def __init__(self, in_ch): self.bnc1 = op.BatchNormalization(name="bnc1") self.bnc2 = op.BatchNormalization(name="bnc2") self.bnc3 = op.BatchNormalization(name="bnc3") self.bnc4 = op.BatchNormalization(name="bnc4") self.conv1 = op.Encoder(in_ch, 64, 3, 3, name='encoder1', strides=[1, 2, 2, 1]) self.conv2 = op.Encoder(64, 128, 3, 3, name='encoder2', strides=[1, 2, 2, 1]) self.conv3 = op.Encoder(128, 256, 3, 3, name='encoder3', strides=[1, 2, 2, 1]) self.conv4 = op.Encoder(256, 512, 3, 3, name='encoder4', strides=[1, 2, 2, 1]) self.dense = op.Dense("dense")
def __init__(self): self.bnd1 = op.BatchNormalization(name='guide2_bnd1') self.bnd2 = op.BatchNormalization(name='guide2_bnd2') self.bnd3 = op.BatchNormalization(name='guide2_bnd3') self.deconv3 = op.PixelShuffler( op.Encoder(512, 512, 3, 3, name='guide2_decoder3'), 128, 2) self.deconv2 = op.PixelShuffler( op.Encoder(128, 256, 3, 3, name='guide2_decoder2'), 64, 2) self.deconv1 = op.PixelShuffler( op.Encoder(64, 128, 3, 3, name='guide2_decoder1'), 32, 2) self.deconv0 = op.Encoder(32, 3, 3, 3, name="guide2_decoder0")
def __init__(self, inference_channels, original_channels, recursives=5): self.bnc1 = op.BatchNormalization(name='upsampler/bnc1') self.bnc2 = op.BatchNormalization(name='upsampler/bnc2') self.conv1 = op.PixelShuffler( op.Encoder(inference_channels * 2, 256, 3, 3, name='upsampler/encoder1'), 64, 2) self.conv2 = op.PixelShuffler( op.Encoder(64, 128, 3, 3, name="upsampler/encoder2"), 32, 2) self.conv3 = op.Encoder(32, 3, 3, 3, name="upsampler/encoder3") self.recursives = recursives
def __init__(self, channels): self.bnc1 = op.BatchNormalization(name='upsampler/bnc1') self.bnc2 = op.BatchNormalization(name='upsampler/bnc2') self.bnc3 = op.BatchNormalization(name='upsampler/bnc3') self.bnc4 = op.BatchNormalization(name='upsampler/bnc4') self.conv1 = op.Encoder(channels, 256, 3, 3, name='upsampler/encoder0') self.conv2 = op.PixelShuffler( op.Encoder(256, 1024, 3, 3, name='upsampler/encoder1'), 256, 2) self.resnet1 = op.ResNet(256, name='upsampler/resnet1') self.conv3 = op.Encoder(256, 128, 3, 3, name='upsampler/encoder3') self.resnet2 = op.PixelShuffler( op.ResNet(128, name='upsampler/resnet2'), 32, 2) self.resnet3 = op.ResNet(32, name='upsampler/resnet3') self.conv4 = op.Encoder(32, 3, 3, 3, name='upsampler/encoder4')
def __init__(self): self.bnd1 = op.BatchNormalization(name='bnd2') self.bnd2 = op.BatchNormalization(name='bnd3') self.bnd3 = op.BatchNormalization(name='bnd4') self.bnd4 = op.BatchNormalization(name='bnd5') self.deconv4 = op.PixelShuffler( op.Encoder(512, 1024, 3, 3, name='decoder4'), 256, 2) self.deconv3 = op.PixelShuffler( op.Encoder(256, 512, 3, 3, name='decoder3'), 128, 2) self.deconv2 = op.PixelShuffler( op.Encoder(128, 256, 3, 3, name='decoder2'), 64, 2) self.deconv1 = op.PixelShuffler( op.Encoder(64, 128, 3, 3, name='decoder1'), 32, 2) self.deconv0 = op.Encoder(32, 3, 3, 3, name="decoder0") self.dense = op.Dense('dense')
def __init__(self, channels, recursives=5): self.encoder = op.Encoder(channels, channels, 3, 3, name="encoder", initializer=tf.constant_initializer(0.0)) self.recursives = recursives self.bn = op.BatchNormalization(name="normalization")
def __init__(self, channels): self.bnc0 = op.BatchNormalization(name='bnc0') self.bnc1 = op.BatchNormalization(name='bnc1') self.bnc2 = op.BatchNormalization(name='bnc2') self.bnc3 = op.BatchNormalization(name='bnc3') self.bnc4 = op.BatchNormalization(name='bnc4') self.conv0 = op.Encoder(channels, 32, 3, 3, strides=[1, 1, 1, 1], name='encoder0') self.conv1 = op.Encoder(32, 64, 4, 4, strides=[1, 2, 2, 1], name='encoder1') self.conv2 = op.Encoder(64, 128, 4, 4, strides=[1, 2, 2, 1], name='encoder2') self.conv3 = op.Encoder(128, 256, 4, 4, strides=[1, 2, 2, 1], name='encoder3') self.conv4 = op.Encoder(256, 512, 4, 4, strides=[1, 2, 2, 1], name='encoder4') self.dense = op.Dense()
def __init__(self, embedding_channels): self.enc1 = op.Encoder(3, 64, 3, 3, strides=[1, 2, 2, 1], name='encoder/1') self.enc2 = op.Encoder(64, 128, 3, 3, strides=[1, 2, 2, 1], name='encoder/2') self.enc3 = op.Encoder(128, 256, 3, 3, strides=[1, 2, 2, 1], name='encoder/3') self.enc4 = op.Encoder(256, 512, 3, 3, strides=[1, 1, 1, 1], name='encoder/4') self.dec4 = op.PixelShuffler(op.Encoder(512 + embedding_channels, 1024, 3, 3, name='decoder/4'), 256, 2) self.dec3 = op.PixelShuffler(op.Encoder(256, 512, 3, 3, name='decoder/3'), 128, 2) self.dec2 = op.PixelShuffler(op.Encoder(128, 256, 3, 3, name='decoder/2'), 64, 2) self.dec1 = op.Encoder(64, 3, 3, 3, name='decoder/1') self.bnd1 = op.BatchNormalization(name='bnd/1') self.bnd2 = op.BatchNormalization(name='bnd/2') self.bnd3 = op.BatchNormalization(name='bnd/3') self.bnc1 = op.BatchNormalization(name='bnc/1') self.bnc2 = op.BatchNormalization(name='bnc/2') self.bnc3 = op.BatchNormalization(name='bnc/3') self.bnc4 = op.BatchNormalization(name='bnc/4')
def __init__(self): self.conv1 = op.Encoder(3, 48, 5, 5, strides=[1, 2, 2, 1], name='encoder1') self.conv1_f1 = op.Encoder(48, 128, 3, 3, name='encoder1_flat1') self.conv1_f2 = op.Encoder(128, 128, 3, 3, name='encoder1_flat2') self.conv2 = op.Encoder(128, 256, 5, 5, strides=[1, 2, 2, 1], name='encoder2') self.conv2_f1 = op.Encoder(256, 256, 3, 3, name='encoder2_flat1') self.conv2_f2 = op.Encoder(256, 256, 3, 3, name='encoder2_flat2') self.conv3 = op.Encoder(256, 256, 5, 5, strides=[1, 2, 2, 1], name='encoder3') self.conv3_f1 = op.Encoder(256, 512, 3, 3, name='encoder3_flat1') self.conv3_f2 = op.Encoder(512, 1024, 3, 3, name='encoder3_flat2') self.conv3_f3 = op.Encoder(1024, 512, 3, 3, name='encoder3_flat3') self.conv3_f4 = op.Encoder(512, 256, 3, 3, name='encoder3_flat4') self.bnc1 = op.BatchNormalization(name='bnc1') self.bnc1_f1 = op.BatchNormalization(name='bnc1_flat1') self.bnc1_f2 = op.BatchNormalization(name='bnc1_flat2') self.bnc2 = op.BatchNormalization(name='bnc2') self.bnc2_f1 = op.BatchNormalization(name='bnc2_flat1') self.bnc2_f2 = op.BatchNormalization(name='bnc2_flat2') self.bnc3 = op.BatchNormalization(name='bnc3') self.bnc3_f1 = op.BatchNormalization(name='bnc3_flat1') self.bnc3_f2 = op.BatchNormalization(name='bnc3_flat2') self.bnc3_f3 = op.BatchNormalization(name='bnc3_flat3') self.bnc3_f4 = op.BatchNormalization(name='bnc3_flat4') self.deconv3 = op.PixelShuffler(op.Encoder(256, 1024, 3, 3, name='decoder3'), 256, 2) self.deconv3_f1 = op.Encoder(256, 128, 3, 3, name='decoder3_flat1') self.deconv3_f2 = op.Encoder(128, 128, 3, 3, name='decoder3_flat2') self.deconv2 = op.PixelShuffler(op.Encoder(128, 512, 3, 3, name='decoder2'), 128, 2) self.deconv2_f1 = op.Encoder(128, 48, 3, 3, name='decoder2_flat1') self.deconv2_f2 = op.Encoder(48, 48, 3, 3, name='decoder2_flat2') self.deconv1 = op.PixelShuffler(op.Encoder(48, 192, 3, 3, name='decoder1'), 48, 2) self.deconv1_f1 = op.Encoder(48, 24, 3, 3, name='decoder1_flat1') self.deconv1_f2 = op.Encoder(24, 24, 3, 3, name='decoder1_flat2') self.deconv0 = op.Encoder(24, 1, 3, 3, name='decoder0') self.bnd3 = op.BatchNormalization(name='bnd3') self.bnd3_f1 = op.BatchNormalization(name='bnd3_flat1') self.bnd3_f2 = op.BatchNormalization(name='bnd3_flat2') self.bnd2 = op.BatchNormalization(name='bnd2') self.bnd2_f1 = op.BatchNormalization(name='bnd2_flat1') self.bnd2_f2 = op.BatchNormalization(name='bnd2_flat2') self.bnd1 = op.BatchNormalization(name='bnd1') self.bnd1_f1 = op.BatchNormalization(name='bnd1_flat1') self.bnd1_f2 = op.BatchNormalization(name='bnd1_flat2')
def __init__(self, channels): self.bnc0 = op.BatchNormalization(name='bnc0') self.bnc1 = op.BatchNormalization(name='bnc1') self.bnc2 = op.BatchNormalization(name='bnc2') self.bnc3 = op.BatchNormalization(name='bnc3') self.bnc4 = op.BatchNormalization(name='bnc4') self.bnc5 = op.BatchNormalization(name='bnc5') self.bnc6 = op.BatchNormalization(name='bnc6') self.bnc7 = op.BatchNormalization(name='bnc7') self.bnc8 = op.BatchNormalization(name='bnc8') self.bnd1 = op.BatchNormalization(name='bnd1') self.bnd2 = op.BatchNormalization(name='bnd2') self.bnd3 = op.BatchNormalization(name='bnd3') self.bnd4 = op.BatchNormalization(name='bnd4') self.bnd5 = op.BatchNormalization(name='bnd5') self.bnd6 = op.BatchNormalization(name='bnd6') self.bnd7 = op.BatchNormalization(name='bnd7') self.bnd8 = op.BatchNormalization(name='bnd8') self.conv0 = op.Encoder(channels, 32, 3, 3, strides=[1, 1, 1, 1], name='encoder0') self.conv1 = op.Encoder(32, 64, 4, 4, strides=[1, 2, 2, 1], name='encoder1') self.conv2 = op.Encoder(64, 64, 3, 3, strides=[1, 1, 1, 1], name='encoder2') self.conv3 = op.Encoder(64, 128, 4, 4, strides=[1, 2, 2, 1], name='encoder3') self.conv4 = op.Encoder(128, 128, 3, 3, strides=[1, 1, 1, 1], name='encoder4') self.conv5 = op.Encoder(128, 256, 4, 4, strides=[1, 2, 2, 1], name='encoder5') self.conv6 = op.Encoder(256, 256, 3, 3, strides=[1, 1, 1, 1], name='encoder6') self.conv7 = op.Encoder(256, 512, 4, 4, strides=[1, 2, 2, 1], name='encoder7') self.conv8 = op.Encoder(512, 512, 3, 3, strides=[1, 1, 1, 1], name='encoder8') self.deconv8 = op.PixelShuffler( op.Encoder(1152, 1024, 3, 3, name='decoder8'), 256, 2) self.deconv7 = op.Encoder(256, 256, 3, 3, name='decoder7') self.deconv6 = op.PixelShuffler( op.Encoder(512, 512, 3, 3, name='decoder6'), 128, 2) self.deconv5 = op.Encoder(128, 128, 3, 3, name='decoder5') self.deconv4 = op.PixelShuffler( op.Encoder(256, 256, 3, 3, name='decoder4'), 64, 2) self.deconv3 = op.Encoder(64, 64, 3, 3, name='decoder3') self.deconv2 = op.PixelShuffler( op.Encoder(128, 128, 3, 3, name='decoder2'), 32, 2) self.deconv1 = op.Encoder(32, 32, 3, 3, name='decoder1') self.deconv0 = op.Encoder(64, 3, 3, 3, name='decoder0')
def __init__(self, channels): self.bnc1 = op.BatchNormalization(name='bnc1') self.bnc1_f = op.BatchNormalization(name='bnc1_f') self.bnc2 = op.BatchNormalization(name='bnc2') self.bnc2_f = op.BatchNormalization(name='bnc2_f') self.bnc3 = op.BatchNormalization(name='bnc3') self.bnc3_f = op.BatchNormalization(name='bnc3_f') self.bnd1 = op.BatchNormalization(name='bnd1') self.bnd1_f = op.BatchNormalization(name='bnd1_f') self.bnd2 = op.BatchNormalization(name='bnd2') self.bnd2_f = op.BatchNormalization(name='bnd2_f') self.bnd3 = op.BatchNormalization(name='bnd3') self.bnd3_f = op.BatchNormalization(name='bnd3_f') self.conv1 = op.Encoder(channels, 32, 3, 3, strides=[1, 2, 2, 1], name='encoder1') self.conv1_f1 = op.Encoder(32, 32, 3, 3, name="encoder1_f1") self.conv2 = op.Encoder(32, 64, 3, 3, strides=[1, 2, 2, 1], name='encoder2') self.conv2_f1 = op.Encoder(64, 64, 3, 3, name="encoder2_f1") self.conv3 = op.Encoder(64, 128, 3, 3, strides=[1, 2, 2, 1], name='encoder3') self.conv3_f1 = op.Encoder(128, 128, 3, 3, name="encoder3_f1") self.deconv3 = op.PixelShuffler( op.Encoder(128, 512, 3, 3, name='decoder3'), 128, 2) self.deconv3_f1 = op.Encoder(128, 128, 3, 3, name="decoder3_f1") self.deconv2 = op.PixelShuffler( op.Encoder(256, 256, 3, 3, name='decoder2'), 64, 2) self.deconv2_f1 = op.Encoder(64, 64, 3, 3, name="decoder2_f1") self.deconv1 = op.PixelShuffler( op.Encoder(128, 128, 3, 3, name='decoder1'), 32, 2) self.deconv1_f1 = op.Encoder(32, 32, 3, 3, name="decoder1_f1") self.deconv0 = op.Encoder(32, 3, 3, 3, name="decoder0") self.fully_connect = op.Dense('fully_connect') self.fully_unconnect = op.Dense('fully_unconnect')
def __init__(self): self.conv0 = op.Encoder(3, 64, 3, 3, name='encoder0') self.conv1 = op.Encoder(64, 128, 4, 4, strides=[1, 2, 2, 1], name='encoder1') self.conv1_f1 = op.Encoder(128, 128, 3, 3, name='encoder1_flat1') self.conv1_f2 = op.Encoder(128, 128, 3, 3, name='encoder1_flat2') self.conv2 = op.Encoder(128, 256, 4, 4, strides=[1, 2, 2, 1], name='encoder2') self.conv2_f1 = op.Encoder(256, 256, 3, 3, name='encoder2_flat1') self.conv2_f2 = op.Encoder(256, 256, 3, 3, name='encoder2_flat2') self.conv3 = op.Encoder(256, 512, 4, 4, strides=[1, 2, 2, 1], name='encoder3') self.conv3_f1 = op.Encoder(512, 512, 3, 3, name='encoder3_flat1') self.conv3_f2 = op.Encoder(512, 512, 3, 3, name='encoder3_flat2') self.bnc0 = op.BatchNormalization(name='bnc0') self.bnc1 = op.BatchNormalization(name='bnc1') self.bnc1_f1 = op.BatchNormalization(name='bnc1_flat1') self.bnc1_f2 = op.BatchNormalization(name='bnc1_flat2') self.bnc2 = op.BatchNormalization(name='bnc2') self.bnc2_f1 = op.BatchNormalization(name='bnc2_flat1') self.bnc2_f2 = op.BatchNormalization(name='bnc2_flat2') self.bnc3 = op.BatchNormalization(name='bnc3') self.bnc3_f1 = op.BatchNormalization(name='bnc3_flat1') self.bnc3_f2 = op.BatchNormalization(name='bnc3_flat2') self.deconv3 = op.PixelShuffler( op.Encoder(512, 1024, 4, 4, name="decoder3"), 256, 2) self.deconv3_f1 = op.Encoder(256, 256, 3, 3, name='decoder3_flat1') self.deconv3_f2 = op.Encoder(256, 256, 3, 3, name='decoder3_flat2') self.deconv2 = op.PixelShuffler(None, 128, 2) self.deconv2_f1 = op.Encoder(128, 128, 3, 3, name='decoder2_flat1') self.deconv2_f2 = op.Encoder(128, 128, 3, 3, name='decoder2_flat2') self.deconv1 = op.PixelShuffler(None, 64, 2) self.deconv1_f1 = op.Encoder(64, 32, 3, 3, name='decoder1_flat1') self.deconv0 = op.Encoder(32, 1, 3, 3, name='decoder0') self.bnd3 = op.BatchNormalization(name='bnd3') self.bnd3_f1 = op.BatchNormalization(name='bnd3_flat1') self.bnd3_f2 = op.BatchNormalization(name='bnd3_flat2') self.bnd2 = op.BatchNormalization(name='bnd2') self.bnd2_f1 = op.BatchNormalization(name='bnd2_flat1') self.bnd2_f2 = op.BatchNormalization(name='bnd2_flat2') self.bnd1 = op.BatchNormalization(name='bnd1') self.bnd1_f1 = op.BatchNormalization(name='bnd1_flat1')
def __init__(self, channels): self.bnc1 = op.BatchNormalization(name='embedding/bnc1') self.bnc2 = op.BatchNormalization(name='embedding/bnc2') self.conv1 = op.Encoder(channels, 256, 3, 3, name='embedding/encoder0') self.conv2 = op.Encoder(256, 256, 3, 3, name='embedding/encoder1')