Merge commit '70d68574'

src/pystencils_autodiff/__init__.py

@@ -4,8 +4,8 @@ import pystencils_autodiff.backends  # NOQA
 from pystencils_autodiff._field_to_tensors import (  # NOQA
     tf_constant_from_field, tf_placeholder_from_field, tf_scalar_variable_from_field,
     tf_variable_from_field, torch_tensor_from_field)
-from pystencils_autodiff.adjoint_field import AdjointField
-from pystencils_autodiff.autodiff import (
+from pystencils_autodiff._adjoint_field import AdjointField
+from pystencils_autodiff._autodiff import (
     AutoDiffAstPair, AutoDiffOp, create_backward_assignments, get_jacobian_of_assignments)
 
 __all__ = ['backends',

src/pystencils_autodiff/tensorflow.py

-import numpy as np
-
-try:
-    import tensorflow as tf
-except ImportError:
-    pass
-
-
-def tf_constant_from_field(field, init_val=0):
-    return tf.constant(init_val, dtype=field.dtype.numpy_dtype, shape=field.shape, name=field.name + '_constant')
-
-
-def tf_scalar_variable_from_field(field, init_val, constraint=None):
-    var = tf.Variable(init_val, dtype=field.dtype.numpy_dtype, name=field.name + '_variable', constraint=constraint)
-    return var * tf_constant_from_field(field, 1)
-
-
-def tf_variable_from_field(field, init_val=0, constraint=None):
-    if isinstance(init_val, (int, float)):
-        init_val *= np.ones(field.shape, field.dtype.numpy_dtype)
-
-    return tf.Variable(init_val, dtype=field.dtype.numpy_dtype, name=field.name + '_variable', constraint=constraint)
-
-
-def tf_placeholder_from_field(field):
-    return tf.placeholder(dtype=field.dtype.numpy_dtype, name=field.name + '_placeholder', shape=field.shape)

src/pystencils_autodiff/torch.py

-# -*- coding: utf-8 -*-
-#
-# Copyright © 2019 Stephan Seitz <stephan.seitz@fau.de>
-#
-# Distributed under terms of the GPLv3 license.
-
-"""
-
-"""
-
-import numpy as np
-
-try:
-    import torch
-except ImportError:
-    pass
-
-
-def torch_tensor_from_field(field, init_val=0, cuda=True, requires_grad=False):
-    if isinstance(init_val, (int, float)):
-        init_val *= np.ones(field.shape, field.dtype.numpy_dtype)
-    device = torch.device('cuda' if cuda else 'cpu')
-    return torch.tensor(init_val, requires_grad=requires_grad, device=device)