class StereoViewConverter(object):
"""
Converts stereo image data between two formats:
A) A dense design matrix, one stereo pair per row (VectorSpace)
B) An image pair (CompositeSpace of two Conv2DSpaces)
Parameters
----------
shape : tuple
See doc for __init__'s <shape> parameter.
"""
def __init__(self, shape, axes=None):
"""
The arguments describe how the data is laid out in the design matrix.
Parameters
----------
shape : tuple
A tuple of 4 ints, describing the shape of each datum.
This is the size of each axis in <axes>, excluding the 'b' axis.
axes : tuple
A tuple of the following elements in any order:
'b' batch axis
's' stereo axis
0 image axis 0 (row)
1 image axis 1 (column)
'c' channel axis
"""
shape = tuple(shape)
if not all(isinstance(s, int) for s in shape):
raise TypeError("Shape must be a tuple/list of ints")
if len(shape) != 4:
raise ValueError("Shape array needs to be of length 4, got %s." %
shape)
datum_axes = list(axes)
datum_axes.remove('b')
if shape[datum_axes.index('s')] != 2:
raise ValueError("Expected 's' axis to have size 2, got %d.\n"
" axes: %s\n"
" shape: %s" %
(shape[datum_axes.index('s')], axes, shape))
self.shape = shape
self.set_axes(axes)
def make_conv2d_space(shape, axes):
shape_axes = list(axes)
shape_axes.remove('b')
image_shape = tuple(shape[shape_axes.index(axis)]
for axis in (0, 1))
conv2d_axes = list(axes)
conv2d_axes.remove('s')
return Conv2DSpace(shape=image_shape,
num_channels=shape[shape_axes.index('c')],
axes=conv2d_axes,
dtype=None)
conv2d_space = make_conv2d_space(shape, axes)
self.topo_space = CompositeSpace((conv2d_space, conv2d_space))
self.storage_space = VectorSpace(dim=numpy.prod(shape))
def get_formatted_batch(self, batch, space):
"""
Returns a batch formatted to a space.
Parameters
----------
batch : ndarray
The batch to format
space : a pylearn2.space.Space
The target space to format to.
"""
return self.storage_space.np_format_as(batch, space)
def design_mat_to_topo_view(self, design_mat):
"""
Called by DenseDesignMatrix.get_formatted_view(), get_batch_topo()
Parameters
----------
design_mat : ndarray
"""
return self.storage_space.np_format_as(design_mat, self.topo_space)
def design_mat_to_weights_view(self, design_mat):
"""
Called by DenseDesignMatrix.get_weights_view()
Parameters
----------
design_mat : ndarray
"""
return self.design_mat_to_topo_view(design_mat)
def topo_view_to_design_mat(self, topo_batch):
"""
Used by DenseDesignMatrix.set_topological_view(), .get_design_mat()
Parameters
----------
topo_batch : ndarray
"""
return self.topo_space.np_format_as(topo_batch, self.storage_space)
def view_shape(self):
"""
TODO: write documentation.
"""
return self.shape
def weights_view_shape(self):
"""
TODO: write documentation.
"""
return self.view_shape()
def set_axes(self, axes):
"""
Change the order of the axes.
Parameters
----------
axes : tuple
Must have length 5, must contain 'b', 's', 0, 1, 'c'.
"""
axes = tuple(axes)
if len(axes) != 5:
raise ValueError("Axes must have 5 elements; got %s" % str(axes))
for required_axis in ('b', 's', 0, 1, 'c'):
if required_axis not in axes:
raise ValueError("Axes must contain 'b', 's', 0, 1, and 'c'. "
"Got %s." % str(axes))
if axes.index('b') != 0:
raise ValueError("The 'b' axis must come first (axes = %s)." %
str(axes))
def remove_b_axis(axes):
axes = list(axes)
axes.remove('b')
return tuple(axes)
if hasattr(self, 'axes'):
# Reorders the shape vector to match the new axis ordering.
assert hasattr(self, 'shape')
old_axes = remove_b_axis(self.axes) # pylint: disable-msg=E0203
new_axes = remove_b_axis(axes)
new_shape = tuple(self.shape[old_axes.index(a)] for a in new_axes)
self.shape = new_shape
self.axes = axes
class StereoViewConverter(object):
"""
Converts stereo image data between two formats:
A) A dense design matrix, one stereo pair per row (VectorSpace)
B) An image pair (CompositeSpace of two Conv2DSpaces)
Parameters
----------
shape : tuple
See doc for __init__'s <shape> parameter.
"""
def __init__(self, shape, axes=None):
"""
The arguments describe how the data is laid out in the design matrix.
Parameters
----------
shape : tuple
A tuple of 4 ints, describing the shape of each datum.
This is the size of each axis in <axes>, excluding the 'b' axis.
axes : tuple
A tuple of the following elements in any order:
'b' batch axis
's' stereo axis
0 image axis 0 (row)
1 image axis 1 (column)
'c' channel axis
"""
shape = tuple(shape)
if not all(isinstance(s, int) for s in shape):
raise TypeError("Shape must be a tuple/list of ints")
if len(shape) != 4:
raise ValueError("Shape array needs to be of length 4, got %s." %
shape)
datum_axes = list(axes)
datum_axes.remove('b')
if shape[datum_axes.index('s')] != 2:
raise ValueError("Expected 's' axis to have size 2, got %d.\n"
" axes: %s\n"
" shape: %s" %
(shape[datum_axes.index('s')],
axes,
shape))
self.shape = shape
self.set_axes(axes)
def make_conv2d_space(shape, axes):
shape_axes = list(axes)
shape_axes.remove('b')
image_shape = tuple(shape[shape_axes.index(axis)]
for axis in (0, 1))
conv2d_axes = list(axes)
conv2d_axes.remove('s')
return Conv2DSpace(shape=image_shape,
num_channels=shape[shape_axes.index('c')],
axes=conv2d_axes,
dtype=None)
conv2d_space = make_conv2d_space(shape, axes)
self.topo_space = CompositeSpace((conv2d_space, conv2d_space))
self.storage_space = VectorSpace(dim=numpy.prod(shape))
def get_formatted_batch(self, batch, space):
"""
Returns a batch formatted to a space.
Parameters
----------
batch : ndarray
The batch to format
space : a pylearn2.space.Space
The target space to format to.
"""
return self.storage_space.np_format_as(batch, space)
def design_mat_to_topo_view(self, design_mat):
"""
Called by DenseDesignMatrix.get_formatted_view(), get_batch_topo()
Parameters
----------
design_mat : ndarray
"""
return self.storage_space.np_format_as(design_mat, self.topo_space)
def design_mat_to_weights_view(self, design_mat):
"""
Called by DenseDesignMatrix.get_weights_view()
Parameters
----------
design_mat : ndarray
"""
return self.design_mat_to_topo_view(design_mat)
def topo_view_to_design_mat(self, topo_batch):
"""
Used by DenseDesignMatrix.set_topological_view(), .get_design_mat()
Parameters
----------
topo_batch : ndarray
"""
return self.topo_space.np_format_as(topo_batch, self.storage_space)
def view_shape(self):
"""
TODO: write documentation.
"""
return self.shape
def weights_view_shape(self):
"""
TODO: write documentation.
"""
return self.view_shape()
def set_axes(self, axes):
"""
Change the order of the axes.
Parameters
----------
axes : tuple
Must have length 5, must contain 'b', 's', 0, 1, 'c'.
"""
axes = tuple(axes)
if len(axes) != 5:
raise ValueError("Axes must have 5 elements; got %s" % str(axes))
for required_axis in ('b', 's', 0, 1, 'c'):
if required_axis not in axes:
raise ValueError("Axes must contain 'b', 's', 0, 1, and 'c'. "
"Got %s." % str(axes))
if axes.index('b') != 0:
raise ValueError("The 'b' axis must come first (axes = %s)." %
str(axes))
def remove_b_axis(axes):
axes = list(axes)
axes.remove('b')
return tuple(axes)
if hasattr(self, 'axes'):
# Reorders the shape vector to match the new axis ordering.
assert hasattr(self, 'shape')
old_axes = remove_b_axis(self.axes) # pylint: disable-msg=E0203
new_axes = remove_b_axis(axes)
new_shape = tuple(self.shape[old_axes.index(a)] for a in new_axes)
self.shape = new_shape
self.axes = axes
class StereoViewConverter(object):
"""
Converts stereo image data between two formats:
#. A dense design matrix, one stereo pair per row (`VectorSpace`)
#. An image pair (`CompositeSpace` of two `Conv2DSpace`)
The arguments describe how the data is laid out in the design matrix.
Parameters
----------
shape: tuple
A tuple of 4 ints, describing the shape of each datum. This is the size
of each axis in `<axes>`, excluding the `b` axis.
axes : tuple
Tuple of the following elements in any order:
* 'b' : batch axis
* 's' : stereo axis
* 0 : image axis 0 (row)
* 1 : image axis 1 (column)
* 'c' : channel axis
"""
def __init__(self, shape, axes=None):
shape = tuple(shape)
if not all(isinstance(s, int) for s in shape):
raise TypeError("Shape must be a tuple/list of ints")
if len(shape) != 4:
raise ValueError("Shape array needs to be of length 4, got %s." %
shape)
datum_axes = list(axes)
datum_axes.remove('b')
if shape[datum_axes.index('s')] != 2:
raise ValueError("Expected 's' axis to have size 2, got %d.\n"
" axes: %s\n"
" shape: %s" %
(shape[datum_axes.index('s')],
axes,
shape))
self.shape = shape
self.set_axes(axes)
def make_conv2d_space(shape, axes):
shape_axes = list(axes)
shape_axes.remove('b')
image_shape = tuple(shape[shape_axes.index(axis)]
for axis in (0, 1))
conv2d_axes = list(axes)
conv2d_axes.remove('s')
return Conv2DSpace(shape=image_shape,
num_channels=shape[shape_axes.index('c')],
axes=conv2d_axes)
conv2d_space = make_conv2d_space(shape, axes)
self.topo_space = CompositeSpace((conv2d_space, conv2d_space))
self.storage_space = VectorSpace(dim=numpy.prod(shape))
def get_formatted_batch(self, batch, space):
"""
.. todo::
WRITEME
"""
return self.storage_space.np_format_as(batch, space)
def design_mat_to_topo_view(self, design_mat):
"""
Called by DenseDesignMatrix.get_formatted_view(), get_batch_topo()
"""
return self.storage_space.np_format_as(design_mat, self.topo_space)
def design_mat_to_weights_view(self, design_mat):
"""
Called by DenseDesignMatrix.get_weights_view()
"""
return self.design_mat_to_topo_view(design_mat)
def topo_view_to_design_mat(self, topo_batch):
"""
Used by `DenseDesignMatrix.set_topological_view()` and
`DenseDesignMatrix.get_design_mat()`.
"""
return self.topo_space.np_format_as(topo_batch, self.storage_space)
def view_shape(self):
"""
.. todo::
WRITEME
"""
return self.shape
def weights_view_shape(self):
"""
.. todo::
WRITEME
"""
return self.view_shape()
def set_axes(self, axes):
"""
.. todo::
WRITEME
"""
axes = tuple(axes)
if len(axes) != 5:
raise ValueError("Axes must have 5 elements; got %s" % str(axes))
for required_axis in ('b', 's', 0, 1, 'c'):
if required_axis not in axes:
raise ValueError("Axes must contain 'b', 's', 0, 1, and 'c'. "
"Got %s." % str(axes))
if axes.index('b') != 0:
raise ValueError("The 'b' axis must come first (axes = %s)." %
str(axes))
def get_batchless_axes(axes):
axes = list(axes)
axes.remove('b')
return tuple(axes)
if hasattr(self, 'axes'):
# Reorders the shape vector to match the new axis ordering.
assert hasattr(self, 'shape')
old_axes = get_batchless_axes(self.axes)
new_axes = get_batchless_axes(axes)
new_shape = tuple(self.shape[old_axes.index(a)] for a in new_axes)
self.shape = new_shape
self.axes = axes
class StereoViewConverter(object):
"""
Converts stereo image data between two formats:
#. A dense design matrix, one stereo pair per row (`VectorSpace`)
#. An image pair (`CompositeSpace` of two `Conv2DSpace`)
The arguments describe how the data is laid out in the design matrix.
Parameters
----------
shape: tuple
A tuple of 4 ints, describing the shape of each datum. This is the size
of each axis in `<axes>`, excluding the `b` axis.
axes : tuple
Tuple of the following elements in any order:
* 'b' : batch axis
* 's' : stereo axis
* 0 : image axis 0 (row)
* 1 : image axis 1 (column)
* 'c' : channel axis
"""
def __init__(self, shape, axes=None):
shape = tuple(shape)
if not all(isinstance(s, int) for s in shape):
raise TypeError("Shape must be a tuple/list of ints")
if len(shape) != 4:
raise ValueError("Shape array needs to be of length 4, got %s." %
shape)
datum_axes = list(axes)
datum_axes.remove('b')
if shape[datum_axes.index('s')] != 2:
raise ValueError("Expected 's' axis to have size 2, got %d.\n"
" axes: %s\n"
" shape: %s" %
(shape[datum_axes.index('s')], axes, shape))
self.shape = shape
self.set_axes(axes)
def make_conv2d_space(shape, axes):
shape_axes = list(axes)
shape_axes.remove('b')
image_shape = tuple(shape[shape_axes.index(axis)]
for axis in (0, 1))
conv2d_axes = list(axes)
conv2d_axes.remove('s')
return Conv2DSpace(shape=image_shape,
num_channels=shape[shape_axes.index('c')],
axes=conv2d_axes)
conv2d_space = make_conv2d_space(shape, axes)
self.topo_space = CompositeSpace((conv2d_space, conv2d_space))
self.storage_space = VectorSpace(dim=numpy.prod(shape))
def get_formatted_batch(self, batch, space):
return self.storage_space.np_format_as(batch, space)
def design_mat_to_topo_view(self, design_mat):
"""
Called by DenseDesignMatrix.get_formatted_view(), get_batch_topo()
"""
return self.storage_space.np_format_as(design_mat, self.topo_space)
def design_mat_to_weights_view(self, design_mat):
"""
Called by DenseDesignMatrix.get_weights_view()
"""
return self.design_mat_to_topo_view(design_mat)
def topo_view_to_design_mat(self, topo_batch):
"""
Used by `DenseDesignMatrix.set_topological_view()` and
`DenseDesignMatrix.get_design_mat()`.
"""
return self.topo_space.np_format_as(topo_batch, self.storage_space)
def view_shape(self):
return self.shape
def weights_view_shape(self):
return self.view_shape()
def set_axes(self, axes):
axes = tuple(axes)
if len(axes) != 5:
raise ValueError("Axes must have 5 elements; got %s" % str(axes))
for required_axis in ('b', 's', 0, 1, 'c'):
if required_axis not in axes:
raise ValueError("Axes must contain 'b', 's', 0, 1, and 'c'. "
"Got %s." % str(axes))
if axes.index('b') != 0:
raise ValueError("The 'b' axis must come first (axes = %s)." %
str(axes))
def get_batchless_axes(axes):
axes = list(axes)
axes.remove('b')
return tuple(axes)
if hasattr(self, 'axes'):
# Reorders the shape vector to match the new axis ordering.
assert hasattr(self, 'shape')
old_axes = get_batchless_axes(self.axes)
new_axes = get_batchless_axes(axes)
new_shape = tuple(self.shape[old_axes.index(a)] for a in new_axes)
self.shape = new_shape
self.axes = axes
