def tdnn_block(self, inputs):
''' TDNN layers. '''
if 'tdnn_method' in self.netconf:
tdnn_method = self.netconf['tdnn_method']
else:
# Runs faster, support discrete context, for now.
tdnn_method = 'splice_layer'
tdnn_contexts = self.netconf['tdnn_contexts']
logging.info("tdnn_contexts : {}".format(tdnn_contexts))
tdnn_dims = self.netconf['tdnn_dims']
logging.info("tdnn_dims : {}".format(tdnn_dims))
layer_num = len(tdnn_contexts)
assert layer_num == len(tdnn_dims)
channels = [self.input_channels] + tdnn_dims
logging.info("tdnn_channels : {}".format(channels))
input_h_t = tf.shape(inputs)[1]
input_w = inputs.shape[2]
input_c = inputs.shape[3]
if tdnn_method == 'conv1d':
# NHWC -> NW'C, W' = H * W
inputs = tf.reshape(inputs, [-1, input_h_t * input_w, input_c])
last_w = channels[0]
else:
inputs = tf.reshape(inputs, [-1, input_h_t, input_w * input_c])
last_w = input_w * input_c
downsample_input_len = self.input_len
with tf.variable_scope('tdnn'):
x = tf.identity(inputs)
for index in range(layer_num):
unit_name = 'unit-' + str(index + 1)
with tf.variable_scope(unit_name):
tdnn_name = 'tdnn-' + str(index + 1)
x = common_layers.tdnn(x,
tdnn_name,
last_w,
tdnn_contexts[index],
channels[index + 1],
has_bias=True,
method=tdnn_method)
last_w = channels[index + 1]
x = tf.nn.relu(x)
if self.netconf['use_bn']:
bn_name = 'bn' + str(index + 1)
x = tf.layers.batch_normalization(x,
axis=-1,
momentum=0.9,
training=self.train,
name=bn_name)
if self.netconf['use_dropout']:
x = tf.layers.dropout(x,
self.netconf['dropout_rate'],
training=self.train)
downsample_input_len = downsample_input_len
return x, downsample_input_len
def tdnn_block(self, inputs):
''' TDNN layers. '''
tdnn_contexts = self.netconf['tdnn_contexts']
logging.info("tdnn_contexts : {}".format(tdnn_contexts))
tdnn_dims = self.netconf['tdnn_dims']
logging.info("tdnn_dims : {}".format(tdnn_dims))
layer_num = len(tdnn_contexts)
assert layer_num == len(tdnn_dims)
channels = [self.input_channels] + tdnn_dims
logging.info("tdnn_channels : {}".format(channels))
# NHWC -> NW'C, W' = H * W
input_n, input_h, input_w, input_c = inputs.shape.as_list()
inputs = tf.reshape(inputs, [-1, input_h * input_w, input_c])
downsample_input_len = self.input_len
with tf.variable_scope('tdnn'):
x = tf.identity(inputs)
for index in range(layer_num):
unit_name = 'unit-' + str(index + 1)
with tf.variable_scope(unit_name):
tdnn_name = 'tdnn-' + str(index + 1)
use_bn = self.netconf['use_bn']
has_bias = not use_bn
x = common_layers.tdnn(x,
tdnn_name,
channels[index],
tdnn_contexts[index],
channels[index + 1],
has_bias=has_bias)
x = tf.nn.relu(x)
if self.netconf['use_bn']:
bn_name = 'bn' + str(index + 1)
x = tf.layers.batch_normalization(x,
axis=-1,
momentum=0.9,
training=self.train,
name=bn_name)
if self.netconf['use_dropout']:
x = tf.layers.dropout(x,
self.netconf['dropout_rate'],
training=self.train)
downsample_input_len = downsample_input_len
return x, downsample_input_len
