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提交 c49136eb authored 作者: Chiheb Trabelsi's avatar Chiheb Trabelsi
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Functions parameters have been modified in pool.py for coherence purposes.…

Functions parameters have been modified in pool.py for coherence purposes. Deprecation warnings and exceptions have been added in order to deal with all possible scenarios.
上级 96be471e
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theano/tensor/signal/pool.py
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...@@ -38,38 +38,96 @@ def max_pool_2d_same_size(input, patch_size): ...@@ -38,38 +38,96 @@ def max_pool_2d_same_size(input, patch_size):
return outs return outs
def pool_2d(input, ds, ignore_border=None, st=None, padding=(0, 0), def pool_2d(input, ws=None, ignore_border=None, stride=None, pad=(0, 0),
mode='max'): mode='max', ds=None, st=None, padding=None):
"""Downscale the input by a specified factor """Downscale the input by a specified factor
Takes as input a N-D tensor, where N >= 2. It downscales the input image by Takes as input a N-D tensor, where N >= 2. It downscales the input image by
the specified factor, by keeping only the maximum value of non-overlapping the specified factor, by keeping only the maximum value of non-overlapping
patches of size (ds[0],ds[1]) patches of size (ws[0],ws[1])
Parameters Parameters
---------- ----------
input : N-D theano tensor of input images input : N-D theano tensor of input images
Input images. Max pooling will be done over the 2 last dimensions. Input images. Max pooling will be done over the 2 last dimensions.
ds : tuple of length 2 or theano vector of ints of size 2. ws : tuple of length 2 or theano vector of ints of size 2.
Factor by which to downscale (vertical ds, horizontal ds). Factor by which to downscale (vertical ws, horizontal ws).
(2,2) will halve the image in each dimension. (2,2) will halve the image in each dimension.
ignore_border : bool (default None, will print a warning and set to False) ignore_border : bool (default None, will print a warning and set to False)
When True, (5,5) input with ds=(2,2) will generate a (2,2) output. When True, (5,5) input with ws=(2,2) will generate a (2,2) output.
(3,3) otherwise. (3,3) otherwise.
st : tuple of two ints or theano vector of ints of size 2. stride : tuple of two ints or theano vector of ints of size 2.
Stride size, which is the number of shifts over rows/cols to get the Stride size, which is the number of shifts over rows/cols to get the
next pool region. If st is None, it is considered equal to ds next pool region. If stride is None, it is considered equal to ws
(no overlap on pooling regions). (no overlap on pooling regions).
padding : tuple of two ints or theano vector of ints of size 2. pad : tuple of two ints or theano vector of ints of size 2.
(pad_h, pad_w), pad zeros to extend beyond four borders of the (pad_h, pad_w), pad zeros to extend beyond four borders of the
images, pad_h is the size of the top and bottom margins, and images, pad_h is the size of the top and bottom margins, and
pad_w is the size of the left and right margins. pad_w is the size of the left and right margins.
mode : {'max', 'sum', 'average_inc_pad', 'average_exc_pad'} mode : {'max', 'sum', 'average_inc_pad', 'average_exc_pad'}
Operation executed on each window. `max` and `sum` always exclude Operation executed on each window. `max` and `sum` always exclude
the padding in the computation. `average` gives you the choice to the pad in the computation. `average` gives you the choice to
include or exclude it. include or exclude it.
""" """
if ds is not None:
if ws is not None:
raise Exception(
"You can't provide different tuple values to 'ws' and 'ds'."
" You must provide only one of them as they both"
" refer to the window size. DEPRECATION NOTE: the"
" 'ds' parameter is not going to exist anymore as"
" it is going to be replaced by the parameter 'ws'."
)
else:
warnings.warn(
"DEPRECATION: the 'ds' parameter is not going to exist"
" anymore as it is going to be replaced by the parameter"
" 'ws'.",
stacklevel=2
)
ws = ds
elif ds is None and ws is None:
raise Exception(
"You must provide a tuple value for the window size."
)
if st is not None:
if stride is not None:
raise Exception(
"You can't provide different tuple values to 'stride' and"
" st. You must provide only one of them as they"
" both refer to the stride size. DEPRECATION NOTE: the"
" 'st' parameter is not going to exist anymore as"
" it is going to be replaced by the parameter 'stride'."
)
else:
warnings.warn(
"DEPRECATION: the 'st' parameter is not going to exist"
" anymore as it is going to be replaced by the parameter"
" 'stride'.",
stacklevel=2
)
stride = st
if padding is not None:
if pad is not (0, 0):
raise Exception(
"You can't provide different tuple value to 'pad' and"
" 'padding'. You must provide only one of them as they"
" both refer to the padding size. DEPRECATION NOTE: the"
" 'padding' parameter is not going to exist anymore as"
" it is going to be replaced by the parameter 'pad'."
)
else:
warnings.warn(
"DEPRECATION: the 'padding' parameter is not going to exist"
" anymore as it is going to be replaced by the parameter"
" 'pad'.",
stacklevel=2
)
pad = padding
if input.ndim < 2: if input.ndim < 2:
raise NotImplementedError('pool_2d requires a dimension >= 2') raise NotImplementedError('pool_2d requires a dimension >= 2')
if ignore_border is None: if ignore_border is None:
...@@ -81,48 +139,107 @@ def pool_2d(input, ds, ignore_border=None, st=None, padding=(0, 0), ...@@ -81,48 +139,107 @@ def pool_2d(input, ds, ignore_border=None, st=None, padding=(0, 0),
" On the GPU, using ignore_border=True is needed to use cuDNN." " On the GPU, using ignore_border=True is needed to use cuDNN."
" When using ignore_border=False and not using cuDNN, the only" " When using ignore_border=False and not using cuDNN, the only"
" GPU combination supported is when" " GPU combination supported is when"
" `ds == st and padding == (0, 0) and mode == 'max'`." " `ws == stride and pad == (0, 0) and mode == 'max'`."
" Otherwise, the convolution will be executed on CPU.", " Otherwise, the convolution will be executed on CPU.",
stacklevel=2) stacklevel=2)
ignore_border = False ignore_border = False
op = Pool(ignore_border, ndim=2, mode=mode) op = Pool(ignore_border, ndim=2, mode=mode)
output = op(input, ds, st, padding) output = op(input, ws, stride, pad)
return output return output
def pool_3d(input, ds, ignore_border=None, st=None, padding=(0, 0, 0), def pool_3d(input, ws=None, ignore_border=None, stride=None, pad=(0, 0, 0),
mode='max'): mode='max', ds=None, st=None, padding=None):
"""Downscale the input by a specified factor """Downscale the input by a specified factor
Takes as input a N-D tensor, where N >= 3. It downscales the input image by Takes as input a N-D tensor, where N >= 3. It downscales the input image by
the specified factor, by keeping only the maximum value of non-overlapping the specified factor, by keeping only the maximum value of non-overlapping
patches of size (ds[0],ds[1],ds[2]) patches of size (ws[0],ws[1],ws[2])
Parameters Parameters
---------- ----------
input : N-D theano tensor of input images input : N-D theano tensor of input images
Input images. Max pooling will be done over the 3 last dimensions. Input images. Max pooling will be done over the 3 last dimensions.
ds : tuple of length 3 or theano vector of ints of size 3 ws : tuple of length 3 or theano vector of ints of size 3
Factor by which to downscale (vertical ds, horizontal ds, depth ds). Factor by which to downscale (vertical ws, horizontal ws, depth ws).
(2,2,2) will halve the image in each dimension. (2,2,2) will halve the image in each dimension.
ignore_border : bool (default None, will print a warning and set to False) ignore_border : bool (default None, will print a warning and set to False)
When True, (5,5,5) input with ds=(2,2,2) will generate a (2,2,2) output. When True, (5,5,5) input with ws=(2,2,2) will generate a (2,2,2) output.
(3,3,3) otherwise. (3,3,3) otherwise.
st : tuple of three ints or theano vector of ints of size 3 stride : tuple of three ints or theano vector of ints of size 3
Stride size, which is the number of shifts over rows/cols/slices to get Stride size, which is the number of shifts over rows/cols/slices to get
the next pool region. If st is None, it is considered equal to ds the next pool region. If stride is None, it is considered equal to ws
(no overlap on pooling regions). (no overlap on pooling regions).
padding : tuple of two ints or theano vector of ints of size 3 pad : tuple of two ints or theano vector of ints of size 3
(pad_h, pad_w, pad_d), pad zeros to extend beyond six borders of the (pad_h, pad_w, pad_d), pad zeros to extend beyond six borders of the
images, pad_h is the size of the top and bottom margins, images, pad_h is the size of the top and bottom margins,
pad_w is the size of the left and right margins, and pad_d is the size pad_w is the size of the left and right margins, and pad_d is the size
of the front and back margins of the front and back margins
mode : {'max', 'sum', 'average_inc_pad', 'average_exc_pad'} mode : {'max', 'sum', 'average_inc_pad', 'average_exc_pad'}
Operation executed on each window. `max` and `sum` always exclude Operation executed on each window. `max` and `sum` always exclude
the padding in the computation. `average` gives you the choice to the pad in the computation. `average` gives you the choice to
include or exclude it. include or exclude it.
""" """
if ds is not None:
if ws is not None:
raise Exception(
"You can't provide different tuple values to 'ws' and 'ds'."
" You must provide only one of them as they both"
" refer to the window size. DEPRECATION NOTE: the"
" 'ds' parameter is not going to exist anymore as"
" it is going to be replaced by the parameter 'ws'."
)
else:
warnings.warn(
"DEPRECATION: the 'ds' parameter is not going to exist"
" anymore as it is going to be replaced by the parameter"
" 'ws'.",
stacklevel=2
)
ws = ds
elif ds is None and ws is None:
raise Exception(
"You must provide a tuple value for the window size."
)
if st is not None:
if stride is not None:
raise Exception(
"You can't provide different tuple values to 'stride' and"
" st. You must provide only one of them as they"
" both refer to the stride size. DEPRECATION NOTE: the"
" 'st' parameter is not going to exist anymore as"
" it is going to be replaced by the parameter 'stride'."
)
else:
warnings.warn(
"DEPRECATION: the 'st' parameter is not going to exist"
" anymore as it is going to be replaced by the parameter"
" 'stride'.",
stacklevel=2
)
stride = st
if padding is not None:
if pad is not (0, 0, 0):
raise Exception(
"You can't provide different tuple value to 'pad' and"
" 'padding'. You must provide only one of them as they"
" both refer to the padding size. DEPRECATION NOTE: the"
" 'padding' parameter is not going to exist anymore as"
" it is going to be replaced by the parameter 'pad'."
)
else:
warnings.warn(
"DEPRECATION: the 'padding' parameter is not going to exist"
" anymore as it is going to be replaced by the parameter"
" 'pad'.",
stacklevel=2
)
pad = padding
if input.ndim < 3: if input.ndim < 3:
raise NotImplementedError('pool_3d requires a dimension >= 3') raise NotImplementedError('pool_3d requires a dimension >= 3')
if ignore_border is None: if ignore_border is None:
...@@ -134,12 +251,12 @@ def pool_3d(input, ds, ignore_border=None, st=None, padding=(0, 0, 0), ...@@ -134,12 +251,12 @@ def pool_3d(input, ds, ignore_border=None, st=None, padding=(0, 0, 0),
" On the GPU, using ignore_border=True is needed to use cuDNN." " On the GPU, using ignore_border=True is needed to use cuDNN."
" When using ignore_border=False and not using cuDNN, the only" " When using ignore_border=False and not using cuDNN, the only"
" GPU combination supported is when" " GPU combination supported is when"
" `ds == st and padding == (0, 0, 0) and mode == 'max'`." " `ws == stride and pad == (0, 0, 0) and mode == 'max'`."
" Otherwise, the convolution will be executed on CPU.", " Otherwise, the convolution will be executed on CPU.",
stacklevel=2) stacklevel=2)
ignore_border = False ignore_border = False
op = Pool(ignore_border, ndim=3, mode=mode) op = Pool(ignore_border, ndim=3, mode=mode)
output = op(input, ds, st, padding) output = op(input, ws, stride, pad)
return output return output
...@@ -150,23 +267,23 @@ class Pool(OpenMPOp): ...@@ -150,23 +267,23 @@ class Pool(OpenMPOp):
Parameters Parameters
---------- ----------
ds : list or tuple of N ints ws : list or tuple of N ints
Downsample factor over rows, columns etc. Downsample factor over rows, columns etc.
ds indicates the size of the pooling region. ws indicates the size of the pooling region.
ignore_border : bool ignore_border : bool
If ds doesn't divide imgshape, do we include an extra row/col/slice If ws doesn't divide imgshape, do we include an extra row/col/slice
of partial downsampling (False) or ignore it (True). of partial downsampling (False) or ignore it (True).
st : list or tuple of N ints or None stride : list or tuple of N ints or None
Stride size, which is the number of shifts over rows/cols/slices to get the Stride size, which is the number of shifts over rows/cols/slices to get the
next pool region. If st is None, it is considered equal to ds next pool region. If stride is None, it is considered equal to ws
(no overlap on pooling regions). (no overlap on pooling regions).
padding : tuple of N ints or None pad : tuple of N ints or None
For each downsampling dimension, this specifies the number of zeros to For each downsampling dimension, this specifies the number of zeros to
add as padding on both sides. For 2D and (pad_h, pad_w), pad_h specifies the add as pad on both sides. For 2D and (pad_h, pad_w), pad_h specifies the
size of the top and bottom margins, pad_w specifies the size of the left and size of the top and bottom margins, pad_w specifies the size of the left and
right margins. No padding is added if padding is None. right margins. No pad is added if pad is None.
mode : {'max', 'sum', 'average_inc_pad', 'average_exc_pad'} mode : {'max', 'sum', 'average_inc_pad', 'average_exc_pad'}
('average_inc_pad' excludes the padding from the count, ('average_inc_pad' excludes the pad from the count,
'average_exc_pad' include it) 'average_exc_pad' include it)
ndim : int ndim : int
The number of pooling dimensions N. The number of pooling dimensions N.
...@@ -177,7 +294,8 @@ class Pool(OpenMPOp): ...@@ -177,7 +294,8 @@ class Pool(OpenMPOp):
__props__ = ('ignore_border', 'mode', 'ndim') __props__ = ('ignore_border', 'mode', 'ndim')
@staticmethod @staticmethod
def out_shape(imgshape, ds, ignore_border=False, st=None, padding=None, ndim=2): def out_shape(imgshape, ws=None, ignore_border=False, stride=None,
pad=None, ndim=2, ds=None, st=None, padding=None):
""" """
Return the shape of the output from this op, for input of given Return the shape of the output from this op, for input of given
shape and flags. shape and flags.
...@@ -187,21 +305,21 @@ class Pool(OpenMPOp): ...@@ -187,21 +305,21 @@ class Pool(OpenMPOp):
imgshape : tuple, list, or similar of integer or scalar Theano variable imgshape : tuple, list, or similar of integer or scalar Theano variable
The shape of a tensor of images. The last N elements are The shape of a tensor of images. The last N elements are
interpreted as the number of rows, and the number of cols. interpreted as the number of rows, and the number of cols.
ds : list or tuple of N ints ws : list or tuple of N ints
Downsample factor over rows and column. Downsample factor over rows and column.
ds indicates the pool region size. ws indicates the pool region size.
ignore_border : bool ignore_border : bool
If ds doesn't divide imgshape, do we include an extra row/col/slice If ws doesn't divide imgshape, do we include an extra row/col/slice
of partial downsampling (False) or ignore it (True). of partial downsampling (False) or ignore it (True).
st : list or tuple of N ints or None stride : list or tuple of N ints or None
Stride size, which is the number of shifts over rows/cols/slices to get the Stride size, which is the number of shifts over rows/cols/slices to get the
next pool region. If st is None, it is considered equal to ds next pool region. If stride is None, it is considered equal to ws
(no overlap on pooling regions). (no overlap on pooling regions).
padding : tuple of N ints or None pad : tuple of N ints or None
For each downsampling dimension, this specifies the number of zeros to For each downsampling dimension, this specifies the number of zeros to
add as padding on both sides. For 2D and (pad_h, pad_w), pad_h specifies the add as pad on both sides. For 2D and (pad_h, pad_w), pad_h specifies the
size of the top and bottom margins, pad_w specifies the size of the left and size of the top and bottom margins, pad_w specifies the size of the left and
right margins. No padding is added if padding is None. right margins. No pad is added if pad is None.
ndim : int ndim : int
The number of pooling dimensions N. The number of pooling dimensions N.
The default is 2. The default is 2.
...@@ -214,17 +332,76 @@ class Pool(OpenMPOp): ...@@ -214,17 +332,76 @@ class Pool(OpenMPOp):
elements reduced as per the downsampling & ignore_border flags. elements reduced as per the downsampling & ignore_border flags.
""" """
if ds is not None:
if ws is not None:
raise Exception(
"You can't provide different tuple values to 'ws' and"
" 'ds'. You must provide only one of them as they both"
" refer to the window size. DEPRECATION NOTE: the"
" 'ds' parameter is not going to exist anymore as"
" it is going to be replaced by the parameter 'ws'."
)
else:
warnings.warn(
"DEPRECATION: the 'ds' parameter is not going to exist"
" anymore as it is going to be replaced by the parameter"
" 'ws'.",
stacklevel=2
)
ws = ds
elif ds is None and ws is None:
raise Exception(
"You must provide a tuple value for the window size."
)
if st is not None:
if stride is not None:
raise Exception(
"You can't provide different tuple values to 'stride' and"
" st. You must provide only one of them as they"
" both refer to the stride size. DEPRECATION NOTE: the"
" 'st' parameter is not going to exist anymore as"
" it is going to be replaced by the parameter 'stride'."
)
else:
warnings.warn(
"DEPRECATION: the 'st' parameter is not going to exist"
" anymore as it is going to be replaced by the parameter"
" 'stride'.",
stacklevel=2
)
stride = st
if padding is not None:
if pad is not None:
raise Exception(
"You can't provide different tuple value to 'pad' and"
" 'padding'. You must provide only one of them as they"
" both refer to the padding size. DEPRECATION NOTE: the"
" 'padding' parameter is not going to exist anymore as"
" it is going to be replaced by the parameter 'pad'."
)
else:
warnings.warn(
"DEPRECATION: the 'padding' parameter is not going to"
" exist anymore as it is going to be replaced by the"
" parameter 'pad'.",
stacklevel=2
)
pad = padding
if ndim is None: if ndim is None:
ndim = 2 ndim = 2
assert ndim > 0 assert ndim > 0
if len(imgshape) < ndim: if len(imgshape) < ndim:
raise TypeError('imgshape must have at least {} dimensions'.format(ndim)) raise TypeError('imgshape must have at least {} dimensions'.format(ndim))
if st is None: if stride is None:
st = ds stride = ws
if padding is None: if pad is None:
padding = (0,) * ndim pad = (0,) * ndim
patch_shape = tuple(tensor.extract_constant(imgshape[-ndim + i]) + padding[i] * 2 patch_shape = tuple(tensor.extract_constant(imgshape[-ndim + i]) + pad[i] * 2
for i in xrange(ndim)) for i in xrange(ndim))
def compute_out(v, downsample, stride): def compute_out(v, downsample, stride):
...@@ -248,7 +425,7 @@ class Pool(OpenMPOp): ...@@ -248,7 +425,7 @@ class Pool(OpenMPOp):
else: else:
return max(0, (v - 1 - downsample + stride) // stride) + 1 return max(0, (v - 1 - downsample + stride) // stride) + 1
out_shape = [compute_out(patch_shape[i], ds[i], st[i]) for i in xrange(ndim)] out_shape = [compute_out(patch_shape[i], ws[i], stride[i]) for i in xrange(ndim)]
rval = list(imgshape[:-ndim]) + out_shape rval = list(imgshape[:-ndim]) + out_shape
return rval return rval
...@@ -266,11 +443,11 @@ class Pool(OpenMPOp): ...@@ -266,11 +443,11 @@ class Pool(OpenMPOp):
def prepare_node(self, node, storage_map, compute_map, impl): def prepare_node(self, node, storage_map, compute_map, impl):
if len(node.inputs) == 1: if len(node.inputs) == 1:
# Old interface # Old interface
self.ndim = len(node.op.ds) self.ndim = len(node.op.ws)
self.mode = node.op.mode self.mode = node.op.mode
ws = theano.tensor.constant(node.op.ds) ws = theano.tensor.constant(node.op.ws)
st = theano.tensor.constant(node.op.st) st = theano.tensor.constant(node.op.stride)
pad = theano.tensor.constant(node.op.padding) pad = theano.tensor.constant(node.op.pad)
node.inputs.append(ws) node.inputs.append(ws)
node.inputs.append(st) node.inputs.append(st)
node.inputs.append(pad) node.inputs.append(pad)
...@@ -715,7 +892,8 @@ class PoolGrad(OpenMPOp): ...@@ -715,7 +892,8 @@ class PoolGrad(OpenMPOp):
__props__ = ('ignore_border', 'mode', 'ndim') __props__ = ('ignore_border', 'mode', 'ndim')
@staticmethod @staticmethod
def out_shape(imgshape, ds, ignore_border=False, st=None, padding=None, ndim=2): def out_shape(imgshape, ws=None, ignore_border=False, stride=None, pad=None, ndim=2,
ds=None, st=None, padding=None):
"""Return the shape of the output from this op, for input of given """Return the shape of the output from this op, for input of given
shape and flags. shape and flags.
...@@ -724,21 +902,21 @@ class PoolGrad(OpenMPOp): ...@@ -724,21 +902,21 @@ class PoolGrad(OpenMPOp):
imgshape : tuple of integers or scalar Theano variables imgshape : tuple of integers or scalar Theano variables
the shape of a tensor of images. The last N elements are the shape of a tensor of images. The last N elements are
interpreted as the downsampling dimensions. interpreted as the downsampling dimensions.
ds : tuple of N ints ws : tuple of N ints
downsample factor over rows and columns this parameter downsample factor over rows and columns this parameter
indicates the size of the pooling region indicates the size of the pooling region
ignore_border : bool ignore_border : bool
If ds doesn't divide imgshape, do we include an extra row/col/slice If ws doesn't divide imgshape, do we include an extra row/col/slice
of partial downsampling (False) or ignore it (True). of partial downsampling (False) or ignore it (True).
st : list or tuple of N ints or None stride : list or tuple of N ints or None
Stride size, which is the number of shifts over rows/cols/slices to get the Stride size, which is the number of shifts over rows/cols/slices to get the
next pool region. If st is None, it is considered equal to ds next pool region. If stride is None, it is considered equal to ws
(no overlap on pooling regions). (no overlap on pooling regions).
padding : tuple of N ints or None pad : tuple of N ints or None
For each downsampling dimension, this specifies the number of zeros to For each downsampling dimension, this specifies the number of zeros to
add as padding on both sides. For 2D and (pad_h, pad_w), pad_h specifies the add as pad on both sides. For 2D and (pad_h, pad_w), pad_h specifies the
size of the top and bottom margins, pad_w specifies the size of the left and size of the top and bottom margins, pad_w specifies the size of the left and
right margins. No padding is added if padding is None. right margins. No pad is added if pad is None.
ndim : int ndim : int
The number of pooling dimensions N. The number of pooling dimensions N.
The default is 2. The default is 2.
...@@ -752,15 +930,72 @@ class PoolGrad(OpenMPOp): ...@@ -752,15 +930,72 @@ class PoolGrad(OpenMPOp):
ignore_border flags. ignore_border flags.
""" """
if ds is not None:
if ws is not None:
raise Exception(
"You can't provide different tuple values to 'ws' and"
" 'ds'. You must provide only one of them as they both"
" refer to the window size. DEPRECATION NOTE: the"
" 'ds' parameter is not going to exist anymore as"
" it is going to be replaced by the parameter 'ws'."
)
else:
warnings.warn(
"DEPRECATION: the 'ds' parameter is not going to exist"
" anymore as it is going to be replaced by the parameter"
" 'ws'.",
stacklevel=2
)
ws = ds
elif ds is None and ws is None:
raise Exception(
"You must provide a tuple value for the window size."
)
if st is not None:
if stride is not None:
raise Exception(
"You can't provide different tuple values to 'stride' and"
" st. You must provide only one of them as they"
" both refer to the stride size. DEPRECATION NOTE: the"
" 'st' parameter is not going to exist anymore as"
" it is going to be replaced by the parameter 'stride'."
)
else:
warnings.warn(
"DEPRECATION: the 'st' parameter is not going to exist"
" anymore as it is going to be replaced by the parameter"
" 'stride'.",
stacklevel=2
)
stride = st
if padding is not None:
if pad is not None:
raise Exception(
"You can't provide different tuple value to 'pad' and"
" 'padding'. You must provide only one of them as they"
" both refer to the padding size. DEPRECATION NOTE: the"
" 'padding' parameter is not going to exist anymore as"
" it is going to be replaced by the parameter 'pad'."
)
else:
warnings.warn(
"DEPRECATION: the 'padding' parameter is not going to"
" exist anymore as it is going to be replaced by the"
" parameter 'pad'.",
stacklevel=2
)
pad = padding
if len(imgshape) < ndim: if len(imgshape) < ndim:
raise TypeError('imgshape must have at least {} dimensions'.format(ndim)) raise TypeError('imgshape must have at least {} dimensions'.format(ndim))
if st is None: if stride is None:
st = ds stride = ws
if padding is None: if pad is None:
padding = (0,) * ndim pad = (0,) * ndim
patch_shape = tuple(tensor.extract_constant(imgshape[-ndim + i]) + padding[i] * 2 patch_shape = tuple(tensor.extract_constant(imgshape[-ndim + i]) + pad[i] * 2
for i in xrange(ndim)) for i in xrange(ndim))
def compute_out(v, downsample, stride): def compute_out(v, downsample, stride):
...@@ -781,7 +1016,7 @@ class PoolGrad(OpenMPOp): ...@@ -781,7 +1016,7 @@ class PoolGrad(OpenMPOp):
else: else:
return max(0, (v - 1 - downsample) // stride + 1) + 1 return max(0, (v - 1 - downsample) // stride + 1) + 1
out_shape = [compute_out(patch_shape[i], ds[i], st[i]) for i in xrange(ndim)] out_shape = [compute_out(patch_shape[i], ws[i], stride[i]) for i in xrange(ndim)]
rval = list(imgshape[:-ndim]) + out_shape rval = list(imgshape[:-ndim]) + out_shape
return rval return rval
...@@ -799,11 +1034,11 @@ class PoolGrad(OpenMPOp): ...@@ -799,11 +1034,11 @@ class PoolGrad(OpenMPOp):
def prepare_node(self, node, storage_map, compute_map, impl): def prepare_node(self, node, storage_map, compute_map, impl):
if len(node.inputs) < 5: # 5 for AveragePoolGrad, 6 for MaxPoolGrad if len(node.inputs) < 5: # 5 for AveragePoolGrad, 6 for MaxPoolGrad
# Old interface # Old interface
self.ndim = len(node.op.ds) self.ndim = len(node.op.ws)
self.mode = node.op.mode self.mode = node.op.mode
ws = theano.tensor.constant(node.op.ds) ws = theano.tensor.constant(node.op.ws)
st = theano.tensor.constant(node.op.st) st = theano.tensor.constant(node.op.stride)
pad = theano.tensor.constant(node.op.padding) pad = theano.tensor.constant(node.op.pad)
node.inputs.append(ws) node.inputs.append(ws)
node.inputs.append(st) node.inputs.append(st)
node.inputs.append(pad) node.inputs.append(pad)
......
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