def inverse(self,
x,
objective,
yy=None,
nlf0=None,
nlf1=None,
iso=None,
cam=None):
z = x
squeeze_factor = self.hps.squeeze_factor
for i in range(self.n_levels):
z = squeeze2d(z, squeeze_factor, self.hps.squeeze_type)
if yy is not None:
yy = squeeze2d(yy, squeeze_factor, self.hps.squeeze_type)
for bijector in self.model[i]:
if type(bijector) in [
AffineCouplingCondY, AffineCouplingCondXY,
AffineCouplingFitSdnGain2, AffineCouplingCondYG,
AffineCouplingCamSdn, AffineCouplingCondXYG,
AffineCouplingSdnGain, AffineCouplingSdn,
AffineCouplingGain, AffineCouplingGainEx1,
AffineCouplingGainEx2, AffineCouplingGainEx3,
AffineCouplingSdnEx1, AffineCouplingSdnEx2,
AffineCouplingSdnEx3, AffineCouplingSdnEx4,
AffineCouplingGainEx4, AffineCouplingSdnEx5,
AffineCouplingSdnEx6
]:
try:
z, log_abs_det_J_inv = \
bijector._inverse_and_log_det_jacobian(z, yy, nlf0, nlf1, iso, cam)
except Exception as e:
print(e)
z = bijector._inverse(z, yy, nlf0, nlf1, iso, cam)
log_abs_det_J_inv = bijector._inverse_log_det_jacobian(
z, yy, nlf0, nlf1, iso, cam)
else:
try:
z, log_abs_det_J_inv = \
bijector._inverse_and_log_det_jacobian(z)
except Exception as e:
print(e)
z = bijector._inverse(z)
log_abs_det_J_inv = bijector._inverse_log_det_jacobian(
z)
objective += log_abs_det_J_inv
if i < self.n_levels - 1:
z, objective = split2d("pool{}".format(i), z, objective)
return z, objective
def _forward_and_log_det_jacobian(self,
x,
yy,
nlf0=None,
nlf1=None,
iso=None,
cam=None):
if self._last_layer:
x = tf.reshape(x, (-1, self.i0, self.i1, self.ic))
yy = tf.reshape(yy, (-1, self.i0, self.i1, self.ic))
if 2 * x.shape[1] == yy.shape[1]:
yy = squeeze2d(yy, 2)
x0 = x[:, :, :, :self.ic // 2]
x1 = x[:, :, :, self.ic // 2:]
x0yy = tf.concat([x0, yy], axis=-1)
shift, log_scale = self._shift_and_log_scale_fn(x0yy, iso)
log_scale = self.scale * tf.tanh(log_scale)
y1 = x1
if shift is not None:
y1 -= shift
if log_scale is not None:
y1 *= tf.exp(-log_scale)
y = tf.concat([x0, y1], axis=-1)
if log_scale is None:
log_abs_det_J = tf.constant(0., dtype=x.dtype, name="fldj")
else:
log_abs_det_J = -tf.reduce_sum(log_scale, axis=[1, 2, 3])
return y, log_abs_det_J
def _forward(self, x, yy, nlf0=None, nlf1=None, iso=None, cam=None):
if self._last_layer:
x = tf.reshape(x, (-1, self.i0, self.i1, self.ic))
yy = tf.reshape(yy, (-1, self.i0, self.i1, self.ic))
if yy.shape[1] == 2 * x.shape[1]: # needs squeezing
yy = squeeze2d(yy, 2)
shift, log_scale = self._shift_and_log_scale_fn(yy, iso)
log_scale = self.scale * tf.tanh(log_scale)
y = x # x[:, :, :, self.ic // 2:]
if shift is not None:
y -= shift
if log_scale is not None:
y *= tf.exp(-log_scale)
return y
def _forward(self, x, yy, nlf0=None, nlf1=None, iso=None, cam=None):
if self._last_layer:
x = tf.reshape(x, (-1, self.i0, self.i1, self.ic))
yy = tf.reshape(yy, (-1, self.i0, self.i1, self.ic))
if yy.shape[1] == 2 * x.shape[1]: # needs squeezing
yy = squeeze2d(yy, 2)
scale = sdn_model_params_ex4(yy, iso, self.gain_init)
shift = 0.0
y = x
if scale is not None:
y *= scale
if shift is not None:
y += shift
return y
def _forward(self, x, yy, nlf0=None, nlf1=None, iso=None, cam=None):
if self._last_layer:
x = tf.reshape(x, (-1, self.i0, self.i1, self.ic))
yy = tf.reshape(yy, (-1, self.i0, self.i1, self.ic))
if yy.shape[1] == 2 * x.shape[1]: # needs squeezing
yy = squeeze2d(yy, 2)
beta1, beta2 = sdn_iso_model_params_3(iso)
scale = tf.sqrt(beta1 * yy + beta2)
shift = 0.0
y = x
if scale is not None:
y *= scale
if shift is not None:
y += shift
return y
def _forward_log_det_jacobian(self,
x,
yy,
nlf0=None,
nlf1=None,
iso=None,
cam=None):
if self._last_layer:
x = tf.reshape(x, (-1, self.i0, self.i1, self.ic))
yy = tf.reshape(yy, (-1, self.i0, self.i1, self.ic))
if 2 * x.shape[1] == yy.shape[1]:
yy = squeeze2d(yy, 2)
x0 = x[:, :, :, :self.ic // 2]
x0yy = tf.concat([x0, yy], axis=-1)
_, log_scale = self._shift_and_log_scale_fn(x0yy, iso)
log_scale = self.scale * tf.tanh(log_scale)
if log_scale is None:
return tf.constant(0., dtype=x.dtype, name="fldj")
return -tf.reduce_sum(log_scale, axis=[1, 2, 3])
def _forward(self, x, yy, nlf0=None, nlf1=None, iso=None, cam=None):
# print('_forward-------')
# import pdb
# pdb.set_trace()
if self._last_layer:
x = tf.reshape(x, (-1, self.i0, self.i1, self.ic))
yy = tf.reshape(yy, (-1, self.i0, self.i1, self.ic))
if 2 * x.shape[1] == yy.shape[1]:
yy = squeeze2d(yy, 2)
x0 = x[:, :, :, :self.ic // 2]
x1 = x[:, :, :, self.ic // 2:]
x0yy = tf.concat([x0, yy], axis=-1)
shift, log_scale = self._shift_and_log_scale_fn(x0yy, iso)
log_scale = self.scale * tf.tanh(log_scale)
y1 = x1
if shift is not None:
y1 -= shift
if log_scale is not None:
y1 *= tf.exp(-log_scale)
y = tf.concat([x0, y1], axis=-1)
return y
