def gconvbn(*args, **kwargs):
scope = kwargs.pop('scope', None)
with tf.variable_scope(scope):
x = sconv2d(*args, **kwargs)
c = args[-1]
f = x.shape[-1].value // c
g = f // c
kernel = np.zeros((1, 1, f * c, f), np.float32)
for i in range(f):
start = (i // c) * c * c + i % c
end = start + c * c
kernel[:, :, start:end:c, i] = 1.
x = conv2d_primitive(x,
tf.constant(kernel),
strides=[1, 1, 1, 1],
padding='VALID',
name='gconv')
return batch_norm(x)
def sconvbnswish(*args, **kwargs):
scope = kwargs.pop('scope', None)
with tf.variable_scope(scope):
return swish(batch_norm(sconv2d(*args, **kwargs)))
def sconvbnact(*args, **kwargs):
scope = kwargs.pop('scope', None)
activation_fn = kwargs.pop('activation_fn', None)
with tf.variable_scope(scope):
return activation_fn(batch_norm(sconv2d(*args, **kwargs)))
