def conv1d(x,
num_filters,
filter_length,
name,
dilation=1,
causal=True,
kernel_initializer=tf.uniform_unit_scaling_initializer(1.0),
biases_initializer=tf.constant_initializer(0.0)):
"""Fast 1D convolution that supports causal padding and dilation.
Args:
x: The [mb, time, channels] float tensor that we convolve.
num_filters: The number of filter maps in the convolution.
filter_length: The integer length of the filter.
name: The name of the scope for the variables.
dilation: The amount of dilation.
causal: Whether or not this is a causal convolution.
kernel_initializer: The kernel initialization function.
biases_initializer: The biases initialization function.
Returns:
y: The output of the 1D convolution.
"""
batch_size, length, num_input_channels = x.get_shape().as_list()
assert length % dilation == 0
kernel_shape = [1, filter_length, num_input_channels, num_filters]
strides = [1, 1, 1, 1]
biases_shape = [num_filters]
padding = 'VALID' if causal else 'SAME'
with tf.variable_scope(name):
weights = tf.get_variable(
'W', shape=kernel_shape, initializer=kernel_initializer)
biases = tf.get_variable(
'biases', shape=biases_shape, initializer=biases_initializer)
x_ttb = time_to_batch(x, dilation)
if filter_length > 1 and causal:
x_ttb = tf.pad(x_ttb, [[0, 0], [filter_length - 1, 0], [0, 0]])
x_ttb_shape = x_ttb.get_shape().as_list()
x_4d = tf.reshape(x_ttb, [x_ttb_shape[0], 1,
x_ttb_shape[1], num_input_channels])
y = tf.nn.conv2d(x_4d, weights, strides, padding=padding)
y = tf.nn.bias_add(y, biases)
y_shape = y.get_shape().as_list()
y = tf.reshape(y, [y_shape[0], y_shape[2], num_filters])
y = batch_to_time(y, dilation)
y.set_shape([batch_size, length, num_filters])
return y
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