def __init__(self, in_ch):
self.ln1 = op.LayerNormalization(name="layer_norm1")
self.ln3 = op.LayerNormalization(name="layer_norm3")
self.ln5 = op.LayerNormalization(name="layer_norm5")
self.ln7 = op.LayerNormalization(name="layer_norm7")
self.conv1 = op.Encoder(in_ch,
64,
3,
3,
name='encoder1',
strides=[1, 2, 2, 1])
self.conv3 = op.Encoder(64,
128,
3,
3,
name='encoder3',
strides=[1, 2, 2, 1])
self.conv5 = op.Encoder(128,
256,
3,
3,
name='encoder5',
strides=[1, 2, 2, 1])
self.conv7 = op.Encoder(256,
512,
3,
3,
name='encoder7',
strides=[1, 2, 2, 1])
self.fully_connect = op.Dense("fully_connect1")
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 gen_encoder(index, filter_size, embedding_size, sequence_length):
encoder = op.Encoder(
1,
128,
filter_size,
embedding_size,
strides=[1, 1, 1, 1],
padding='VALID',
trainable=trainable,
name='encoder/{}'.format(index))
max_pool = op.MaxPool([1, sequence_length - filter_size + 1, 1, 1], name="max_pooling/{}".format(index))
def encode(tensor):
return max_pool(tf.nn.relu(encoder(tensor)))
return encode
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')
