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提交 5ae986b1 authored 作者: Vikram's avatar Vikram
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theano/gpuarray/dnn.py
浏览文件 @ 5ae986b1
......@@ -3039,6 +3039,7 @@ def local_abstractconv_cudnn_graph(op, context_name, inputs, outputs):
return None
if isinstance(op.border_mode, tuple) and any(isinstance(p, tuple) for p in op.border_mode):
# Asymmetric padding not yet supported
return None
inp1 = inputs[0]
......@@ -3138,6 +3139,7 @@ def local_abstractconv_cudnn(node):
if node.op.unshared:
return None
if isinstance(node.op.border_mode, tuple) and any(isinstance(p, tuple) for p in node.op.border_mode):
# Asymmetric padding not yet supported
return None
if isinstance(node.op, AbstractConv2d):
return local_abstractconv_cudnn_graph(node.op, ctx, node.inputs, node.outputs)
......@@ -3156,6 +3158,7 @@ def local_abstractconv_cudnn_alt(node):
if node.op.unshared:
return None
if isinstance(node.op.border_mode, tuple) and any(isinstance(p, tuple) for p in node.op.border_mode):
# Asymmetric padding not yet supported
return None
inp1 = node.inputs[0]
inp2 = node.inputs[1]
......@@ -3366,6 +3369,7 @@ def local_abstractconv_gw_cudnn(node):
if node.op.unshared:
return None
if isinstance(node.op.border_mode, tuple) and any(isinstance(p, tuple) for p in node.op.border_mode):
# Asymmetric padding not yet supported
return None
if isinstance(node.op, AbstractConv2d_gradWeights):
return local_abstractconv_cudnn_graph(node.op, ctx, node.inputs, node.outputs)
......@@ -3381,6 +3385,7 @@ def local_abstractconv_gi_cudnn(node):
if node.op.unshared:
return None
if isinstance(node.op.border_mode, tuple) and any(isinstance(p, tuple) for p in node.op.border_mode):
# Asymmetric padding not yet supported
return None
if isinstance(node.op, AbstractConv2d_gradInputs):
return local_abstractconv_cudnn_graph(node.op, ctx, node.inputs, node.outputs)
......
theano/tensor/nnet/__init__.py
浏览文件 @ 5ae986b1
......@@ -72,18 +72,17 @@ def conv2d(input, filters, input_shape=None, filter_shape=None,
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 ``convdim`` elements where each element
is an integer or a tuple of length 2.
border_mode: str, int or a tuple of two ints or pairs of ints
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 size // 2``
in each convolution dimension, then perform a valid convolution.
For filters with an odd filter size, this leads to the output
shape being equal to the input shape.
``'half'``: pad input with a symmetric border of ``filter rows // 2``
rows and ``filter columns // 2`` columns, then perform a valid
convolution. For filters with an odd number of 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)``: (for 2D) pad input with a symmetric border of ``int1``,
......@@ -91,11 +90,6 @@ def conv2d(input, filters, input_shape=None, filter_shape=None,
``(int1, (int2, int3))`` or ``((int1, int2), int3)``: (for 2D)
pad input with one symmetric border of `int1`` or ``int3``, and
one asymmetric border of ``(int2, int3)`` or ``(int1, int2)``.
``((int1, int2), (int3, int4))``: (for 2D) pad input with an asymmetric
border of ``(int1, int2)`` along one dimension and ``(int3, int4)``
along the second dimension.
``(int1, int2, int3)``: (for 3D) pad input with a symmetric border of
``int1``, ``int2`` and ``int3``, then perform a valid convolution.
subsample: tuple of len 2
Factor by which to subsample the output.
......@@ -208,7 +202,7 @@ def conv2d_transpose(input, filters, output_shape, filter_shape=None,
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 two elements
border_mode: str, int or tuple of two int
Refers to the ``border_mode`` argument of the corresponding forward
(non-transposed) convolution. See the argument description in
``conv2d``. What was ``padding`` for the forward convolution means
......
theano/tensor/nnet/abstract_conv.py
浏览文件 @ 5ae986b1
......@@ -52,11 +52,11 @@ def get_conv_output_shape(image_shape, kernel_shape,
number of output channels, height and width of the output, number of
input channels, height and width of the kernel.
None where undefined.
border_mode: string, int (symbolic or numeric) or tuple of int (symbolic
or numeric). If it is a string, it must be 'valid', 'half' or 'full'.
If it is a tuple, its two (or three) elements respectively correspond
to the padding (possibly left and right) on height and width
(and possibly depth) axis.
border_mode: string, int (symbolic or numeric) or tuple of int (symbolic
or numeric) or pairs of ints. If it is a string, it must be 'valid',
'half' or 'full'. If it is a tuple, its two (or three) elements respectively
correspond to the padding on height and width (and possibly depth)
axis. For asymmetric padding, provide a pair of ints for each dimension.
subsample: tuple of int (symbolic or numeric). Its two or three elements
espectively correspond to the subsampling on height and width (and
possibly depth) axis.
......@@ -104,10 +104,11 @@ def get_conv_shape_1axis(image_shape, kernel_shape, border_mode,
given axis. None if undefined.
kernel_shape: int or None. Corresponds to the kernel shape on a given
axis. None if undefined.
border_mode: string, int or tuple. If it is a string, it must be
border_mode: string, int or tuple of 2 ints. If it is a string, it must be
'valid', 'half' or 'full'. If it is an integer, it must correspond to
the padding on the considered axis. If it is a tuple, its two elements
must correspond to the padding (left and right) on the desired axis.
must correspond to the asymmetric padding (e.g., left and right) on
the considered axis.
subsample: int. It must correspond to the subsampling on the
considered axis.
dilation: int. It must correspond to the dilation on the
......@@ -173,11 +174,11 @@ def get_conv_gradweights_shape(image_shape, top_shape,
image shape. Its four (or five) element must correspond respectively
to: batch size, number of output channels, height and width (and
possibly depth) of the image. None where undefined.
border_mode: string, int (symbolic or numeric) or tuple of int (symbolic
or numeric). If it is a string, it must be 'valid', 'half' or 'full'.
If it is a tuple, its two (or three) elements respectively correspond
to the padding (possibly left and right) on height and width
(and possibly depth) axis.
border_mode: string, int (symbolic or numeric) or tuple of int (symbolic
or numeric) or pairs of ints. If it is a string, it must be 'valid',
'half' or 'full'. If it is a tuple, its two (or three) elements respectively
correspond to the padding on height and width (and possibly depth)
axis. For asymmetric padding, provide a pair of ints for each dimension.
subsample: tuple of int (symbolic or numeric). Its two or three elements
respectively correspond to the subsampling on height and width (and
possibly depth) axis.
......@@ -234,10 +235,11 @@ def get_conv_gradweights_shape_1axis(image_shape, top_shape, border_mode,
given axis. None if undefined.
top_shape: int or None. Corresponds to the top shape on a given axis.
None if undefined.
border_mode: string, int or tuple. If it is a string, it must be
border_mode: string, int or tuple of 2 ints. If it is a string, it must be
'valid', 'half' or 'full'. If it is an integer, it must correspond to
the padding on the considered axis. If it is a tuple, its two elements
must correspond to the padding (left and right) on the desired axis.
must correspond to the asymmetric padding (e.g., left and right) on
the considered axis.
subsample: int. It must correspond to the subsampling on the
considered axis.
dilation: int. It must correspond to the dilation on the
......@@ -296,11 +298,11 @@ def get_conv_gradinputs_shape(kernel_shape, top_shape,
image shape. Its four (or five) element must correspond respectively
to: batch size, number of output channels, height and width (and
possibly depth) of the image. None where undefined.
border_mode: string, int (symbolic or numeric) or tuple of int (symbolic
or numeric). If it is a string, it must be 'valid', 'half' or 'full'.
If it is a tuple, its two (or three) elements respectively correspond
to the padding (possibly left and right) on height and width
(and possibly depth) axis.
border_mode: string, int (symbolic or numeric) or tuple of int (symbolic
or numeric) or pairs of ints. If it is a string, it must be 'valid',
'half' or 'full'. If it is a tuple, its two (or three) elements respectively
correspond to the padding on height and width (and possibly depth)
axis. For asymmetric padding, provide a pair of ints for each dimension.
subsample: tuple of int (symbolic or numeric). Its two or three elements
respectively correspond to the subsampling on height and width (and
possibly depth) axis.
......@@ -354,10 +356,11 @@ def get_conv_gradinputs_shape_1axis(kernel_shape, top_shape, border_mode,
axis. None if undefined.
top_shape: int or None. Corresponds to the top shape on a given axis.
None if undefined.
border_mode: string, int or tuple. If it is a string, it must be
border_mode: string, int or tuple of 2 ints. If it is a string, it must be
'valid', 'half' or 'full'. If it is an integer, it must correspond to
the padding on the considered axis. If it is a tuple, its two elements
must correspond to the padding (left and right) on the desired axis.
must correspond to the asymmetric padding (e.g., left and right) on
the considered axis.
subsample: int. It must correspond to the subsampling on the
considered axis.
dilation: int. It must correspond to the dilation on the
......@@ -423,11 +426,11 @@ def check_conv_gradinputs_shape(image_shape, kernel_shape, output_shape,
output shape. Its four (or five) elements must correspond respectively
to: batch size, number of output channels, height and width
(and possibly depth) of the output. None where undefined.
border_mode: string, int (symbolic or numeric) or tuple where each element
is either an int or a tuple of length 2 (symbolic or numeric).
If it is a string, it must be 'valid', 'half' or 'full'.
If it is a tuple, its two (or three) elements respectively correspond
to the padding on height and width (and possibly depth) axis.
border_mode: string, int (symbolic or numeric) or tuple of int (symbolic
or numeric) or pairs of ints. If it is a string, it must be 'valid',
'half' or 'full'. If it is a tuple, its two (or three) elements respectively
correspond to the padding on height and width (and possibly depth)
axis. For asymmetric padding, provide a pair of ints for each dimension.
subsample: tuple of int (symbolic or numeric). Its two or three elements
respectively correspond to the subsampling on height and width (and
possibly depth) axis.
......@@ -553,8 +556,9 @@ def assert_shape(x, expected_shape, msg='Unexpected shape.'):
return x
def mode_to_pad(mode, convdim, kshp):
""" Computes a tuple for padding given the border_mode parameter
def border_mode_to_pad(mode, convdim, kshp):
"""
Computes a tuple for padding given the border_mode parameter
Parameters
----------
......@@ -708,10 +712,10 @@ def separable_conv2d(input,
width, then perform a valid convolution.
``(int1, int2)``: pad input with a symmetric border of ``int1`` rows
and ``int2`` columns, then perform a valid convolution.
``(int1, (int2, int3))`` or ``((int1, int2), int3)``: (for 2D)
``(int1, (int2, int3))`` or ``((int1, int2), int3)``:
pad input with one symmetric border of `int1`` or ``int3``, and
one asymmetric border of ``(int2, int3)`` or ``(int1, int2)``.
``((int1, int2), (int3, int4))``: (for 2D) pad input with an asymmetric
``((int1, int2), (int3, int4))``: pad input with an asymmetric
border of ``(int1, int2)`` along one dimension and ``(int3, int4)``
along the second dimension.
......@@ -1041,8 +1045,7 @@ def conv2d_grad_wrt_inputs(output_grad,
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 2 elements where each element
is an integer or a tuple of length 2.
border_mode: str, int or a tuple of two ints or pairs of ints
Either of the following:
``'valid'``
......@@ -1073,8 +1076,8 @@ def conv2d_grad_wrt_inputs(output_grad,
pad input with one symmetric border of `int1`` or ``int3``, and
one asymmetric border of ``(int2, int3)`` or ``(int1, int2)``.
``((int1, int2), (int3, int4))``: (for 2D) pad input with an asymmetric
border of ``(int1, int2)`` along one dimension and ``(int3, int4)``
``((int1, int2), (int3, int4))``
pad input with an asymmetric border of ``(int1, int2)`` along one dimension and ``(int3, int4)``
along the second dimension.
subsample : tuple of len 2
......@@ -1336,8 +1339,7 @@ def conv2d_grad_wrt_weights(input,
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 2 elements where each element
is an integer or a tuple of length 2.
border_mode: str, int or a tuple of two ints or pairs of ints
Either of the following:
``'valid'``
......@@ -1368,9 +1370,9 @@ def conv2d_grad_wrt_weights(input,
pad input with one symmetric border of `int1`` or ``int3``, and
one asymmetric border of ``(int2, int3)`` or ``(int1, int2)``.
``((int1, int2), (int3, int4))``: (for 2D) pad input with an asymmetric
border of ``(int1, int2)`` along one dimension and ``(int3, int4)``
along the second dimension.
``((int1, int2), (int3, int4))``
pad input with an asymmetric border of ``(int1, int2)`` along
one dimension and ``(int3, int4)`` along the second dimension.
subsample : tuple of len 2
The subsampling used in the forward pass of the convolutional
operation. Also called strides elsewhere.
......@@ -1584,16 +1586,17 @@ def conv3d_grad_wrt_weights(input,
return gradWeight_op(input, output_grad, filter_shape[-3:])
def causal_conv(input,
filters,
filter_shape,
input_shape=None,
subsample=1,
filter_flip=True,
filter_dilation=1,
num_groups=1,
unshared=False):
"""Computes (dilated) causal convolution
def causal_conv1d(input,
filters,
filter_shape,
input_shape=None,
subsample=1,
filter_flip=True,
filter_dilation=1,
num_groups=1,
unshared=False):
"""
Computes (dilated) causal convolution
The output at time t depends only on the inputs till t-1. Used for
modelling temporal data.
......@@ -1629,7 +1632,7 @@ def causal_conv(input,
num_groups : int
Divides the image, kernel and output tensors into num_groups
separate groups. Each which carry out convolutions separately
unshared: bool
unshared : bool
If true, then unshared or 'locally connected' convolution will be
performed. A different filter will be used for each region of the
input.
......@@ -1640,6 +1643,11 @@ def causal_conv(input,
Set of feature vectors generated by convolutional layer. Tensor is
of shape (batch_size, output_channels, output_length)
Notes
-----
:note: Currently, this is implemented with the 2D convolution ops.
"""
input = as_tensor_variable(input)
......@@ -1885,8 +1893,7 @@ class BaseAbstractConv(Op):
element is not known at compile time.
kshp is defined w.r.t the forward conv.
border_mode: str, int or tuple of ``convdim`` elements where each element
is an integer or a tuple of length 2.
border_mode: str, int or a tuple of two ints or pairs of ints
Either of the following:
``'valid'``: apply filter wherever it completely overlaps with the
......@@ -1965,12 +1972,15 @@ class BaseAbstractConv(Op):
'invalid border_mode {}, which must be a '
'tuple of length {}'.format(border_mode, convdim))
for mode in border_mode:
if isinstance(mode, tuple) and convdim != 2:
raise NotImplementedError(
'Asymmetric padding not implemented for {}D'.format(convdim))
if not((isinstance(mode, integer_types) and mode >= 0) or
(isinstance(mode, tuple) and len(mode) == 2 and min(mode) >= 0 and
all(isinstance(m, integer_types) for m in mode))):
raise ValueError(
'invalid border mode {}. The tuple can only contain integers '
' or tuples of integers of length 2'.format(border_mode))
' or pairs of integers'.format(border_mode))
elif border_mode not in ('valid', 'full', 'half'):
raise ValueError(
'invalid border_mode {}, which must be either '
......@@ -2238,7 +2248,7 @@ class AbstractConv(BaseAbstractConv):
% self.convdim)
o, = out_
mode = self.border_mode
pad = mode_to_pad(mode, self.convdim, dil_kernshp)
pad = border_mode_to_pad(mode, self.convdim, dil_kernshp)
if any(p != (0, 0) for p in pad):
mode = "valid"
......@@ -2503,7 +2513,7 @@ class AbstractConv_gradWeights(BaseAbstractConv):
dil_shape = tuple((shape[i] - 1) * self.filter_dilation[i] + 1
for i in range(self.convdim))
pad = mode_to_pad(self.border_mode, self.convdim, dil_shape)
pad = border_mode_to_pad(self.border_mode, self.convdim, dil_shape)
if any(p != (0, 0) for p in pad):
new_img = np.zeros((img.shape[0], img.shape[1]) +
......@@ -2805,8 +2815,7 @@ class AbstractConv_gradInputs(BaseAbstractConv):
dil_kernshp = tuple((kern.shape[-self.convdim + i] - 1) * self.filter_dilation[i] + 1
for i in range(self.convdim))
mode = self.border_mode
pad = mode_to_pad(mode, self.convdim, dil_kernshp)
pad = border_mode_to_pad(self.border_mode, self.convdim, dil_kernshp)
imshp = self.imshp[:] if self.imshp is not None else [None] * (2 + self.convdim)
fallback_imshp = ([topgrad.shape[0], kern.shape[-self.convdim - 1]] +
......@@ -2815,7 +2824,7 @@ class AbstractConv_gradInputs(BaseAbstractConv):
for i in range(2 + self.convdim)]
expected_topgrad_shape = get_conv_output_shape(
imshp, kern.shape,
mode, self.subsample, self.filter_dilation)
self.border_mode, self.subsample, self.filter_dilation)
if not tuple(expected_topgrad_shape) == tuple(topgrad.shape):
raise ValueError(
'invalid input_shape for gradInputs: the given input_shape '
......
theano/tensor/nnet/corr.py
浏览文件 @ 5ae986b1
......@@ -89,7 +89,7 @@ class BaseCorrMM(gof.OpenMPOp):
raise ValueError(
'invalid border_mode {}, which must be either '
'"valid", "full", "half", an integer or a tuple '
'of length 2'.format(border_mode))
'of two integers or a pair of integers'.format(border_mode))
self.border_mode = border_mode
if len(subsample) != 2:
raise ValueError("subsample must have two elements")
......
theano/tensor/nnet/tests/test_abstract_conv.py
浏览文件 @ 5ae986b1
......@@ -24,7 +24,7 @@ from theano.tensor.nnet.abstract_conv import bilinear_kernel_1D
from theano.tensor.nnet.abstract_conv import bilinear_kernel_2D
from theano.tensor.nnet.abstract_conv import bilinear_upsampling
from theano.tensor.nnet.abstract_conv import separable_conv2d, separable_conv3d
from theano.tensor.nnet.abstract_conv import causal_conv
from theano.tensor.nnet.abstract_conv import causal_conv1d
from theano.tensor.nnet.corr import (CorrMM, CorrMM_gradWeights,
CorrMM_gradInputs)
from theano.tensor.nnet.corr3d import (Corr3dMM, Corr3dMM_gradWeights,
......@@ -2037,7 +2037,7 @@ class TestCausalConv(unittest.TestCase):
img_sym = theano.tensor.tensor3('img')
kern_sym = theano.tensor.tensor3('kern')
sym_out = causal_conv(img_sym, kern_sym, self.kern.shape, filter_dilation=self.dilation)
sym_out = causal_conv1d(img_sym, kern_sym, self.kern.shape, filter_dilation=self.dilation)
causal_func = theano.function([img_sym, kern_sym], sym_out, mode=self.mode)
......@@ -2046,6 +2046,6 @@ class TestCausalConv(unittest.TestCase):
utt.assert_allclose(output, self.precomp_top)
def causal_conv_fn(inputs_val, filters_val):
return causal_conv(inputs_val, filters_val, self.kern.shape, filter_dilation=1)
return causal_conv1d(inputs_val, filters_val, self.kern.shape, filter_dilation=1)
utt.verify_grad(causal_conv_fn, [self.img, self.kern], mode=self.mode, eps=1)
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