from keras import backend as K
from keras.layers import Layer, LSTM, MaxPooling1D
import numpy as np
class TFiLM(Layer):
def __init__(self, block_size, **kwargs):
self.block_size = block_size
super(tFiLM, self).__init__(**kwargs)
def make_normalizer(self, x_in):
""" Pools to downsample along 'temporal' dimension and then
runs LSTM to generate normalization weights.
"""
x_in_down = (MaxPooling1D(pool_size=self.block_size, padding='valid'))(x_in)
x_rnn = self.rnn(x_in_down)
return x_rnn
def apply_normalizer(self, x_in, x_norm):
"""
Applies normalization weights by multiplying them into their respective blocks.
"""
n_blocks = K.shape(x_in)[1] / self.block_size
n_filters = K.shape(x_in)[2]
# reshape input into blocks
x_norm = K.reshape(x_norm, shape=(-1, n_blocks, 1, n_filters))
x_in = K.reshape(x_in, shape=(-1, n_blocks, self.block_size, n_filters))
# multiply
x_out = x_norm * x_in
# return to original shape
x_out = K.reshape(x_out, shape=(-1, n_blocks * self.block_size, n_filters))
return x_out
def build(self, input_shape):
self.rnn = LSTM(units = input_shape[2], return_sequences = True, trainable=True)
self.rnn.build(input_shape)
self._trainable_weights = self.rnn.trainable_weights
super(tFiLM, self).build(input_shape) # Be sure to call this at the end
def call(self, x):
assert len(x.shape) == 3, 'Input should be tensor with dimension \
(batch_size, steps, num_features).'
assert x.shape[1] % self.block_size == 0, 'Number of steps must be a \
multiple of the block size.'
x_norm = self.make_normalizer(x)
x = self.apply_normalizer(x, x_norm)
return x
def compute_output_shape(self, input_shape):
return input_shape
