):
super(Decoder, self).__init__()
input_channel = 512
fea_dim = 100
self.to_style1 = nn.Linear(in_features=fea_dim, out_features=input_channel)
self.up_16_16_1 = Conv(input_channel, 256, activation=mid_act, resnet=resnet)
self.up_16_16_2 = Conv(768, 512, activation=mid_act, resnet=resnet)
self.up_16_16_3 = Conv(1024, 512, activation=mid_act, resnet=resnet)
self.up_16_32 = Up(1024, 256, activation=mid_act, resnet=resnet)
self.up_32_32_1 = Conv(512, 256, activation=mid_act, resnet=resnet)
self.up_32_64 = Up(512, 128, activation=mid_act, resnet=resnet)
self.up_64_64_1 = Conv(256, 128, activation=mid_act, resnet=resnet)
self.up_64_128 = Up(256, 64, activation=mid_act, resnet=resnet)
self.up_128_128_1 = Conv(128, 64, activation=mid_act, resnet=resnet)
self.up_128_256 = Up(128, 32, activation=mid_act, resnet=resnet)
self.out_conv = Conv(64, out_channels, activation=mid_act)
self.out_act = get_activation(out_act)
def forward(self, x):
x11, x10, x9, x8, x7, x6, x5, x4, x3, x2, x1 = x
y = self.up_16_16_1(x11)
y = torch.cat((x10, y), dim=1)
y = self.up_16_16_2(y)
y = torch.cat((x9, y), dim=1)
y = self.up_16_16_3(y)
y = torch.cat((x8, y), dim=1)
y = self.up_16_32(y)
y = torch.cat((x7, y), dim=1)
y = self.up_32_32_1(y)
y = torch.cat((x6, y), dim=1)
y = self.up_32_64(y)
y = torch.cat((x5, y), dim=1)
y = self.up_64_64_1(y) # 128 x 64 x 64
y = torch.cat((x4, y), dim=1)
y = self.up_64_128(y)
y = torch.cat((x3, y), dim=1)
y = self.up_128_128_1(y) # 64 x 128 x 128
y = torch.cat((x2, y), dim=1)
y = self.up_128_256(y) # 32 x 256 x 256
y = torch.cat((x1, y), dim=1)
y = self.out_conv(y) # 3 x 256 x 256
y = self.out_act(y)
return y
class SSN_v1(nn.Module):
""" Implementation of Relighting Net """
def __init__(self,
in_channels=3,
out_channels=3,
mid_act='leaky',
out_act='sigmoid',
resnet=True):
super(SSN_v1, self).__init__()
self.encoder = Encoder(in_channels, mid_act=mid_act, resnet=resnet)
self.decoder = Decoder(out_channels, mid_act=mid_act, out_act=out_act, resnet=resnet)
def forward(self, x, softness):
"""
Input is (source image, target light, source light, )
Output is: predicted new image, predicted source light, self-supervision image
"""
latent = self.encoder(x)
pred = self.decoder(latent)
return pred
if __name__ == '__main__':
test_input = torch.randn(5, 1, 256, 256)
style = torch.randn(5, 100)
model = SSN_v1(1, 1, mid_act='gelu', out_act='gelu', resnet=True)
test_out = model(test_input, style)
print('Ouptut shape: ', test_out.shape)
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