def __init__(self, ngpu=1):
super(LowToHighGenerator, self).__init__()
self.ngpu = ngpu
res_units = [256, 128, 96]
inp_res_units = [
[256, 256, 256, 256, 256, 256, 256, 256, 256, 256, 256, 256, 256, 256, 256, 256, 256, 256, 256, 256, 256,
256], [256, 128, 128], [128, 96, 96]]
self.layers_set = []
self.layers_set_up = []
self.layers_set_final = nn.ModuleList()
self.layers_set_final_up = nn.ModuleList()
self.a1 = nn.Sequential(nn.Conv2d(256, 128, 1, 1))
self.a2 = nn.Sequential(nn.Conv2d(128, 96, 1, 1))
self.layers_in = conv3x3(3, 256)
layers = []
for ru in range(len(res_units) - 1):
nunits = res_units[ru]
curr_inp_resu = inp_res_units[ru]
self.layers_set.insert(ru, [])
self.layers_set_up.insert(ru, [])
if ru == 0:
num_blocks_level = 12
else:
num_blocks_level = 3
for j in range(num_blocks_level):
# if curr_inp_resu[j]==3:
self.layers_set[ru].append(BasicBlock(curr_inp_resu[j], nunits))
# else:
# layers.append(MyBlock(curr_inp_resu[j], nunits))
self.layers_set_up[ru].append(nn.Upsample(scale_factor=2, mode='bilinear',align_corners=True))
self.layers_set_up[ru].append(nn.BatchNorm2d(nunits))
self.layers_set_up[ru].append(nn.ReLU(True))
self.layers_set_up[ru].append(nn.ConvTranspose2d(nunits, nunits, kernel_size=1, stride=1))
self.layers_set_final.append(nn.Sequential(*self.layers_set[ru]))
self.layers_set_final_up.append(nn.Sequential(*self.layers_set_up[ru]))
nunits = res_units[-1]
layers.append(conv3x3(inp_res_units[-1][0], nunits))
layers.append(nn.ReLU(True))
layers.append(nn.Conv2d(inp_res_units[-1][1], nunits, kernel_size=1, stride=1))
layers.append(nn.ReLU(True))
layers.append(nn.Conv2d(nunits, 3, kernel_size=1, stride=1))
layers.append(nn.Tanh())
self.main = nn.Sequential(*layers)
def __init__(self, ngpu=1):
super(HighToLowDiscriminator, self).__init__()
self.ngpu = ngpu
res_units = [256, 128, 96]
inp_res_units = [[256, 256, 256, 256], [256, 128], [128, 96]]
self.layers_set = []
self.layers_set_down = []
self.layers_set_final = nn.ModuleList()
self.layers_set_final_down = nn.ModuleList()
self.layers_in = conv3x3(3, 256)
layers = []
for ru in range(len(res_units) - 1):
nunits = res_units[ru]
curr_inp_resu = inp_res_units[ru]
self.layers_set.insert(ru, [])
self.layers_set_down.insert(ru, [])
if ru == 0:
num_blocks_level = 4
else:
num_blocks_level = 1
for j in range(num_blocks_level):
self.layers_set[ru].append(
BasicBlock(curr_inp_resu[j], nunits, nobn=True))
self.layers_set_down[ru].append(nn.MaxPool2d(2, 2))
self.layers_set_down[ru].append(nn.ReLU(True))
self.layers_set_down[ru].append(
nn.ConvTranspose2d(nunits, nunits, kernel_size=1, stride=1))
self.layers_set_final.append(nn.Sequential(*self.layers_set[ru]))
self.layers_set_final_down.append(
nn.Sequential(*self.layers_set_down[ru]))
nunits = res_units[-1]
layers.append(conv3x3(inp_res_units[-1][0], nunits))
layers.append(nn.ReLU(True))
layers.append(
nn.Conv2d(inp_res_units[-1][1], nunits, kernel_size=1, stride=1))
layers.append(nn.ReLU(True))
layers.append(nn.Conv2d(nunits, 3, kernel_size=1, stride=1))
layers.append(nn.Sigmoid())
self.main = nn.Sequential(*layers)
def __init__(self,
inplanes,
planes,
stride=1,
downsample=False,
upsample=False,
nobn=False):
super(ResidualBlock, self).__init__()
self.upsample = upsample
self.downsample = downsample
self.nobn = nobn
if self.upsample:
self.conv1 = nn.ConvTranspose2d(inplanes, planes, 4, 2, 1)
else:
self.conv1 = conv3x3(inplanes, planes, stride)
if not self.nobn:
self.bn1 = nn.BatchNorm2d(inplanes)
self.relu = nn.ReLU(inplace=True)
if self.downsample:
self.conv2 = nn.Sequential(nn.AvgPool2d(2, 2),
conv3x3(planes, planes))
else:
self.conv2 = conv3x3(planes, planes)
if not self.nobn:
self.bn2 = nn.BatchNorm2d(planes)
if inplanes != planes or self.upsample or self.downsample:
if self.upsample:
self.skip = nn.ConvTranspose2d(inplanes, planes, 4, 2, 1)
elif self.downsample:
self.skip = nn.Sequential(nn.AvgPool2d(2, 2),
nn.Conv2d(inplanes, planes, 1, 1))
else:
self.skip = nn.Conv2d(inplanes, planes, 1, 1, 0)
else:
self.skip = None
self.stride = stride
def __init__(self, ngpu=1):
super(HighToLowGenerator, self).__init__()
self.ngpu = ngpu
res_units = [96, 128, 256, 512, 256, 128]
inp_res_units = [
[96, 96], [96, 128], [128, 256], [256, 512], [512, 256],[256, 128]]
self.layers_set = []
self.layers_set_up = []
self.layers_set_final = nn.ModuleList()
self.layers_set_final_up = nn.ModuleList()
self.layers_in = conv3x3(4, 96)
layers = []
num_blocks_level = 2
for ru in range(4):
nunits = res_units[ru]
curr_inp_resu = inp_res_units[ru]
self.layers_set.insert(ru, [])
self.layers_set_up.insert(ru, [])
for j in range(num_blocks_level):
self.layers_set[ru].append(BasicBlock(curr_inp_resu[j], nunits))
self.layers_set_up[ru].append(nn.AvgPool2d(2, 2))
self.layers_set_up[ru].append(nn.BatchNorm2d(nunits))
self.layers_set_up[ru].append(nn.ReLU(True))
self.layers_set_up[ru].append(nn.ConvTranspose2d(nunits, nunits, kernel_size=1, stride=1))
self.layers_set_final.append(nn.Sequential(*self.layers_set[ru]))
self.layers_set_final_up.append(nn.Sequential(*self.layers_set_up[ru]))
for ru in range(4,len(res_units)):
nunits = res_units[ru]
curr_inp_resu = inp_res_units[ru]
self.layers_set.insert(ru, [])
self.layers_set_up.insert(ru, [])
for j in range(num_blocks_level):
self.layers_set[ru].append(BasicBlock(curr_inp_resu[j], nunits))
self.layers_set_up[ru].append(nn.Upsample(scale_factor=2, mode='bilinear',align_corners=True))
self.layers_set_up[ru].append(nn.BatchNorm2d(nunits))
self.layers_set_up[ru].append(nn.ReLU(True))
self.layers_set_up[ru].append(nn.ConvTranspose2d(nunits, nunits, kernel_size=1, stride=1))
self.layers_set_final.append(nn.Sequential(*self.layers_set[ru]))
self.layers_set_final_up.append(nn.Sequential(*self.layers_set_up[ru]))
layers.append(nn.Conv2d(res_units[-1], 3, kernel_size=1, stride=1))
layers.append(nn.Tanh())
self.main = nn.Sequential(*layers)