def __retrieve_np_exmachina():
if not hasExmachina:
raise RuntimeError("Requested npExmachina but ExMachina not compiled.")
import numpy as np
# Define the exmachina numpy constants
return npConstants(useFortran=True, fp_dtype=np.float64, int_dtype=np.int64)
def __retrieve_np_fastnet():
if not hasFastnet:
raise RuntimeError("Requested npFastnet but FastNet not compiled.")
import numpy as np
# Define the fastnet numpy constants
return npConstants(useFortran=False, fp_dtype=np.float32, int_dtype=np.int32)
def numpy_wrapper(self):
"""
Returns the api instance which is to be used to read the data
"""
import numpy as np
if self.core is TuningToolCores.ExMachina:
# Define the exmachina numpy constants
kwargs = {
'useFortran': True,
'fp_dtype': np.float64,
'int_dtype': np.int64
}
elif self.core is TuningToolCores.FastNet:
kwargs = {
'useFortran': False,
'fp_dtype': np.float32,
'int_dtype': np.int32
}
elif self.core is TuningToolCores.keras:
from keras.backend import backend
if backend(
) == "theano": # Theano copies data if input is not c-contiguous
kwargs = {
'useFortran': False,
'fp_dtype': np.float32,
'int_dtype': np.int32
}
elif backend(
) == "tensorflow": # tensorflow copies data if input is not fortran-contiguous
kwargs = {
'useFortran': False,
'fp_dtype': np.float32,
'int_dtype': np.int32
}
return npConstants(**kwargs)