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numpydoc for theano/sandbox/gpuarray/dnn.py

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theano/sandbox/gpuarray/dnn.py
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......@@ -160,6 +160,7 @@ def c_set_tensor4d(var, desc, err, fail):
class DnnBase(COp):
"""
Creates a handle for cudnn and pulls in the cudnn libraries and headers.
"""
# dnn does not know about broadcasting, so we do not need to assert
# the input broadcasting pattern.
......@@ -230,11 +231,11 @@ class DnnVersion(Op):
def version():
"""return the current cuDNN version we compile with.
"""
Return the current cuDNN version we compile with.
This return a tuple with the header version and the library
version we link with. For older cudnn version without version
information, we return -1.
This return a tuple with the header version and the library version we link
with. For older cudnn version without version information, we return -1.
"""
if not dnn_available():
......@@ -252,12 +253,14 @@ version.v = None
class GpuDnnConvDesc(Op):
"""This Op builds a convolution descriptor for use in the other
convolution operations.
"""
This Op builds a convolution descriptor for use in the other convolution
operations.
see the doc of :func:`dnn_conv` for a description of the parameters
See the doc of :func:`dnn_conv` for a description of the parameters
"""
__props__ = ('border_mode', 'subsample', 'conv_mode')
def c_headers(self):
......@@ -408,17 +411,21 @@ class GpuDnnConv(DnnBase, COp):
"""
The forward convolution.
:param image:
:param kernel:
:param descr: the convolution descriptor
Parameters
----------
image
kernel
descr
The convolution descriptor.
workmem
Either 'none', 'small' or 'large'. Default is the value of
:attr:`config.dnn.conv.workmem`.
"""
__props__ = ('workmem', 'inplace')
def __init__(self, workmem=None, inplace=False):
"""
:param workmem: either 'none', 'small' or 'large'. Default is
the value of :attr:`config.dnn.conv.workmem`.
"""
COp.__init__(self, ["dnn_base.c", "dnn_conv_base.c", "dnn_fwd.c"],
"APPLY_SPECIFIC(conv_fwd)")
if workmem is None:
......@@ -489,8 +496,9 @@ class GpuDnnConv(DnnBase, COp):
def get_out_shape(ishape, kshape, border_mode, subsample):
"""
This function computes the output shape for a convolution with
the specified parameters. `ishape` and `kshape` can be symbolic
the specified parameters. `ishape` and `kshape` can be symbolic
or scalar.
"""
b = ishape[0] # Number of inputs
h = ishape[2] # Height of input feature maps
......@@ -524,11 +532,15 @@ class GpuDnnConvGradW(DnnBase, COp):
"""
The convolution gradient with respect to the weights.
:param image:
:param kernel:
:param descr: the convolution descriptor
Parameters
----------
image
kernel
descr
The convolution descriptor.
"""
__props__ = ('inplace',)
def __init__(self, inplace=False):
......@@ -596,11 +608,15 @@ class GpuDnnConvGradI(DnnBase):
"""
The convolution gradient with respect to the inputs.
:param image:
:param kernel:
:param descr: the convolution descriptor
Parameters
----------
image
kernel
descr
The convolution descriptor.
"""
__props__ = ('inplace',)
def __init__(self, inplace=False):
......@@ -667,14 +683,22 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
The memory layout to use is 'bc01', that is 'batch', 'channel',
'first dim', 'second dim' in that order.
:param img: images to do the convolution over
:param kerns: convolution filters
:param border_mode: one of 'valid', 'full'; additionally, the padding size
could be directly specified by an integer or a pair of integers
:param subsample: perform subsampling of the output (default: (1, 1))
:param conv_mode: perform convolution (kernels flipped) or cross-correlation.
One of 'conv', 'cross'. (default: 'conv')
:param direction_hint: Used by graph optimizers to change algorithm choice.
Parameters
----------
img
Images to do the convolution over.
kerns
Convolution filters.
border_mode
One of 'valid', 'full'; additionally, the padding size
could be directly specified by an integer or a pair of integers.
subsample
Perform subsampling of the output (default: (1, 1)).
conv_mode
Perform convolution (kernels flipped) or cross-correlation.
One of 'conv', 'cross' (default: 'conv').
direction_hint
Used by graph optimizers to change algorithm choice.
By default, GpuDnnConv will be used to carry out the convolution.
If border_mode is 'valid', subsample is (1,1) and direction_hint is
'bprop weights', it will use GpuDnnConvGradW.
......@@ -682,15 +706,15 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
*not* 'forward!', it will use GpuDnnConvGradI.
This parameter is used internally by graph optimizers and may be
removed at any time without a deprecation period. You have been warned.
:param workmem: Specify the amount of working memory allowed.
More memory is usually faster. One of 'none', 'small' or
'large'. (default is None which takes its value from
:attr:`config.dnn.conv.workmem`)
workmem
Specify the amount of working memory allowed. More memory is usually
faster. One of 'none', 'small' or 'large' (default is None which takes
its value from :attr:`config.dnn.conv.workmem`).
.. warning:: The cuDNN library only works with GPU that have a compute
capability of 3.0 or higer. This means that older GPU will not
work with this Op.
:warning: The cuDNN library only works with GPU that have a compute
capability of 3.0 or higer. This means that older GPU will not
work with this Op.
"""
fgraph = getattr(img, 'fgraph', None) or getattr(kerns, 'fgraph', None)
if (border_mode == 'valid' and subsample == (1, 1) and
......@@ -749,14 +773,21 @@ class GpuDnnPoolDesc(Op):
This Op builds a pooling descriptor for use in the other
pooling operations.
:param ws: windows size
:param stride: (dx, dy)
:param mode: 'max', 'average_inc_pad' or 'average_exc_pad'
The old deprecated name 'average' correspond to 'average_inc_pad'
:param pad: (padX, padY) padding information.
Parameters
----------
ws
Windows size.
stride
(dx, dy).
mode : {'max', 'average_inc_pad', 'average_exc_pad'}
The old deprecated name 'average' correspond to 'average_inc_pad'.
pad
(padX, padY) padding information.
padX is the size of the left and right borders,
padY is the size of the top and bottom borders.
"""
__props__ = ('ws', 'stride', 'mode', 'pad')
def c_headers(self):
......@@ -859,9 +890,15 @@ class GpuDnnPool(DnnBase):
"""
Pooling.
:param img: the image 4d tensor.
:param desc: the pooling descriptor.
Parameters
----------
img
The image 4d tensor.
desc
The pooling descriptor.
"""
__props__ = ()
def make_node(self, img, desc):
......@@ -1029,11 +1066,19 @@ class GpuDnnPoolGrad(DnnBase):
"""
The pooling gradient.
:param inp: the input of the pooling.
:param out: the output of the pooling in the forward.
:param inp_grad: same size as out, but is the corresponding gradient information.
:param desc: The pooling descriptor.
Parameters
----------
inp
The input of the pooling.
out
The output of the pooling in the forward.
inp_grad
Same size as out, but is the corresponding gradient information.
desc
The pooling descriptor.
"""
__props__ = ()
def make_node(self, inp, out, inp_grad, desc):
......@@ -1209,19 +1254,28 @@ def dnn_pool(img, ws, stride=(1, 1), mode='max', pad=(0, 0)):
The memory layout to use is 'bc01', that is 'batch', 'channel',
'first dim', 'second dim' in that order.
:param img: images to do the pooling over
:param ws: subsampling window size
:param stride: subsampling stride (default: (1, 1))
:param mode: one of 'max', 'average_inc_pad' or 'average_exc_pad
(default: 'max')
:param pad: (padX, padY) padding information.
Parameters
----------
img
Images to do the pooling over.
ws
Subsampling window size.
stride
Subsampling stride (default: (1, 1)).
mode : {'max', 'average_inc_pad', 'average_exc_pad'}
pad
(padX, padY) padding information.
padX is the size of the left and right borders,
padY is the size of the top and bottom borders.
:warning: The cuDNN library only works with GPU that have a compute
capability of 3.0 or higer. This means that older GPU will not
work with this Op.
:note: This Op implements the ignore_border=True of max_pool_2d.
.. warning:: The cuDNN library only works with GPU that have a compute
capability of 3.0 or higer. This means that older GPU will not
work with this Op.
Notes
-----
This Op implements the ignore_border=True of max_pool_2d.
"""
img = gpu_contiguous(img)
desc = GpuDnnPoolDesc(ws=ws, stride=stride, mode=mode, pad=pad)()
......@@ -1232,12 +1286,18 @@ class GpuDnnSoftmaxBase(DnnBase):
"""
Op for the cuDNN Softmax.
:param tensor_format: Whether the data format is 'bc01' or 'b01c'.
:param algo: 'fast' or 'accurate' indicating whether computations should be
Parameters
----------
tensor_format
Whether the data format is 'bc01' or 'b01c'.
algo
'fast' or 'accurate' indicating whether computations should be
optimized for speed or accuracy respectively.
:param mode: 'instance' or 'channel' indicating whether the softmax should
be computed per image across 'c01' or per spatial location '01' per
mode
'instance' or 'channel' indicating whether the softmax should be
computed per image across 'c01' or per spatial location '01' per
image across 'c'.
"""
__props__ = ('tensor_format', 'mode', 'algo')
......@@ -1381,13 +1441,18 @@ class GpuDnnSoftmax(GpuDnnSoftmaxBase):
"""
Op for the cuDNN Softmax.
:param tensor_format: Whether the data format is 'bc01' or 'b01c'.
:param algo: 'fast' or 'accurate' indicating whether computations should be
tensor_format
Whether the data format is 'bc01' or 'b01c'.
algo
'fast' or 'accurate' indicating whether computations should be
optimized for speed or accuracy respectively.
:param mode: 'instance' or 'channel' indicating whether the softmax should
be computed per image across 'c01' or per spatial location '01' per
mode
'instance' or 'channel' indicating whether the softmax should be
computed per image across 'c01' or per spatial location '01' per
image across 'c'.
"""
direction = 'forward'
softmax_inputs = ['softmax_input']
......@@ -1442,12 +1507,18 @@ class GpuDnnSoftmaxGrad(GpuDnnSoftmaxBase):
"""
Op for the cuDNN SoftmaxGrad.
:param tensor_format: Whether the data format is 'bc01' or 'b01c'.
:param algo: 'fast' or 'accurate' indicating whether computations should be
Parameters
----------
tensor_format
Whether the data format is 'bc01' or 'b01c'.
algo
'fast' or 'accurate' indicating whether computations should be
optimized for speed or accuracy respectively.
:param mode: 'instance' or 'channel' indicating whether the softmax should
mode
'instance' or 'channel' indicating whether the softmax should
be computed per image across 'c01' or per spatial location '01' per
image across 'c'.
"""
direction = 'backward'
softmax_inputs = ['softmax_gout', 'softmax_input']
......@@ -1713,7 +1784,10 @@ def local_softmax_dnn(node):
class NoCuDNNRaise(Optimizer):
def apply(self, fgraph):
""" Raise a RuntimeError if cudnn can't be used"""
"""
Raise a RuntimeError if cudnn can't be used.
"""
if not dnn_available():
# Make an assert error as we want Theano to fail, not
# just skip this optimization.
......
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