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representation.py
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representation.py
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import tensorflow as tf
from resblock import TimeDistributedResBlock2D
tfk = tf.keras
tfkl = tf.keras.layers
class Pyramid(tfkl.Layer):
def __init__(self):
super(Pyramid, self).__init__()
self.net = tfk.Sequential([
tfkl.TimeDistributed(
tfkl.Conv2D(32, 2, strides=2, padding='valid', activation=tf.nn.relu)
),
tfkl.TimeDistributed(
tfkl.Conv2D(64, 2, strides=2, padding='valid', activation=tf.nn.relu)
),
tfkl.TimeDistributed(
tfkl.Conv2D(128, 2, strides=2, padding='valid', activation=tf.nn.relu)
),
tfkl.TimeDistributed(
tfkl.Conv2D(256, 8, strides=8, padding='valid', activation=tf.nn.relu)
)
])
def call(self, inputs):
x, v = inputs
v = tf.expand_dims(tf.expand_dims(v, 2), 2)
v = tf.tile(v, [1, 1, 64, 64, 1])
r = tf.concat([v, x], -1)
return self.net(r)
class Tower(tfkl.Layer):
def __init__(self):
super(Tower, self).__init__()
self.conv_1 = tfkl.TimeDistributed(
tfkl.Conv2D(256, 2, strides=2, padding='valid', activation=tf.nn.relu)
)
self.block_1 = TimeDistributedResBlock2D((128, 256), 3, strides=1, padding='same', activation=tf.nn.relu)
self.conv_2 = tfkl.TimeDistributed(
tfkl.Conv2D(256, 2, strides=2, padding='valid', activation=tf.nn.relu)
)
self.block_2 = TimeDistributedResBlock2D((128, 256+7), 3, strides=1, padding='same', activation=tf.nn.relu)
self.conv_3 = tfkl.TimeDistributed(
tfkl.Conv2D(256, 3, strides=1, padding='same', activation=tf.nn.relu),
)
self.conv_4 = tfkl.TimeDistributed(
tfkl.Conv2D(256, 1, strides=1, padding='same', activation=tf.nn.relu)
)
def call(self, inputs):
x, v = inputs
x = self.conv_1(x)
x = self.block_1(x)
x = self.conv_2(x)
v = tf.expand_dims(tf.expand_dims(v, 2), 2)
v = tf.tile(v, [1, 1, 16, 16, 1])
r = tf.concat([x, v], -1)
r = self.block_2(r)
r = self.conv_3(r)
r = self.conv_4(r)
return r
class Pool(tfkl.Layer):
def __init__(self):
super(Pool, self).__init__()
self.conv_1 = tfkl.TimeDistributed(
tfkl.Conv2D(256, 2, strides=2, padding='valid', activation=tf.nn.relu)
)
self.block_1 = TimeDistributedResBlock2D((128, 256), 3, strides=1, padding='same', activation=tf.nn.relu)
self.conv_2 = tfkl.TimeDistributed(
tfkl.Conv2D(256, 2, strides=2, padding='valid', activation=tf.nn.relu)
)
self.block_2 = TimeDistributedResBlock2D((128, 256+7), 3, strides=1, padding='same', activation=tf.nn.relu)
self.conv_3 = tfkl.TimeDistributed(
tfkl.Conv2D(256, 3, strides=1, padding='same', activation=tf.nn.relu),
)
self.conv_4 = tfkl.TimeDistributed(
tfkl.Conv2D(256, 1, strides=1, padding='same', activation=tf.nn.relu)
)
self.pool = tfkl.TimeDistributed(tfkl.AveragePooling2D(16))
def call(self, inputs):
x, v = inputs
x = self.conv_1(x)
x = self.block_1(x)
x = self.conv_2(x)
v = tf.expand_dims(tf.expand_dims(v, 2), 2)
v = tf.tile(v, [1, 1, 16, 16, 1])
r = tf.concat([x, v], -1)
r = self.block_2(r)
r = self.conv_3(r)
r = self.conv_4(r)
r = self.pool(r)
return r