"""Unit tests for the causal_conv op."""
import numpy as np
import tensorflow as tf
from wavenet import time_to_batch, batch_to_time, causal_conv
class TestCausalConv(tf.test.TestCase):
def testCausalConv(self):
"""Tests that the op is equivalent to a numpy implementation."""
x1 = np.arange(1, 21, dtype=np.float32)
x = np.append(x1, x1)
x = np.reshape(x, [2, 20, 1])
f = np.reshape(np.array([1, 1], dtype=np.float32), [2, 1, 1])
out = causal_conv(x, f, 4)
with self.test_session() as sess:
result = sess.run(out)
# Causal convolution using numpy
ref = np.convolve(x1, [1, 0, 0, 0, 1], mode='valid')
ref = np.append(ref, ref)
ref = np.reshape(ref, [2, 16, 1])
self.assertAllEqual(result, ref)
def testNoTimeShift(self):
"""Tests that the convolution does not introduce a time shift.
We give it a time series, choose a filter that should be the identity,
and assert that the output is not shifted at all relative to the input.
"""
# Input to filter is a time series of values 1..10
x = np.arange(1, 11, dtype=np.float32)
# Reshape the input: shape is batch item x duration x channels = 1x10x1
x = np.reshape(x, [1, 10, 1])
# Default shape ordering for conv filter = HWIO for 2d. Since we use
# 1d, this just becomes WxIxO where:
# W = width AKA number of time steps in time series = 2
# I = input channels = 1
# O = output channels = 1
# Since the filter is size 2, for it to be identity-preserving, one
# value is 1.0, the other 0.0
filter = np.reshape(np.array([0.0, 1.0], dtype=np.float32), [2, 1, 1])
x_padded = np.pad(x, [[0, 0], [2, 0], [0, 0]], 'constant')
# Compute the output
out = causal_conv(x_padded, filter, dilation=2)
with self.test_session() as sess:
result = sess.run(out)
# The shapes should be the same.
self.assertAllEqual(result.shape, x.shape)
# The output time series should be identical to the input series.
self.assertAllEqual(result, x)
if __name__ == '__main__':
tf.test.main()
