188 lines
5.6 KiB
Python
188 lines
5.6 KiB
Python
import tensorflow as tf
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from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverseLabels, Block
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from models.base import AEEncoder, Bottleneck, Decoder
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# class Bottleneck(tf.keras.Model):
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# def __init__(self, dim, latent_dim):
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# super(Bottleneck, self).__init__()
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# self.bottleneck = tf.keras.Sequential(
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# [Block(dim=dim, drop_path=0,
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# layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
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# )
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# self.flatten = tf.keras.layers.Flatten()
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# self.dense = tf.keras.layers.Dense(latent_dim)
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# def call(self, inputs, training=None):
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# x = inputs
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# x = self.bottleneck(x)
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# x = self.flatten(x)
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# x = self.dense(x)
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# return x
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# class Decoder(tf.keras.Model):
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# def __init__(self, bottleneck_size, latent_dim):
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# super(Decoder, self).__init__()
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# self.dense = tf.keras.layers.Dense(16 * 8 * bottleneck_size)
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# self.reshape = tf.keras.layers.Reshape((16, 8, bottleneck_size))
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# self.decoder = ConvNeXtReverseLabels(depths=list(reversed([3, 3, 9, 3])), dims=list(reversed([bottleneck_size, 128, 64, 16])), nr_of_classes=7)
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# def call(self, inputs, training=None):
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# x = inputs
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# x = self.dense(x)
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# x = self.reshape(x)
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# x = self.decoder(x)
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# return x
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class AutoEncoder(tf.keras.Model):
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def __init__(self, size=512):
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super(AutoEncoder, self).__init__()
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if size == 512:
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bottleneck_size = 64
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latent_dim = 128
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kernel_size = 7
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encoder_depths = [3, 3, 9, 3, 3]
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encoder_dims = [32, 64, 128, 256, bottleneck_size]
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decoder_input = (16, 8, bottleneck_size)
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decoder_depths = [3, 3, 9, 3, 3]
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decoder_dims = [bottleneck_size, 256, 128, 64, 16]
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elif size == 128:
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raise NotImplementedError
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elif size == 32:
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raise NotImplementedError
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else:
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raise NotImplementedError
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self.encoder : AEEncoder = AEEncoder(latent_dim, encoder_depths, encoder_dims, bottleneck_size, kernel_size)
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self.decoder = Decoder(decoder_input, decoder_depths, decoder_dims, kernel_size, is_label=True)
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def call(self, inputs, training=None):
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x, uv_mask = inputs
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# x = tf.transpose(x, [0,2,3,1])
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x = self.encoder(x, training=training)
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x = self.decoder(x, training=training)
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# x = tf.transpose(x, [0,3,1,2])
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# x = x * uv_mask[:,:, tf.newaxis]
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# x = x * uv_mask[:,tf.newaxis,:,:]
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x = x * uv_mask[:,:,:,tf.newaxis]
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return x
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# class Decoder32(tf.keras.Model):
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# def __init__(self, bottleneck_size, latent_dim):
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# super(Decoder32, self).__init__()
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# self.dense = tf.keras.layers.Dense(4 * 2 * bottleneck_size)
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# self.reshape = tf.keras.layers.Reshape((4, 2, bottleneck_size))
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# self.decoder = ConvNeXtReverseLabels(depths=list(reversed([27, 27])), dims=list(reversed([bottleneck_size, 256])), nr_of_classes=7)
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# def call(self, inputs, training=None):
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# x = inputs
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# x = self.dense(x)
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# x = self.reshape(x)
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# x = self.decoder(x)
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# return x
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# class AutoEncoder32(tf.keras.Model):
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# def __init__(self):
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# super(AutoEncoder32, self).__init__()
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# bottleneck_size = 512
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# latent_dim = 128
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# self.encoder = ConvNeXt(depths=[27, 27], dims=[256, bottleneck_size], include_top=False)
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# self.bottleneck = Bottleneck(bottleneck_size, latent_dim)
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# self.decoder = Decoder32(bottleneck_size, latent_dim)
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# def call(self, inputs, training=None):
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# x, uv_mask = inputs
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# # x = tf.transpose(x, [0,2,3,1])
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# x = self.encoder(x, training=training)
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# x = self.bottleneck(x, training=training)
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# x = self.decoder(x, training=training)
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# # x = tf.transpose(x, [0,3,1,2])
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# # x = x * uv_mask[:,:, tf.newaxis]
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# # x = x * uv_mask[:,tf.newaxis,:,:]
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# # x = x * uv_mask[:,:,:,tf.newaxis]
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# return x
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# class Decoder128(tf.keras.Model):
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# def __init__(self, bottleneck_size, latent_dim):
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# super(Decoder128, self).__init__()
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# self.dense = tf.keras.layers.Dense(4 * 2 * bottleneck_size)
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# self.reshape = tf.keras.layers.Reshape((4, 2, bottleneck_size))
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# self.decoder = ConvNeXtReverseLabels(depths=list(reversed([16, 16, 9, 9])), dims=list(reversed([bottleneck_size, 256, 128, 64])), nr_of_classes=7)
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# def call(self, inputs, training=None):
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# x = inputs
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# x = self.dense(x)
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# x = self.reshape(x)
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# x = self.decoder(x)
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# return x
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# class AutoEncoder128(tf.keras.Model):
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# def __init__(self):
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# super(AutoEncoder128, self).__init__()
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# bottleneck_size = 512
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# latent_dim = 128
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# self.encoder = ConvNeXt(depths=[16, 16, 9, 9], dims=[64, 128, 256, bottleneck_size], include_top=False)
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# self.bottleneck = Bottleneck(bottleneck_size, latent_dim)
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# self.decoder = Decoder128(bottleneck_size, latent_dim)
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# def call(self, inputs, training=None):
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# x, uv_mask = inputs
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# # x = tf.transpose(x, [0,2,3,1])
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# x = self.encoder(x, training=training)
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# x = self.bottleneck(x, training=training)
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# x = self.decoder(x, training=training)
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# # x = tf.transpose(x, [0,3,1,2])
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# # x = x * uv_mask[:,:, tf.newaxis]
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# # x = x * uv_mask[:,tf.newaxis,:,:]
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# # x = x * uv_mask[:,:,:,tf.newaxis]
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# return x
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