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resblock.py
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resblock.py
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import tensorflow as tf
tfkl = tf.keras.layers
class TimeDistributedResBlock2D(tfkl.Layer):
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='same',
data_format=None,
dilation_rate=(1, 1),
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(TimeDistributedResBlock2D, self).__init__()
filters1, filters2 = filters
self.conv2a = tfkl.TimeDistributed(
tfkl.Conv2D(filters=filters1,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=tf.nn.relu,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
**kwargs)
)
self.conv2b = tfkl.TimeDistributed(
tfkl.Conv2D(filters=filters2,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
**kwargs)
)
self.activation = activation
def call(self, inputs):
x = self.conv2a(inputs)
x = self.conv2b(x)
return self.activation(inputs + x)