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import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, Sequential
from tensorflow_addons.layers import GroupNormalization
@tf.keras.utils.register_keras_serializable()
class ResnetBlock(layers.Layer):
def __init__(
self,
*,
in_channels,
dropout=0.0,
out_channels=None,
conv_shortcut=False,
**kwargs
):
super().__init__(**kwargs)
self.in_channels = in_channels
self.dropout_rate = dropout
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.norm1 = GroupNormalization(groups=32, epsilon=1e-6)
self.conv1 = layers.Conv2D(
out_channels, kernel_size=3, strides=1, padding="same"
)
self.norm2 = GroupNormalization(groups=32, epsilon=1e-6)
self.dropout = layers.Dropout(dropout)
self.conv2 = layers.Conv2D(
out_channels, kernel_size=3, strides=1, padding="same"
)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
self.conv_shortcut = layers.Conv2D(
out_channels, kernel_size=3, strides=1, padding="same"
)
else:
self.nin_shortcut = layers.Conv2D(
out_channels, kernel_size=1, strides=1, padding="valid"
)
def get_config(self):
config = super().get_config()
config.update(
{
"in_channels": self.in_channels,
"dropout": self.dropout_rate,
"out_channels": self.out_channels,
"conv_shortcut": self.use_conv_shortcut,
}
)
return config
def call(self, x):
h = x
h = self.norm1(h)
h = keras.activations.swish(h)
h = self.conv1(h)
h = self.norm2(h)
h = keras.activations.swish(h)
h = self.dropout(h)
h = self.conv2(h)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
x = self.conv_shortcut(x)
else:
x = self.nin_shortcut(x)
return x + h
@tf.keras.utils.register_keras_serializable()
class AttentionBlock(layers.Layer):
def __init__(self, channels, **kwargs):
super().__init__(**kwargs)
self.channels = channels
self.norm = GroupNormalization(groups=32, epsilon=1e-6)
self.q = layers.Conv2D(channels, kernel_size=1, strides=1, padding="valid")
self.k = layers.Conv2D(channels, kernel_size=1, strides=1, padding="valid")
self.v = layers.Conv2D(channels, kernel_size=1, strides=1, padding="valid")
self.proj_out = layers.Conv2D(
channels, kernel_size=1, strides=1, padding="valid"
)
self.attention = layers.Attention()
def get_config(self):
config = super().get_config()
config.update(
{
"channels": self.channels,
}
)
return config
def call(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
(b, h, w, c,) = (
tf.shape(q)[0],
tf.shape(q)[1],
tf.shape(q)[2],
tf.shape(q)[3],
)
if b is None:
b = -1
q = tf.reshape(q, [b, h * w, c])
k = tf.reshape(k, [b, h * w, c])
v = tf.reshape(v, [b, h * w, c])
h_ = self.attention([q, v, k])
h_ = tf.reshape(h_, [b, h, w, c])
h_ = self.proj_out(h_)
return x + h_
@tf.keras.utils.register_keras_serializable()
class Downsample(layers.Layer):
def __init__(self, channels, **kwargs):
super().__init__(**kwargs)
self.channels = channels
self.down_sample = self.down_sample = layers.AveragePooling2D(
pool_size=2, strides=2
)
self.conv = layers.Conv2D(channels, kernel_size=3, strides=1, padding="same")
def get_config(self):
config = super().get_config()
config.update(
{
"channels": self.channels,
}
)
return config
def call(self, x):
x = self.down_sample(x)
x = self.conv(x)
return x
@tf.keras.utils.register_keras_serializable()
class Upsample(layers.Layer):
def __init__(self, channels, **kwargs):
super().__init__(**kwargs)
self.channels = channels
self.up_sample = layers.UpSampling2D(size=2, interpolation="bilinear")
self.conv = layers.Conv2D(channels, kernel_size=3, strides=1, padding="same")
def get_config(self):
config = super().get_config()
config.update(
{
"channels": self.channels,
}
)
return config
def call(self, x):
x = self.up_sample(x)
x = self.conv(x)
return x
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