def predict_second_order(self,
obj_v: tf.Variable,
probe_v: tf.Variable,
position_indices_t: tf.Tensor,
scope_name: str = "") -> tf.Tensor:
scope_name = scope_name + "_predict" if scope_name else "predict"
ndiffs = position_indices_t.get_shape().as_list()[0]
with tf.name_scope(scope_name) as scope:
if ndiffs == 0:
return tf.zeros(shape=[], dtype='float32', name="scope")
obj_cmplx_t = self._reshapeComplexVariable(obj_v, self._obj.shape)
obj_w_border_t = tf.pad(obj_cmplx_t,
self._obj.border_shape,
constant_values=self._obj.border_const)
probe_cmplx_t = self._reshapeComplexVariable(
probe_v, self._probe.shape)
batch_obj_views_t = self._getPtychoObjViewStack(
obj_w_border_t, probe_cmplx_t, position_indices_t)
batch_obj_views_t = fftshift_t(batch_obj_views_t)
exit_waves_t = batch_obj_views_t * probe_cmplx_t
farfield_waves_t = propFF_t(exit_waves_t)
return tf.reshape(
tf.stack(
[tf.real(farfield_waves_t),
tf.imag(farfield_waves_t)]), [-1])
def predict_second_order(self,
joint_v: tf.Variable,
position_indices_t: tf.Tensor,
scope_name: str = "") -> tf.Tensor:
scope_name = scope_name + "_predict" if scope_name else "predict"
ndiffs = position_indices_t.get_shape().as_list()[0]
with tf.name_scope(scope_name) as scope:
if ndiffs == 0:
return tf.zeros(shape=[], dtype=self._dtype, name="scope")
joint_t = self._reshapeComplexVariable(joint_v,
self._joint_array_shape)
obj_t_flat = joint_t[:self._obj.array.size]
probe_t_flat = joint_t[self._obj.array.size:]
obj_cmplx_t = tf.reshape(obj_t_flat, self._obj.shape)
probe_cmplx_t = tf.reshape(probe_t_flat, self._probe.shape)
obj_w_border_t = tf.pad(obj_cmplx_t,
self._obj.border_shape,
constant_values=self._obj.border_const)
batch_obj_views_t = self._getPtychoObjViewStack(
obj_w_border_t, probe_cmplx_t, position_indices_t)
batch_obj_views_t = fftshift_t(batch_obj_views_t)
exit_waves_t = batch_obj_views_t * probe_cmplx_t
out_wavefronts_t = propFF_t(exit_waves_t)
amplitudes_t = tf.abs(out_wavefronts_t)
if self.upsampling_factor > 1:
amplitudes_t = self._downsample(amplitudes_t)
return tf.reshape(amplitudes_t, [-1])
def predict(self,
position_indices_t: tf.Tensor,
scope_name: str = "") -> tf.Tensor:
ndiffs = position_indices_t.get_shape().as_list()[0]
scope_name = scope_name + "_predict" if scope_name else "predict"
with tf.name_scope(scope_name) as scope:
if ndiffs == 0:
return tf.zeros(shape=[], dtype=self._dtype, name=scope)
batch_rc_positions_indices = tf.gather(
self._full_rc_positions_indices_t, position_indices_t)
batch_obj_views_t = tf.gather(self._obj_views_all_t,
batch_rc_positions_indices[:, 1])
batch_phase_modulations_t = tf.gather(
self._probe_phase_modulations_all_t,
batch_rc_positions_indices[:, 0])
batch_obj_views_t = batch_obj_views_t
exit_waves_t = batch_obj_views_t * self.probe_cmplx_t * batch_phase_modulations_t
exit_waves_proj_t = fftshift_t(tf.reduce_sum(exit_waves_t,
axis=-3))
out_wavefronts_t = propFF_t(exit_waves_proj_t)
amplitudes_t = tf.abs(out_wavefronts_t)
if self.upsampling_factor > 1:
amplitudes_t = self._downsample(amplitudes_t)
return tf.reshape(amplitudes_t, [-1], name=scope)
def predict(self, position_indices_t: tf.Tensor):
ndiffs = position_indices_t.get_shape().as_list()[0]
if ndiffs == 0:
return tf.zeros(shape=[], dtype='float32')
batch_obj_views_t = tf.gather(self._obj_views_all_t, position_indices_t)
batch_obj_views_t = fftshift_t(batch_obj_views_t)
exit_waves_t = batch_obj_views_t * self.probe_cmplx_t
out_wavefronts_t = propFF_t(exit_waves_t)
amplitudes_t = tf.abs(out_wavefronts_t)
if self.upsampling_factor > 1:
amplitudes_t = self._downsample(amplitudes_t)
return amplitudes_t
def predict(self,
position_indices_t: tf.Tensor,
scope_name: str = "") -> tf.Tensor:
scope_name = scope_name + "_predict" if scope_name else "predict"
ndiffs = position_indices_t.get_shape().as_list()[0]
with tf.name_scope(scope_name) as scope:
if ndiffs == 0:
return tf.zeros(shape=[], dtype='complex64', name=scope)
batch_obj_views_t = tf.gather(self._obj_views_all_t,
position_indices_t)
batch_obj_views_t = fftshift_t(batch_obj_views_t)
exit_waves_t = batch_obj_views_t * self.probe_cmplx_t
farfield_waves_t = propFF_t(exit_waves_t)
return farfield_waves_t #tf.reshape(tf.stack([tf.real(farfield_waves_t), tf.imag(farfield_waves_t)]), [-1])
def predict(self, position_indices_t: tf.Tensor):
ndiffs = position_indices_t.get_shape().as_list()[0]
if ndiffs == 0:
return tf.zeros(shape=[], dtype='float32')
batch_rc_positions_indices = tf.gather(self._full_rc_positions_indices_t, position_indices_t)
batch_obj_views_t = tf.gather(self._obj_views_all_t, batch_rc_positions_indices[:, 1])
batch_phase_modulations_t = tf.gather(self._probe_phase_modulations_all_t, batch_rc_positions_indices[:, 0])
batch_obj_views_t = batch_obj_views_t
exit_waves_t = batch_obj_views_t * self.probe_cmplx_t * batch_phase_modulations_t
exit_waves_proj_t = fftshift_t(tf.reduce_sum(exit_waves_t, axis=-3))
out_wavefronts_t = propFF_t(exit_waves_proj_t)
amplitudes_t = tf.abs(out_wavefronts_t)
if self.upsampling_factor > 1:
amplitudes_t = self._downsample(amplitudes_t)
return amplitudes_t
def predict(self,
position_indices_t: tf.Tensor,
scope_name: str = "") -> tf.Tensor:
scope_name = scope_name + "_predict" if scope_name else "predict"
ndiffs = position_indices_t.get_shape().as_list()[0]
with tf.name_scope(scope_name) as scope:
if ndiffs == 0:
return tf.zeros(shape=[], dtype=self._dtype, name=scope)
batch_obj_views_t = tf.gather(self._obj_views_all_t,
position_indices_t,
name="test_gather")
batch_obj_views_t = fftshift_t(batch_obj_views_t)
exit_waves_t = batch_obj_views_t * self.probe_cmplx_t
out_wavefronts_t = propFF_t(exit_waves_t)
amplitudes_t = tf.abs(out_wavefronts_t)
if self.upsampling_factor > 1:
amplitudes_t = self._downsample(amplitudes_t)
return tf.reshape(amplitudes_t, [-1], name=scope)
def predict_second_order(self,
obj_v: tf.Variable,
probe_v: tf.Variable,
position_indices_t: tf.Tensor,
scope_name: str = "") -> tf.Tensor:
ndiffs = position_indices_t.get_shape().as_list()[0]
scope_name = scope_name + "_predict" if scope_name else "predict"
with tf.name_scope(scope_name) as scope:
if ndiffs == 0:
return tf.zeros(shape=[], dtype=self._dtype, name=scope)
obj_cmplx_t = self._reshapeComplexVariable(obj_v, self._obj.shape)
obj_w_border_t = tf.pad(obj_cmplx_t,
self._obj.border_shape,
constant_values=self._obj.border_const)
probe_cmplx_t = self._reshapeComplexVariable(
probe_v, self._probe.shape)
batch_rc_positions_indices = tf.gather(
self._full_rc_positions_indices_t, position_indices_t)
batch_obj_views_t = self._getPtychoObjViewStack(
obj_w_border_t, probe_cmplx_t,
batch_rc_positions_indices[:, 1]) #position_indices_t)
batch_phase_modulations_t = tf.gather(
self._probe_phase_modulations_all_t,
batch_rc_positions_indices[:, 0])
batch_obj_views_t = batch_obj_views_t
exit_waves_t = batch_obj_views_t * self.probe_cmplx_t * batch_phase_modulations_t
exit_waves_proj_t = fftshift_t(tf.reduce_sum(exit_waves_t,
axis=-3))
out_wavefronts_t = propFF_t(exit_waves_proj_t)
amplitudes_t = tf.abs(out_wavefronts_t)
if self.upsampling_factor > 1:
amplitudes_t = self._downsample(amplitudes_t)
return tf.reshape(amplitudes_t, [-1], name=scope)
