def holo_complex_transform(self):
# Transform X_train and X_test using hologram filtering.
(X_train, y_train), (X_test, y_test) = self.Org
print('Performing complex hologram transformation...')
sim = recon.Simulator(X_train.shape[1:])
def holo(X_train):
X_train_holo_abs_l = []
X_train_holo_ang_l = []
for x in X_train:
X_train_h = sim.diffract_full(x)
X_train_holo_abs_l.append(np.abs(X_train_h))
X_train_holo_ang_l.append(np.angle(X_train_h))
X_train_holo = np.zeros(
(X_train.shape[0], 2, X_train.shape[1], X_train.shape[2]))
X_train_holo[:, 0, :, :] = np.array(X_train_holo_abs_l)
X_train_holo[:, 1, :, :] = np.array(X_train_holo_ang_l)
return X_train_holo
X_train_holo = holo(X_train)
X_test_holo = holo(X_test)
self.Data = (X_train_holo, y_train), (X_test_holo, y_test)
self.Holo_complex = self.Data
self.complex_flag = True
def _holo_transform_r0(self):
# Transform X_train and X_test using hologram filtering.
(X_train, y_train), (X_test, y_test) = self.Org
print('Performing hologram transformation...')
sim = recon.Simulator(X_train.shape[1:])
X_train_holo = np.array([sim.diffract(x) for x in X_train])
X_test_holo = np.array([sim.diffract(x) for x in X_test])
self.Data = (X_train_holo, y_train), (X_test_holo, y_test)
self.Holo = self.Data
def holo_transform(Org):
# Transform X_train and X_test using hologram filtering.
(X_train, dump_train), (X_test, dump_test) = Org
print('Performing hologram transformation...')
sim = recon.Simulator(X_train.shape[1:])
X_train_holo = np.array([sim.diffract(x) for x in X_train])
X_test_holo = np.array([sim.diffract(x) for x in X_test])
Data = (X_train_holo, dump_train), (X_test_holo, dump_test)
return Data
def recon_transform(Holo):
"""
One-shot Recon with Hologram Image
"""
(X_train_holo, dump_train), (X_test_holo, dump_test) = Holo
print('Performing first-shot recon...')
sim = recon.Simulator(X_train_holo.shape[1:])
X_train_recon = np.array(
[np.abs(sim.reconstruct(x)) for x in X_train_holo])
X_test_recon = np.array([np.abs(sim.reconstruct(x)) for x in X_test_holo])
Data = (X_train_recon, dump_train), (X_test_recon, dump_test)
return Data
def _recon_transform_r0(self):
if not hasattr(self, 'Holo'):
self.holo_transform()
(X_train_holo, y_train), (X_test_holo, y_test) = self.Holo
print('Performing first-shot recon...')
sim = recon.Simulator(X_train_holo.shape[1:])
X_train_recon = np.array(
[np.abs(sim.reconstruct(x)) for x in X_train_holo])
X_test_recon = np.array(
[np.abs(sim.reconstruct(x)) for x in X_test_holo])
self.Data = (X_train_recon, y_train), (X_test_recon, y_test)
self.Recon = self.Data
def holo_complex_transform(Org):
(X_train, y_train), (X_test, y_test) = Org
print('Performing complex hologram transformation...')
sim = recon.Simulator(X_train.shape[1:])
def holo(X_train):
X_train_holo_abs_l = []
X_train_holo_ang_l = []
for x in X_train:
X_train_h = sim.diffract_full(x)
X_train_holo_abs_l.append(np.abs(X_train_h))
X_train_holo_ang_l.append(np.angle(X_train_h))
X_train_holo = np.zeros(
(X_train.shape[0], 2, X_train.shape[1], X_train.shape[2]))
X_train_holo[:, 0, :, :] = np.array(X_train_holo_abs_l)
X_train_holo[:, 1, :, :] = np.array(X_train_holo_ang_l)
return X_train_holo
X_train_holo = holo(X_train)
X_test_holo = holo(X_test)
Data = (X_train_holo, y_train), (X_test_holo, y_test)
return Data