def __init__(self, M, N, J, name="scattering"):
super(Scattering, self).__init__(name)
self.M, self.N, self.J = M, N, J
self._prepare_padding_size([1, 1, M, N])
# Create the filters
filters = filters_bank(self.M_padded, self.N_padded, J)
self.Psi = filters['psi']
self.Phi = [filters['phi'][j] for j in range(J)]
def __init__(self, M, N, J, check=False):
super(Scattering, self).__init__()
self.M, self.N, self.J = M, N, J
self.check = check # for tests
self._prepare_padding_size([1, 1, M, N])
# Create the filters
filters = filters_bank(self.M_padded, self.N_padded, J)
self.Psi = filters['psi']
self.Phi = [filters['phi'][j] for j in range(J)]
def __init__(self, M, N, J, check=False):
super(Scattering, self).__init__()
self.M, self.N, self.J = M, N, J
self.check = check # for tests
self._prepare_padding_size([1, 1, M, N])
# Create the filters
filters = filters_bank(self.M_padded, self.N_padded, J)
self.Psi = filters['psi']
self.Phi = [filters['phi'][j] for j in range(J)]
def run_filter_bank(M, N, J):
filters = filters_bank.filters_bank(M, N, J)
d_save = {}
# Save phi
d_save["phi"] = {}
for key in filters["phi"].keys():
val = filters["phi"][key]
if isinstance(val, tf.Tensor):
val_numpy = val.eval(session=tf.Session())
d_save["phi"][key] = val_numpy
# Save psi
d_save["psi"] = []
for elem in filters["psi"]:
d = {}
for key in elem.keys():
val = elem[key]
if isinstance(val, tf.Tensor):
val_numpy = val.eval(session=tf.Session())
d[key] = val_numpy
d_save["psi"].append(d)
return d_save
def run_filter_bank(M, N, J):
filters = filters_bank.filters_bank(M, N, J)
d_save = {}
# Save phi
d_save["phi"] = {}
for key in filters["phi"].keys():
val = filters["phi"][key]
if isinstance(val, tf.Tensor):
val_numpy = val.eval(session=tf.Session())
d_save["phi"][key] = val_numpy
# Save psi
d_save["psi"] = []
for elem in filters["psi"]:
d = {}
for key in elem.keys():
val = elem[key]
if isinstance(val, tf.Tensor):
val_numpy = val.eval(session=tf.Session())
d[key] = val_numpy
d_save["psi"].append(d)
return d_save