def __init__(self): super(DecodeNet, self).__init__() convList = [] convList.append( baseNet.SampleNet(downSample=False, in_channels=16, out_channels=32)) convList.append( baseNet.SampleNet(downSample=False, in_channels=32, out_channels=64)) convList.append( baseNet.SampleNet(downSample=False, in_channels=64, out_channels=128)) convList.append( baseNet.SampleNet(downSample=False, in_channels=128, out_channels=256)) convList.append(nn.ConvTranspose2d(256, 16, 1)) convList.append(nn.LeakyReLU()) convList.append(nn.ConvTranspose2d(16, 1, 1)) convList.append(nn.LeakyReLU()) self.convList = nn.Sequential(*convList)
def __init__(self): super(DecodeNet, self).__init__() self.tconv_channels_down = nn.ConvTranspose2d(256, 1, 1) convList = [] convList.append(baseNet.SampleNet(downSample=False, in_channels=16, out_channels=32)) convList.append(baseNet.SampleNet(downSample=False, in_channels=32, out_channels=64)) convList.append(baseNet.SampleNet(downSample=False, in_channels=64, out_channels=256, kernel_size=4, stride=4)) self.convList = nn.Sequential(*convList)
def __init__(self): super(EncodeNet, self).__init__() self.conv_channels_up = nn.Conv2d(1, 256, 1) convList = [] convList.append(baseNet.SampleNet(downSample=True, in_channels=256, out_channels=64, kernel_size=4, stride=4)) convList.append(baseNet.SampleNet(downSample=True, in_channels=64, out_channels=32)) convList.append(baseNet.SampleNet(downSample=True, in_channels=32, out_channels=16)) self.convList = nn.Sequential(*convList)
def __init__(self, channelsList): super(EncodeNet, self).__init__() self.conv_channels_up = nn.Conv2d(1, channelsList[0], 1) convList = [] for i in range(channelsList.__len__() - 1): convList.append(baseNet.SampleNet(downSample=True, in_channels=channelsList[i], out_channels=channelsList[i+1])) print(channelsList[i], channelsList[i+1]) self.convList = nn.Sequential(*convList)
def __init__(self, dim_z, device): super(Generator, self).__init__() self.fc1 = nn.Linear(dim_z, 2048) self.fc2 = nn.Linear(2048, 64 * 8 * 8) self.tconv_channels_down = nn.ConvTranspose2d(256, 3, 1) convList = [] convList.append( baseNet.SampleNet(downSample=False, in_channels=64, out_channels=128, device=device)) convList.append( baseNet.SampleNet(downSample=False, in_channels=128, out_channels=256, device=device)) self.convList = nn.Sequential(*convList)
def __init__(self, channelsList): super(DecodeNet, self).__init__() self.tconv_channels_down = nn.ConvTranspose2d(channelsList[-1], 1, 1) convList = [] for i in range(channelsList.__len__() - 1): convList.append( baseNet.SampleNet(downSample=False, in_channels=channelsList[i], out_channels=channelsList[i + 1])) print(channelsList[i], channelsList[i + 1]) self.convList = nn.Sequential(*convList)
def __init__(self, dim_z, device): super(Encoder, self).__init__() self.dim_z = dim_z self.conv_channels_up = nn.Conv2d(3, 256, 1) convList = [] convList.append( baseNet.SampleNet(downSample=True, in_channels=256, out_channels=128, device=device)) convList.append( baseNet.SampleNet(downSample=True, in_channels=128, out_channels=64, device=device)) self.convList = nn.Sequential(*convList) self.fc1 = nn.Linear(64 * 8 * 8, 2048) self.fc2 = nn.Linear(2048, dim_z * 2)
def __init__(self): super(DecodeNet, self).__init__() self.tconv_channels_down = nn.ConvTranspose2d(512, 16, 1, groups=16) convList = [] for i in range(3): convList.append( baseNet.SampleNet(downSample=False, in_channels=512, out_channels=512, groups=16)) self.convList = nn.Sequential(*convList)
def __init__(self): super(EncodeNet, self).__init__() self.conv_channels_up = nn.Conv2d(16, 512, 1, groups=16) convList = [] for i in range(3): convList.append( baseNet.SampleNet(downSample=True, in_channels=512, out_channels=512, groups=16)) self.convList = nn.Sequential(*convList)
def __init__(self): super(EncodeNet, self).__init__() convList = [] convList.append(nn.Conv2d(1, 64, 1)) convList.append(nn.LeakyReLU()) convList.append( baseNet.ResNet(transpose=False, channels=64, kernel_size=3, padding=1)) convList.append( baseNet.SampleNet(downSample=True, in_channels=64, out_channels=1)) self.convList = nn.Sequential(*convList)
def __init__(self): super(DecodeNet, self).__init__() self.conv_up = baseNet.SampleNet(downSample=False, in_channels=3, out_channels=64) convList = [] for i in range(4): convList.append( baseNet.ResNet(transpose=True, channels=64, kernel_size=3, padding=1)) self.convList = nn.Sequential(*convList) self.conv_channels_down = nn.ConvTranspose2d(64, 3, 1)