Reuse nnet.abstract_conv.conv3d as nnet.conv3d.

theano/tensor/nnet/__init__.py

@@ -32,7 +32,7 @@ from .bn import batch_normalization
 
 import warnings
 from .abstract_conv import conv2d as abstract_conv2d
-from .abstract_conv import conv3d as abstract_conv3d
+from .abstract_conv import conv3d
 
 
 def conv2d(input, filters, input_shape=None, filter_shape=None,
@@ -151,120 +151,3 @@ def conv2d(input, filters, input_shape=None, filter_shape=None,
     return abstract_conv2d(input, filters, input_shape, filter_shape,
                            border_mode, subsample, filter_flip,
                            filter_dilation)
-
-
-def conv3d(input, filters, input_shape=None, filter_shape=None,
-           border_mode='valid', subsample=(1, 1, 1), filter_flip=True,
-           image_shape=None, filter_dilation=(1, 1, 1), **kwargs):
-    """
-    This function will build the symbolic graph for convolving a mini-batch of a
-    stack of 3D inputs with a set of 3D filters. The implementation is modelled
-    after Convolutional Neural Networks (CNN).
-
-
-    Parameters
-    ----------
-    input: symbolic 5D tensor
-        Mini-batch of feature map stacks, of shape
-        (batch size, input channels, input depth, input rows, input columns).
-        See the optional parameter ``input_shape``.
-
-    filters: symbolic 5D tensor
-        Set of filters used in CNN layer of shape
-        (output channels, input channels, filter depth, filter rows, filter columns).
-        See the optional parameter ``filter_shape``.
-
-    input_shape: None, tuple/list of len 5 of int or Constant variable
-        The shape of the input parameter.
-        Optional, possibly used to choose an optimal implementation.
-        You can give ``None`` for any element of the list to specify that this
-        element is not known at compile time.
-
-    filter_shape: None, tuple/list of len 5 of int or Constant variable
-        The shape of the filters parameter.
-        Optional, possibly used to choose an optimal implementation.
-        You can give ``None`` for any element of the list to specify that this
-        element is not known at compile time.
-
-    border_mode: str, int or tuple of three int
-        Either of the following:
-
-        ``'valid'``: apply filter wherever it completely overlaps with the
-            input. Generates output of shape: input shape - filter shape + 1
-        ``'full'``: apply filter wherever it partly overlaps with the input.
-            Generates output of shape: input shape + filter shape - 1
-        ``'half'``: pad input with a symmetric border of ``filter // 2``,
-            then perform a valid convolution. For filters with an odd
-            number of slices, rows and columns, this leads to the output
-            shape being equal to the input shape.
-        ``int``: pad input with a symmetric border of zeros of the given
-            width, then perform a valid convolution.
-        ``(int1, int2, int3)``
-            pad input with a symmetric border of ``int1``, ``int2`` and
-            ``int3`` columns, then perform a valid convolution.
-
-    subsample: tuple of len 3
-        Factor by which to subsample the output.
-        Also called strides elsewhere.
-
-    filter_flip: bool
-        If ``True``, will flip the filter x, y and z dimensions before
-        sliding them over the input. This operation is normally
-        referred to as a convolution, and this is the default. If
-        ``False``, the filters are not flipped and the operation is
-        referred to as a cross-correlation.
-
-    image_shape: None, tuple/list of len 5 of int or Constant variable
-        Deprecated alias for input_shape.
-
-    filter_dilation: tuple of len 3
-        Factor by which to subsample (stride) the input.
-        Also called dilation elsewhere.
-
-    kwargs: Any other keyword arguments are accepted for backwards
-            compatibility, but will be ignored.
-
-    Returns
-    -------
-    Symbolic 5D tensor
-        Set of feature maps generated by convolutional layer. Tensor is
-        is of shape (batch size, output channels, output depth,
-        output rows, output columns)
-
-    Notes
-    -----
-        If cuDNN is available, it will be used on the
-        GPU. Otherwise, it is the *Corr3dMM* convolution that will be used
-        "caffe style convolution".
-
-        This is only supported in Theano 0.8 or the development
-        version until it is released.
-
-    """
-
-    if 'imshp_logical' in kwargs or 'kshp_logical' in kwargs:
-        raise ValueError(
-            "Keyword arguments 'imshp_logical' and 'kshp_logical' for conv3d "
-            "are not supported anymore (and have not been a reliable way to "
-            "perform upsampling).")
-    if len(kwargs.keys()) > 0:
-        warnings.warn(str(kwargs.keys()) +
-                      " are now deprecated in "
-                      "`tensor.nnet.abstract_conv.conv3d` interface"
-                      " and will be ignored.",
-                      stacklevel=2)
-
-    if image_shape is not None:
-        warnings.warn("The `image_shape` keyword argument to "
-                      "`tensor.nnet.conv3d` is deprecated, it has been "
-                      "renamed to `input_shape`.",
-                      stacklevel=2)
-        if input_shape is None:
-            input_shape = image_shape
-        else:
-            raise ValueError("input_shape and image_shape should not"
-                             " be provided at the same time.")
-
-    return abstract_conv3d(input, filters, input_shape, filter_shape,
-                           border_mode, subsample, filter_flip,
-                           filter_dilation)

theano/tensor/nnet/abstract_conv.py

@@ -171,12 +171,84 @@ def conv3d(input,
            subsample=(1, 1, 1),
            filter_flip=True,
            filter_dilation=(1, 1, 1)):
-    """This function will build the symbolic graph for convolving a mini-batch of a
+    """
+    This function will build the symbolic graph for convolving a mini-batch of a
     stack of 3D inputs with a set of 3D filters. The implementation is modelled
     after Convolutional Neural Networks (CNN).
 
-    TODO
-    Refer to :func:`nnet.conv3d <theano.tensor.nnet.conv2d>` for a more detailed documentation.
+
+    Parameters
+    ----------
+    input: symbolic 5D tensor
+        Mini-batch of feature map stacks, of shape
+        (batch size, input channels, input depth, input rows, input columns).
+        See the optional parameter ``input_shape``.
+
+    filters: symbolic 5D tensor
+        Set of filters used in CNN layer of shape
+        (output channels, input channels, filter depth, filter rows, filter columns).
+        See the optional parameter ``filter_shape``.
+
+    input_shape: None, tuple/list of len 5 of int or Constant variable
+        The shape of the input parameter.
+        Optional, possibly used to choose an optimal implementation.
+        You can give ``None`` for any element of the list to specify that this
+        element is not known at compile time.
+
+    filter_shape: None, tuple/list of len 5 of int or Constant variable
+        The shape of the filters parameter.
+        Optional, possibly used to choose an optimal implementation.
+        You can give ``None`` for any element of the list to specify that this
+        element is not known at compile time.
+
+    border_mode: str, int or tuple of three int
+        Either of the following:
+
+        ``'valid'``: apply filter wherever it completely overlaps with the
+            input. Generates output of shape: input shape - filter shape + 1
+        ``'full'``: apply filter wherever it partly overlaps with the input.
+            Generates output of shape: input shape + filter shape - 1
+        ``'half'``: pad input with a symmetric border of ``filter // 2``,
+            then perform a valid convolution. For filters with an odd
+            number of slices, rows and columns, this leads to the output
+            shape being equal to the input shape.
+        ``int``: pad input with a symmetric border of zeros of the given
+            width, then perform a valid convolution.
+        ``(int1, int2, int3)``
+            pad input with a symmetric border of ``int1``, ``int2`` and
+            ``int3`` columns, then perform a valid convolution.
+
+    subsample: tuple of len 3
+        Factor by which to subsample the output.
+        Also called strides elsewhere.
+
+    filter_flip: bool
+        If ``True``, will flip the filter x, y and z dimensions before
+        sliding them over the input. This operation is normally
+        referred to as a convolution, and this is the default. If
+        ``False``, the filters are not flipped and the operation is
+        referred to as a cross-correlation.
+
+    filter_dilation: tuple of len 3
+        Factor by which to subsample (stride) the input.
+        Also called dilation elsewhere.
+
+    Returns
+    -------
+    Symbolic 5D tensor
+        Set of feature maps generated by convolutional layer. Tensor is
+        is of shape (batch size, output channels, output depth,
+        output rows, output columns)
+
+    Notes
+    -----
+        If cuDNN is available, it will be used on the
+        GPU. Otherwise, it is the *Corr3dMM* convolution that will be used
+        "caffe style convolution".
+
+        This is only supported in Theano 0.8 or the development
+        version until it is released.
+
     """
 
     input = as_tensor_variable(input)