def convfn(x, hparams):
return common_layers.subseparable_conv(
x,
hparams.hidden_size // div, (kw, kh),
padding="SAME",
separability=sep,
name="conv_%d%d_sep%d_div%d" % (kw, kh, sep, div))
def convfn(x, hparams):
return common_layers.subseparable_conv(
x,
hparams.hidden_size // div, (kw, kh),
padding="SAME",
separability=sep,
name="conv_%d%d_sep%d_div%d" % (kw, kh, sep, div))
def testSubSeparableConv(self):
for sep in [0, 1, 2, 4]:
x = np.random.rand(5, 7, 1, 12)
with tf.variable_scope("sep_%d" % sep):
y = common_layers.subseparable_conv(
tf.constant(x, dtype=tf.float32), 16, (3, 1), separability=sep)
self.evaluate(tf.global_variables_initializer())
res = self.evaluate(y)
self.assertEqual(res.shape, (5, 5, 1, 16))
def testSubSeparableConv(self):
for sep in [0, 1, 2, 4]:
x = np.random.rand(5, 7, 1, 12)
with tf.variable_scope("sep_%d" % sep):
y = common_layers.subseparable_conv(
tf.constant(x, dtype=tf.float32), 16, (3, 1), separability=sep)
self.evaluate(tf.global_variables_initializer())
res = self.evaluate(y)
self.assertEqual(res.shape, (5, 5, 1, 16))
