def __init__(self, channels):
super(EncodeNet, self).__init__()
self.conv_channels_up = nn.Conv2d(1, channels, 1)
convList = []
for i in range(4):
convList.append(pixelShuffleFunction.PixelUnshuffle(2))
if(i>=2):
convList.append(nn.Conv2d(channels * 4, channels, 1))
elif(i==0):
convList.append(nn.Conv2d(channels * 4, channels, 5, padding=2))
elif(i==1):
convList.append(nn.Conv2d(channels * 4, channels, 3, padding=1))
'''
i kernel padding
0 5 2
1 3 1
2 1 0
3 1 0
'''
convList.append(nn.LeakyReLU(inplace=True))
convList.append(nn.BatchNorm2d(channels))
convList.append(baseNet.ResNet(transpose=False, channels=channels, kernel_size=3, padding=1))
convList.append(nn.Conv2d(channels, 16, 1))
convList.append(nn.LeakyReLU(inplace=True))
self.convList = nn.Sequential(*convList)
def __init__(self, channels):
super(EncodeNet, self).__init__()
self.conv_channels_up = nn.Conv2d(1, channels, 1)
convList = []
for i in range(4):
convList.append(pixelShuffleFunction.PixelUnshuffle(2))
convList.append(nn.Conv2d(channels*4, channels, 1))
convList.append(nn.LeakyReLU())
convList.append(nn.Conv2d(channels, 16, 1))
convList.append(nn.LeakyReLU())
self.convList = nn.Sequential(*convList)
def __init__(self, channels, downSample=True):
super(SampleResNet, self).__init__()
self.downSample = downSample
convList = []
if(self.downSample==True):
convList.append(pixelShuffleFunction.PixelUnshuffle(2))
convList.append(nn.Conv2d(channels * 4, channels, 1))
convList.append(nn.LeakyReLU())
else:
convList.append(nn.Conv2d(channels, channels * 4, 1))
convList.append(nn.LeakyReLU())
convList.append(nn.PixelShuffle(2))
self.convList = nn.Sequential(*convList)
def __init__(self, channels):
super(EncodeNet, self).__init__()
self.conv_channels_up = nn.Conv2d(1, channels, 1)
convList = []
for i in range(4):
convList.append(pixelShuffleFunction.PixelUnshuffle(2))
if(i<=1):
convList.append(nn.Conv2d(channels*4, channels, 3, padding=1))
else:
convList.append(nn.Conv2d(channels * 4, channels, 1))
convList.append(nn.LeakyReLU(inplace=True))
convList.append(nn.BatchNorm2d(channels))
convList.append(nn.Conv2d(channels, 16, 1))
convList.append(nn.LeakyReLU(inplace=True))
self.convList = nn.Sequential(*convList)
def __init__(self, channels):
super(EncodeNet, self).__init__()
self.conv_channels_up = nn.Conv2d(1, channels, 1)
convList = []
for i in range(4):
convList.append(pixelShuffleFunction.PixelUnshuffle(2))
convList.append(nn.Conv2d(channels * 4, channels, 1))
convList.append(nn.LeakyReLU())
convList.append(
baseNet.ResNet(transpose=False,
channels=channels,
kernel_size=3,
padding=1))
convList.append(nn.Conv2d(channels, 16, 1))
convList.append(nn.LeakyReLU())
self.convList = nn.Sequential(*convList)
