Merge pull request #4475 from chainer/revert-4025-add-conv1d-conv3d

Revert "Add alias for conv1d and conv3d"

chainer/functions/__init__.py

@@ -105,12 +105,8 @@
 
 from chainer.functions.connection.bilinear import bilinear  # NOQA
 from chainer.functions.connection.convolution_2d import convolution_2d  # NOQA
-from chainer.functions.connection.convolution_nd import convolution_1d  # NOQA
-from chainer.functions.connection.convolution_nd import convolution_3d  # NOQA
 from chainer.functions.connection.convolution_nd import convolution_nd  # NOQA
 from chainer.functions.connection.deconvolution_2d import deconvolution_2d  # NOQA
-from chainer.functions.connection.deconvolution_nd import deconvolution_1d  # NOQA
-from chainer.functions.connection.deconvolution_nd import deconvolution_3d  # NOQA
 from chainer.functions.connection.deconvolution_nd import deconvolution_nd  # NOQA
 from chainer.functions.connection.depthwise_convolution_2d import depthwise_convolution_2d  # NOQA
 from chainer.functions.connection.dilated_convolution_2d import dilated_convolution_2d  # NOQA
@@ -293,23 +289,17 @@
 
 from chainer.functions.pooling.average_pooling_2d import average_pooling_2d  # NOQA
 from chainer.functions.pooling.average_pooling_2d import AveragePooling2D  # NOQA
-from chainer.functions.pooling.average_pooling_nd import average_pooling_1d  # NOQA
-from chainer.functions.pooling.average_pooling_nd import average_pooling_3d  # NOQA
 from chainer.functions.pooling.average_pooling_nd import average_pooling_nd  # NOQA
 from chainer.functions.pooling.average_pooling_nd import AveragePoolingND  # NOQA
 from chainer.functions.pooling.max_pooling_2d import max_pooling_2d  # NOQA
 from chainer.functions.pooling.max_pooling_2d import MaxPooling2D  # NOQA
-from chainer.functions.pooling.max_pooling_nd import max_pooling_1d  # NOQA
-from chainer.functions.pooling.max_pooling_nd import max_pooling_3d  # NOQA
 from chainer.functions.pooling.max_pooling_nd import max_pooling_nd  # NOQA
 from chainer.functions.pooling.max_pooling_nd import MaxPoolingND  # NOQA
 from chainer.functions.pooling.roi_pooling_2d import roi_pooling_2d  # NOQA
 from chainer.functions.pooling.roi_pooling_2d import ROIPooling2D  # NOQA
 from chainer.functions.pooling.spatial_pyramid_pooling_2d import spatial_pyramid_pooling_2d  # NOQA
 from chainer.functions.pooling.unpooling_2d import Unpooling2D  # NOQA
 from chainer.functions.pooling.unpooling_2d import unpooling_2d  # NOQA
-from chainer.functions.pooling.unpooling_nd import unpooling_1d  # NOQA
-from chainer.functions.pooling.unpooling_nd import unpooling_3d  # NOQA
 from chainer.functions.pooling.unpooling_nd import unpooling_nd  # NOQA
 from chainer.functions.pooling.unpooling_nd import UnpoolingND  # NOQA
 from chainer.functions.pooling.upsampling_2d import Upsampling2D  # NOQA

chainer/functions/connection/convolution_nd.py

@@ -441,49 +441,3 @@ def convolution_nd(x, W, b=None, stride=1, pad=0, cover_all=False):
     args = (x, W) if b is None else (x, W, b)
     y, = fnode.apply(args)
     return y
-
-
-def convolution_1d(x, W, b=None, stride=1, pad=0, cover_all=False):
-    """1-dimensional convolution function.
-
-    .. note::
-
-        This function calls :func:`~chainer.functions.convolution_nd`
-        internally, so see the detail in the documentation of
-        :func:`~chainer.functions.convolution_nd`.
-
-    """
-    if len(x.shape[2:]) != 1:
-        raise ValueError(
-            'The number of dimensions under channel dimension of the input '
-            '\'x\' should be 1. But the actual ndim was {}.'.fromat(
-                len(x.shape[2:])))
-
-    func = ConvolutionND(1, stride, pad, cover_all)
-    if b is None:
-        return func(x, W)
-    else:
-        return func(x, W, b)
-
-
-def convolution_3d(x, W, b=None, stride=1, pad=0, cover_all=False):
-    """3-dimensional convolution function.
-
-    .. note::
-
-        This function calls :func:`~chainer.functions.convolution_nd`
-        internally, so see the detail in the documentation of
-        :func:`~chainer.functions.convolution_nd`.
-
-    """
-    if len(x.shape[2:]) != 3:
-        raise ValueError(
-            'The number of dimensions under channel dimension of the input '
-            '\'x\' should be 3. But the actual ndim was {}.'.fromat(
-                len(x.shape[2:])))
-
-    func = ConvolutionND(3, stride, pad, cover_all)
-    if b is None:
-        return func(x, W)
-    else:
-        return func(x, W, b)

chainer/functions/connection/deconvolution_nd.py

@@ -446,49 +446,3 @@ def deconvolution_nd(x, W, b=None, stride=1, pad=0, outsize=None):
     args = (x, W) if b is None else (x, W, b)
     y, = func.apply(args)
     return y
-
-
-def deconvolution_1d(x, W, b=None, stride=1, pad=0, outsize=None):
-    """1-dimensional deconvolution function.
-
-    .. note::
-
-        This function calls :func:`~chainer.functions.deconvolution_nd` with
-        ``ndim = 1``, so see the details of the behavior in
-        the documentation of :func:`~chainer.functions.deconvolution_nd`.
-
-    """
-    if len(x.shape[2:]) != 1:
-        raise ValueError(
-            'The number of dimensions under channel dimension of the input '
-            '\'x\' should be 1. But the actual ndim was {}.'.fromat(
-                len(x.shape[2:])))
-
-    func = DeconvolutionND(1, stride, pad, outsize)
-    if b is None:
-        return func(x, W)
-    else:
-        return func(x, W, b)
-
-
-def deconvolution_3d(x, W, b=None, stride=1, pad=0, outsize=None):
-    """3-dimensional deconvolution function.
-
-    .. note::
-
-        This function calls :func:`~chainer.functions.deconvolution_nd` with
-        ``ndim = 3``, so see the details of the behavior in
-        the documentation of :func:`~chainer.functions.deconvolution_nd`.
-
-    """
-    if len(x.shape[2:]) != 3:
-        raise ValueError(
-            'The number of dimensions under channel dimension of the input '
-            '\'x\' should be 3. But the actual ndim was {}.'.fromat(
-                len(x.shape[2:])))
-
-    func = DeconvolutionND(3, stride, pad, outsize)
-    if b is None:
-        return func(x, W)
-    else:
-        return func(x, W, b)

chainer/functions/pooling/average_pooling_nd.py

@@ -170,47 +170,3 @@ def average_pooling_nd(x, ksize, stride=None, pad=0):
     """
     ndim = len(x.shape[2:])
     return AveragePoolingND(ndim, ksize, stride=stride, pad=pad).apply((x,))[0]
-
-
-def average_pooling_1d(x, ksize, stride=None, pad=0):
-    """1-dimensional spatial average pooling function.
-
-    .. warning::
-
-        This feature is experimental. The interface can change in the future.
-
-    .. note::
-
-        This function calls :func:`~functions.average_pooling_nd` with
-        ``ndim = 1``, so see the details of the behavior in
-        the documentation of :func:`~functions.average_pooling_nd`.
-
-    """
-    if len(x.shape[2:]) != 1:
-        raise ValueError(
-            'The number of dimensions under channel dimension of the input '
-            '\'x\' should be 1. But the actual ndim was {}.'.fromat(
-                len(x.shape[2:])))
-    return AveragePoolingND(1, ksize, stride=stride, pad=pad)(x)
-
-
-def average_pooling_3d(x, ksize, stride=None, pad=0):
-    """3-dimensional spatial average pooling function.
-
-    .. warning::
-
-        This feature is experimental. The interface can change in the future.
-
-    .. note::
-
-        This function calls :func:`~functions.average_pooling_nd` with
-        ``ndim = 3``, so see the details of the behavior in
-        the documentation of :func:`~functions.average_pooling_nd`.
-
-    """
-    if len(x.shape[2:]) != 3:
-        raise ValueError(
-            'The number of dimensions under channel dimension of the input '
-            '\'x\' should be 3. But the actual ndim was {}.'.fromat(
-                len(x.shape[2:])))
-    return AveragePoolingND(3, ksize, stride=stride, pad=pad)(x)

chainer/functions/pooling/max_pooling_nd.py

@@ -264,47 +264,3 @@ def max_pooling_nd(x, ksize, stride=None, pad=0, cover_all=True):
     """
     ndim = len(x.shape[2:])
     return MaxPoolingND(ndim, ksize, stride, pad, cover_all).apply((x,))[0]
-
-
-def max_pooling_1d(x, ksize, stride=None, pad=0):
-    """1-dimensional spatial max pooling function.
-
-    .. warning::
-
-        This feature is experimental. The interface can change in the future.
-
-    .. note::
-
-        This function calls :func:`~functions.max_pooling_nd` with
-        ``ndim = 1``, so see the details of the behavior in
-        the documentation of :func:`~functions.max_pooling_nd`.
-
-    """
-    if len(x.shape[2:]) != 1:
-        raise ValueError(
-            'The number of dimensions under channel dimension of the input '
-            '\'x\' should be 1. But the actual ndim was {}.'.fromat(
-                len(x.shape[2:])))
-    return MaxPoolingND(1, ksize, stride=stride, pad=pad)(x)
-
-
-def max_pooling_3d(x, ksize, stride=None, pad=0):
-    """3-dimensional spatial max pooling function.
-
-    .. warning::
-
-        This feature is experimental. The interface can change in the future.
-
-    .. note::
-
-        This function calls :func:`~functions.max_pooling_nd` with
-        ``ndim = 3``, so see the details of the behavior in
-        the documentation of :func:`~functions.max_pooling_nd`.
-
-    """
-    if len(x.shape[2:]) != 3:
-        raise ValueError(
-            'The number of dimensions under channel dimension of the input '
-            '\'x\' should be 3. But the actual ndim was {}.'.fromat(
-                len(x.shape[2:])))
-    return MaxPoolingND(3, ksize, stride=stride, pad=pad)(x)

chainer/functions/pooling/unpooling_nd.py

@@ -140,47 +140,3 @@ def unpooling_nd(x, ksize, stride=None, pad=0, outsize=None, cover_all=True):
     ndim = len(x.shape[2:])
     return UnpoolingND(
         ndim, ksize, stride, pad, outsize, cover_all).apply((x,))[0]
-
-
-def unpooling_1d(x, ksize, stride=None, pad=0):
-    """Inverse operation of 1-dimensional spatial pooling.
-
-    .. warning::
-
-        This feature is experimental. The interface can change in the future.
-
-    .. note::
-
-        This function calls :func:`~functions.unpooling_nd` with
-        ``ndim = 1``, so see the details of the behavior in
-        the documentation of :func:`~functions.unpooling_nd`.
-
-    """
-    if len(x.shape[2:]) != 1:
-        raise ValueError(
-            'The number of dimensions under channel dimension of the input '
-            '\'x\' should be 1. But the actual ndim was {}.'.fromat(
-                len(x.shape[2:])))
-    return UnpoolingND(1, ksize, stride=stride, pad=pad)(x)
-
-
-def unpooling_3d(x, ksize, stride=None, pad=0):
-    """Inverse operation of 3-dimensional spatial pooling.
-
-    .. warning::
-
-        This feature is experimental. The interface can change in the future.
-
-    .. note::
-
-        This function calls :func:`~functions.unpooling_nd` with
-        ``ndim = 3``, so see the details of the behavior in
-        the documentation of :func:`~functions.unpooling_nd`.
-
-    """
-    if len(x.shape[2:]) != 3:
-        raise ValueError(
-            'The number of dimensions under channel dimension of the input '
-            '\'x\' should be 3. But the actual ndim was {}.'.fromat(
-                len(x.shape[2:])))
-    return UnpoolingND(3, ksize, stride=stride, pad=pad)(x)

chainer/links/__init__.py

@@ -7,12 +7,8 @@
 from chainer.links.connection.bias import Bias  # NOQA
 from chainer.links.connection.bilinear import Bilinear  # NOQA
 from chainer.links.connection.convolution_2d import Convolution2D  # NOQA
-from chainer.links.connection.convolution_nd import Convolution1D  # NOQA
-from chainer.links.connection.convolution_nd import Convolution3D  # NOQA
 from chainer.links.connection.convolution_nd import ConvolutionND  # NOQA
 from chainer.links.connection.deconvolution_2d import Deconvolution2D  # NOQA
-from chainer.links.connection.deconvolution_nd import Deconvolution1D  # NOQA
-from chainer.links.connection.deconvolution_nd import Deconvolution3D  # NOQA
 from chainer.links.connection.deconvolution_nd import DeconvolutionND  # NOQA
 from chainer.links.connection.depthwise_convolution_2d import DepthwiseConvolution2D  # NOQA
 from chainer.links.connection.dilated_convolution_2d import DilatedConvolution2D  # NOQA

chainer/links/connection/convolution_nd.py

@@ -86,41 +86,3 @@ def __call__(self, x):
         """
         return convolution_nd.convolution_nd(
             x, self.W, self.b, self.stride, self.pad, cover_all=self.cover_all)
-
-
-class Convolution1D(ConvolutionND):
-    """1-dimensional convolution layer.
-
-    .. note::
-
-        This link wraps :class:`~chainer.links.ConvolutionND` by giving 1 to
-        the first argument ``ndim``, so see the details of the behavior in
-        the documentation of :class:`~chainer.links.ConvolutionND`.
-
-    """
-
-    def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,
-                 nobias=False, initialW=None, initial_bias=None,
-                 cover_all=False):
-        super(Convolution1D, self).__init__(
-            1, in_channels, out_channels, ksize, stride, pad, nobias, initialW,
-            initial_bias, cover_all)
-
-
-class Convolution3D(ConvolutionND):
-    """3-dimensional convolution layer.
-
-    .. note::
-
-        This link wraps :class:`~chainer.links.ConvolutionND` by giving 3 to
-        the first argument ``ndim``, so see the details of the behavior in
-        the documentation of :class:`~chainer.links.ConvolutionND`.
-
-    """
-
-    def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,
-                 nobias=False, initialW=None, initial_bias=None,
-                 cover_all=False):
-        super(Convolution3D, self).__init__(
-            3, in_channels, out_channels, ksize, stride, pad, nobias, initialW,
-            initial_bias, cover_all)

chainer/links/connection/deconvolution_nd.py

@@ -75,41 +75,3 @@ def __call__(self, x):
         return deconvolution_nd.deconvolution_nd(
             x, self.W, b=self.b, stride=self.stride, pad=self.pad,
             outsize=self.outsize)
-
-
-class Deconvolution1D(DeconvolutionND):
-    """1-dimensional deconvolution layer.
-
-    .. note::
-
-        This link wraps :class:`~chainer.links.DeconvolutionND` by giving 1 to
-        the first argument ``ndim``, so see the details of the behavior in
-        the documentation of :class:`~chainer.links.DeconvolutionND`.
-
-    """
-
-    def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,
-                 nobias=False, initialW=None, initial_bias=None,
-                 cover_all=False):
-        super(Deconvolution1D, self).__init__(
-            1, in_channels, out_channels, ksize, stride, pad, nobias, initialW,
-            initial_bias, cover_all)
-
-
-class Deconvolution3D(DeconvolutionND):
-    """3-dimensional deconvolution layer.
-
-    .. note::
-
-        This link wraps :class:`~chainer.links.DeconvolutionND` by giving 3 to
-        the first argument ``ndim``, so see the details of the behavior in
-        the documentation of :class:`~chainer.links.DeconvolutionND`.
-
-    """
-
-    def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,
-                 nobias=False, initialW=None, initial_bias=None,
-                 cover_all=False):
-        super(Deconvolution1D, self).__init__(
-            3, in_channels, out_channels, ksize, stride, pad, nobias, initialW,
-            initial_bias, cover_all)