def test_params_type_with_enums(self):
# Test that we fail if we create a params type with common enum names inside different enum types.
try:
ParamsType(enum1=EnumList("A", "B", "C"),
enum2=EnumList("A", "B", "F"))
except AttributeError:
pass
else:
raise Exception(
"ParamsType should fail with common enum names inside different enum types."
)
# Test that we fail if we create a params type with common names in both aliases and constants.
try:
ParamsType(
enum1=EnumList(("A", "a"), ("B", "b")),
enum2=EnumList(("ONE", "a"), ("TWO", "two")),
)
except AttributeError:
ParamsType(
enum1=EnumList(("A", "a"), ("B", "b")),
enum2=EnumList(("ONE", "one"), ("TWO", "two")),
)
else:
raise Exception(
"ParamsType should fail when there are aliases with same names as some constants."
)
# Test that we can access enum values through wrapper directly.
w = ParamsType(
enum1=EnumList("A", ("B", "beta"), "C"),
enum2=EnumList(("D", "delta"), "E", "F"),
)
assert w.A == 0 and w.B == 1 and w.C == 2
assert w.D == 0 and w.E == 1 and w.F == 2
# Test constants access through aliases.
assert w.enum_from_alias("beta") == w.B
assert w.enum_from_alias("delta") == w.D
assert (w.enum_from_alias("C") == w.C
) # C is not an alias, so it should return a constant named C.
# Test that other regular wrapper attributes are still available.
assert len(w.fields) == len(w.types) == w.length
assert w.name
def test_params_type_with_enums(self):
# Test that we fail if we create a params type with common enum names inside different enum types.
try:
ParamsType(enum1=EnumList('A', 'B', 'C'),
enum2=EnumList('A', 'B', 'F'))
except AttributeError:
pass
else:
raise Exception(
'ParamsType should fail with common enum names inside different enum types.'
)
# Test that we fail if we create a params type with common names in both aliases and constants.
try:
ParamsType(enum1=EnumList(('A', 'a'), ('B', 'b')),
enum2=EnumList(('ONE', 'a'), ('TWO', 'two')))
except AttributeError:
ParamsType(enum1=EnumList(('A', 'a'), ('B', 'b')),
enum2=EnumList(('ONE', 'one'), ('TWO', 'two')))
else:
raise Exception(
'ParamsType should fail when there are aliases with same names as some constants.'
)
# Test that we can access enum values through wrapper directly.
w = ParamsType(enum1=EnumList('A', ('B', 'beta'), 'C'),
enum2=EnumList(('D', 'delta'), 'E', 'F'))
assert w.A == 0 and w.B == 1 and w.C == 2
assert w.D == 0 and w.E == 1 and w.F == 2
# Test constants access through aliases.
assert w.enum_from_alias('beta') == w.B
assert w.enum_from_alias('delta') == w.D
assert w.enum_from_alias(
'C'
) == w.C # C is not an alias, so it should return a constant named C.
# Test that other regular wrapper attributes are still available.
assert len(w.fields) == len(w.types) == w.length
assert w.name
class BaseCorrMM(gof.OpenMPOp):
"""
Base class for `CorrMM`, `CorrMM_gradWeights` and
`CorrMM_gradInputs`. Cannot be used directly.
Every sub-class must define internal attribute ``_direction`` out of __init__().
``_direction`` must take one of following values:
- "forward" to correlate bottom with weights and store results in top.
- "backprop weights" to do a valid convolution of bottom with top
(swapping the first two dimensions) and store results in weights.
- "backprop inputs" to do a full convolution of top with weights
(swapping the first two dimensions) and store results in bottom.
Parameters
----------
border_mode : {'valid', 'full', 'half'}
Additionally, the padding size could be directly specified by an integer,
a pair of integers, or two pairs of integers.
subsample
Perform subsampling of the output (default: (1, 1)).
filter_dilation
Perform dilated correlation (default: (1,1))
num_groups
Perform grouped convolutions (default: 1)
unshared
Perform unshared correlation (default: False)
"""
check_broadcast = False
__props__ = (
"border_mode",
"subsample",
"filter_dilation",
"num_groups",
"unshared",
)
_direction = None
params_type = ParamsType(
direction=EnumList(
("DIRECTION_FORWARD", "forward"), # 0
("DIRECTION_BACKPROP_WEIGHTS", "backprop weights"), # 1
("DIRECTION_BACKPROP_INPUTS", "backprop inputs"),
), # 2
dH=int64,
dW=int64,
dilH=int64,
dilW=int64,
padH_l=int64,
padH_r=int64,
padW_l=int64,
padW_r=int64,
num_groups=int64,
unshared=int8,
)
def __init__(
self,
border_mode="valid",
subsample=(1, 1),
filter_dilation=(1, 1),
num_groups=1,
unshared=False,
openmp=None,
):
super(BaseCorrMM, self).__init__(openmp=openmp)
if isinstance(border_mode, integer_types):
if border_mode < 0:
raise ValueError(
"invalid border_mode {}, which must be a "
"non-negative integer".format(border_mode)
)
border_mode = ((border_mode, border_mode),) * 2
elif isinstance(border_mode, tuple):
if len(border_mode) != 2:
raise ValueError(
"invalid border_mode {} which must be a "
"tuple of length 2".format(border_mode)
)
border = ()
for mode in border_mode:
if isinstance(mode, tuple) and len(mode) == 2 and min(mode) >= 0:
border += ((int(mode[0]), int(mode[1])),)
elif mode >= 0:
border += ((int(mode), int(mode)),)
else:
raise ValueError(
"invalid border mode {}. The tuple can only contain "
"integers or tuples of length 2".format(border_mode)
)
border_mode = border
elif border_mode not in ("valid", "full", "half"):
raise ValueError(
"invalid border_mode {}, which must be either "
'"valid", "full", "half", an integer or a tuple '
"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")
if len(filter_dilation) != 2:
raise ValueError("filter_dilation must have two elements")
self.subsample = tuple(subsample)
self.filter_dilation = tuple(filter_dilation)
self.unshared = unshared
if not theano.config.blas.ldflags:
# Theano will use a NumPy C implementation of [sd]gemm_ instead.
self.blas_type = ""
else:
if "openblas" in theano.config.blas.ldflags:
self.blas_type = "openblas"
elif "mkl" in theano.config.blas.ldflags:
self.blas_type = "mkl"
else:
self.blas_type = ""
if self._direction not in ["forward", "backprop weights", "backprop inputs"]:
raise ValueError(
"_direction must be one of 'forward', "
"'backprop weights', 'backprop inputs'"
)
if num_groups < 1:
raise ValueError("Number of groups should be greater than 0")
self.num_groups = num_groups
@property
def pad(self):
if self.border_mode == "half":
return ((-1, -1),) * 2
elif self.border_mode == "full":
return ((-2, -2),) * 2
elif isinstance(self.border_mode, tuple):
return self.border_mode
else:
assert self.border_mode == "valid"
return ((0, 0),) * 2
# Direction should be converted to real enum value,
# as it is compared to integer later in c_code_helper().
direction = property(lambda self: self.params_type.enum_from_alias(self._direction))
dH = property(lambda self: self.subsample[0])
dW = property(lambda self: self.subsample[1])
dilH = property(lambda self: self.filter_dilation[0])
dilW = property(lambda self: self.filter_dilation[1])
padH_l = property(lambda self: self.pad[0][0])
padH_r = property(lambda self: self.pad[0][1])
padW_l = property(lambda self: self.pad[1][0])
padW_r = property(lambda self: self.pad[1][1])
def __str__(self):
return "%s{%s, %s, %s, %s %s}" % (
self.__class__.__name__,
self.border_mode,
str(self.subsample),
str(self.filter_dilation),
str(self.num_groups),
str(self.unshared),
)
@staticmethod
def as_common_dtype(in1, in2):
"""
Upcast input variables if necessary.
"""
dtype = theano.scalar.upcast(in1.dtype, in2.dtype)
return in1.astype(dtype), in2.astype(dtype)
def __setstate__(self, d):
self.__dict__.update(d)
if not hasattr(self, "num_groups"):
self.num_groups = 1
def c_support_code(self):
ccodes = blas_headers.blas_header_text()
if self.blas_type == "openblas":
ccodes += blas_headers.openblas_threads_text()
elif self.blas_type == "mkl":
ccodes += blas_headers.mkl_threads_text()
return ccodes
def c_libraries(self):
return ldflags()
def c_compile_args(self):
compile_args = ldflags(libs=False, flags=True)
compile_args += super(BaseCorrMM, self).c_compile_args()
return compile_args
def c_lib_dirs(self):
return ldflags(libs=False, libs_dir=True)
def c_header_dirs(self):
return ldflags(libs=False, include_dir=True)
def c_headers(self):
headers = ["<stdio.h>"]
headers += super(BaseCorrMM, self).c_headers()
return headers
def c_code_cache_version(self):
# raise this whenever modifying any of the support_code_files
return (10, self.openmp, blas_header_version())
def c_support_code_apply(self, node, nodename):
# REMEMBER TO RAISE c_code_cache_version when changing any of
# these files
sub = {}
dtype = str(node.__dict__["inputs"][0].dtype)
assert dtype in ("float32", "float64")
if dtype == "float32":
sub["gemm"] = "sgemm_"
sub["gemv"] = "sgemv_"
sub["float_type"] = "npy_float"
sub["float_typenum"] = "NPY_FLOAT"
sub["n_bytes"] = 4
sub["c_float_type"] = "float"
else:
sub["gemm"] = "dgemm_"
sub["gemv"] = "dgemv_"
sub["float_type"] = "npy_double"
sub["float_typenum"] = "NPY_DOUBLE"
sub["n_bytes"] = 8
sub["c_float_type"] = "double"
if self.openmp:
sub["omp_flags"] = "#pragma omp parallel for schedule(static)"
sub["omp_get_max_threads"] = "omp_get_max_threads()"
sub["omp_get_thread_num"] = "omp_get_thread_num()"
if self.blas_type == "openblas":
sub["blas_set_num_threads"] = "openblas_set_num_threads"
sub["blas_get_num_threads"] = "openblas_get_num_threads()"
elif self.blas_type == "mkl":
sub["blas_set_num_threads"] = "mkl_set_num_threads"
sub["blas_get_num_threads"] = "mkl_get_max_threads()"
else:
sub["blas_set_num_threads"] = ""
sub["blas_get_num_threads"] = "0"
else:
sub["omp_flags"] = ""
sub["omp_get_max_threads"] = "1"
sub["omp_get_thread_num"] = "0"
sub["blas_set_num_threads"] = ""
sub["blas_get_num_threads"] = "0"
files = [os.path.join("c_code", "corr_gemm.c")]
codes = [
open(os.path.join(os.path.split(__file__)[0], f)).read() for f in files
]
final_code = ""
for code in codes:
final_code += code
return final_code % sub
def c_code_helper(self, bottom, weights, top, sub, height=None, width=None):
"""
This generates the C code for CorrMM (direction="forward"),
CorrMM_gradWeights (direction="backprop weights"), and
CorrMM_gradInputs (direction="backprop inputs").
Depending on the direction, one of bottom, weights, top will
receive the output, while the other two serve as inputs.
:param bottom: Variable name of the input images in the forward pass,
or the gradient of the input images in backprop wrt. inputs
:param weights: Variable name of the filters in the forward pass,
or the gradient of the filters in backprop wrt. weights
:param top: Variable name of the output images / feature maps in the
forward pass, or the gradient of the outputs in the backprop passes
:param sub: Dictionary of substitutions useable to help generating the
C code.
:param height: If self.subsample[0] != 1, a variable giving the height
of the filters for direction="backprop weights" or the height of
the input images for direction="backprop inputs".
If self.border_mode == 'half', a variable giving the height of the
filters for direction="backprop weights". Ignored otherwise.
:param width: If self.subsample[1] != 1, a variable giving the width
of the filters for direction="backprop weights" or the width of the
input images for direction="backprop inputs".
If self.border_mode == 'half', a variable giving the width of the
filters for direction="backprop weights". Ignored otherwise.
"""
# When subsampling, we cannot unambiguously infer the height and width
# of bottom and weights from top, so we require them to be given.
# Similarly, when border_mode="half", we cannot infer the weight size.
if height:
height = "(*(npy_int64 *)(PyArray_DATA(%s)))" % height
else:
if ((self.direction != 0) and (self.dH != 1)) or (
(self.direction == 1) and (self.padH_l == -1 or self.padH_r == -1)
):
raise ValueError(
"height must be given for backprop with vertical sampling or border_mode='half'"
)
height = "-1"
if width:
width = "(*(npy_int64 *)(PyArray_DATA(%s)))" % width
else:
if ((self.direction != 0) and (self.dW != 1)) or (
(self.direction == 1) and (self.padW_l == -1 or self.padW_r == -1)
):
raise ValueError(
"width must be given for backprop with horizontal sampling or border_mode='half'"
)
width = "-1"
return """
// Mandatory args
int direction = %(params)s->direction; // forward, bprop weights, bprop inputs
// Optional args
int dH = %(params)s->dH;
int dW = %(params)s->dW;
int dilH = %(params)s->dilH;
int dilW = %(params)s->dilW;
int padH_l = %(params)s->padH_l;
int padH_r = %(params)s->padH_r;
int padW_l = %(params)s->padW_l;
int padW_r = %(params)s->padW_r;
int numgroups = %(params)s->num_groups;
int unshared = %(params)s->unshared;
PyArrayObject * bottom = %(bottom)s;
PyArrayObject * weights = %(weights)s;
PyArrayObject * top = %(top)s;
PyArrayObject * out2 = NULL;
PyArrayObject **out = NULL;
switch(%(params)s->direction) {
case DIRECTION_FORWARD:
out = &%(top)s;
break;
case DIRECTION_BACKPROP_WEIGHTS:
out = &%(weights)s;
break;
case DIRECTION_BACKPROP_INPUTS:
out = &%(bottom)s;
break;
default:
PyErr_SetString(PyExc_ValueError, "CPU CorrMM: Invalid direction.");
{%(fail)s}
break;
}
int wdim, odim;
wdim = unshared ? 6 : 4;
odim = 4; //Can be set to 6 later for unshared backprop wrt weights
// Obtain or infer kernel width and height
// (we need to know it early to be able to handle auto-padding)
int kH, kW, dil_kH, dil_kW;
if (direction != 1) {
// weight is an input variable, we can just read its shape
kH = PyArray_DIMS(weights)[wdim-2];
kW = PyArray_DIMS(weights)[wdim-1];
}
else {
if (%(height)s != -1) {
// kernel height is specified (perhaps vertical subsampling or half padding)
kH = %(height)s;
}
else if (padH_l == -2 || padH_r == -2) {
// vertical full padding, we can infer the kernel height
kH = (2 - PyArray_DIMS(bottom)[2] + (PyArray_DIMS(top)[2] - 1) * dH - 1)/ dilH + 1;
}
else {
// explicit padding, we can infer the kernel height
kH = (PyArray_DIMS(bottom)[2] + padH_l + padH_r - (PyArray_DIMS(top)[2] - 1) * dH - 1) / dilH +1;
}
if (%(width)s != -1) {
// kernel width is specified (perhaps horizontal subsampling or half padding)
kW = %(width)s;
}
else if (padW_l == -2 || padW_r == -2) {
kW = (2 - PyArray_DIMS(bottom)[3] + (PyArray_DIMS(top)[3] - 1) * dW - 1) / dilW + 1;
}
else {
kW = (PyArray_DIMS(bottom)[3] + padW_l + padW_r - (PyArray_DIMS(top)[3] - 1) * dW - 1) / dilW + 1;
}
}
// Implicit dilated kernel size
dil_kH = (kH - 1) * dilH + 1;
dil_kW = (kW - 1) * dilW + 1;
// Auto-padding if requested
if (padH_l == -1 || padH_r == -1) { // vertical half padding
padH_l = padH_r = dil_kH / 2;
}
else if (padH_l == -2 || padH_r == -2) { // vertical full padding
padH_l = padH_r = dil_kH - 1;
}
else if (padH_l < -2 || padH_r < -2) {
PyErr_SetString(PyExc_ValueError, "BaseCorrMM: padH_l and padH_r must be >= -2");
%(fail)s
}
if (padW_l == -1 || padW_r == -1) { // horizontal half padding
padW_l = padW_r = dil_kW / 2;
}
else if (padW_l == -2 || padW_r == -2) { // horizontal full padding
padW_l = padW_r = dil_kW - 1;
}
else if (padW_l < -2 || padW_r < -2) {
PyErr_SetString(PyExc_ValueError, "BaseCorrMM: padW_l and padW_r must be >= -2");
%(fail)s
}
// Infer output shape
npy_intp out_dim[6];
out_dim[4] = out_dim[5] = 0; //Only used for unshared backprop wrt weights
switch(direction) {
case 0: // forward pass
// output is top: (batchsize, num_filters, height, width)
// height and width: top = (bottom + pad_l + pad_r - ((weight-1)*dil + 1)) / sample + 1
out_dim[0] = (npy_intp)PyArray_DIMS(bottom)[0];
out_dim[1] = (npy_intp)PyArray_DIMS(weights)[0];
out_dim[2] = (npy_intp)((PyArray_DIMS(bottom)[2] + padH_l + padH_r - ((PyArray_DIMS(weights)[wdim-2]-1)*dilH + 1)) / dH + 1);
out_dim[3] = (npy_intp)((PyArray_DIMS(bottom)[3] + padW_l + padW_r - ((PyArray_DIMS(weights)[wdim-1]-1)*dilW + 1)) / dW + 1);
if (out_dim[0] < 0 || out_dim[1] < 0 || out_dim[2] <= 0 || out_dim[3] <= 0)
{
if (unshared) {
PyErr_Format(PyExc_ValueError,
"CorrMM: impossible output shape\\n"
" bottom shape: %%ld x %%ld x %%ld x %%ld\\n"
" weights shape: %%ld x %%ld x %%ld x %%ld x %%ld x %%ld\\n"
" top shape: %%ld x %%ld x %%ld x %%ld\\n",
(long int)PyArray_DIMS(bottom)[0], (long int)PyArray_DIMS(bottom)[1],
(long int)PyArray_DIMS(bottom)[2], (long int)PyArray_DIMS(bottom)[3],
(long int)PyArray_DIMS(weights)[0], (long int)PyArray_DIMS(weights)[1],
(long int)PyArray_DIMS(weights)[2], (long int)PyArray_DIMS(weights)[3],
(long int)PyArray_DIMS(weights)[4], (long int)PyArray_DIMS(weights)[5],
(long int)out_dim[0], (long int)out_dim[1], (long int)out_dim[2],
(long int)out_dim[3]);
}
else {
PyErr_Format(PyExc_ValueError,
"CorrMM: impossible output shape\\n"
" bottom shape: %%ld x %%ld x %%ld x %%ld\\n"
" weights shape: %%ld x %%ld x %%ld x %%ld\\n"
" top shape: %%ld x %%ld x %%ld x %%ld\\n",
(long int)PyArray_DIMS(bottom)[0], (long int)PyArray_DIMS(bottom)[1],
(long int)PyArray_DIMS(bottom)[2], (long int)PyArray_DIMS(bottom)[3],
(long int)PyArray_DIMS(weights)[0], (long int)PyArray_DIMS(weights)[1],
(long int)PyArray_DIMS(weights)[2], (long int)PyArray_DIMS(weights)[3],
(long int)out_dim[0], (long int)out_dim[1], (long int)out_dim[2],
(long int)out_dim[3]);
}
%(fail)s
}
break;
case 1: // backprop wrt. weights
// output is weights: (num_filters, num_channels, height, width)
// height and width: weights = (bottom + pad_l + pad_r - (top - 1) * sample - 1) / dil + 1
out_dim[0] = (npy_intp)PyArray_DIMS(top)[1];
if (unshared){
odim = 6;
out_dim[1] = (npy_intp)PyArray_DIMS(top)[2];
out_dim[2] = (npy_intp)PyArray_DIMS(top)[3];
}
out_dim[wdim-3] = (npy_intp)PyArray_DIMS(bottom)[1] / numgroups;
out_dim[wdim-2] = (npy_intp)kH; // already inferred further above
out_dim[wdim-1] = (npy_intp)kW; // how convenient
if (unshared) {
if (out_dim[0] < 0 || out_dim[1] <= 0 || out_dim[2] <= 0 || out_dim[3] < 0
|| out_dim[4] <= 0 || out_dim[5] <= 0){
PyErr_Format(PyExc_ValueError,
"CorrMM backprop wrt. weights: impossible output shape\\n"
" bottom shape: %%ld x %%ld x %%ld x %%ld\\n"
" weights shape: %%ld x %%ld x %%ld x %%ld x %%ld x %%ld\\n"
" top shape: %%ld x %%ld x %%ld x %%ld\\n",
(long int)PyArray_DIMS(bottom)[0], (long int)PyArray_DIMS(bottom)[1],
(long int)PyArray_DIMS(bottom)[2], (long int)PyArray_DIMS(bottom)[3],
(long int)out_dim[0], (long int)out_dim[1], (long int)out_dim[2],
(long int)out_dim[3], (long int)out_dim[4], (long int)out_dim[5],
(long int)PyArray_DIMS(top)[0], (long int)PyArray_DIMS(top)[1],
(long int)PyArray_DIMS(top)[2], (long int)PyArray_DIMS(top)[3]);
%(fail)s
}
}
else {
if (out_dim[0] < 0 || out_dim[1] < 0 || out_dim[2] <= 0 || out_dim[3] <= 0)
{
PyErr_Format(PyExc_ValueError,
"CorrMM backprop wrt. weights: impossible output shape\\n"
" bottom shape: %%ld x %%ld x %%ld x %%ld\\n"
" weights shape: %%ld x %%ld x %%ld x %%ld\\n"
" top shape: %%ld x %%ld x %%ld x %%ld\\n",
(long int)PyArray_DIMS(bottom)[0], (long int)PyArray_DIMS(bottom)[1],
(long int)PyArray_DIMS(bottom)[2], (long int)PyArray_DIMS(bottom)[3],
(long int)out_dim[0], (long int)out_dim[1], (long int)out_dim[2],
(long int)out_dim[3],
(long int)PyArray_DIMS(top)[0], (long int)PyArray_DIMS(top)[1],
(long int)PyArray_DIMS(top)[2], (long int)PyArray_DIMS(top)[3]);
%(fail)s
}
}
break;
case 2: // backprop wrt. inputs
// output is bottom: (batchsize, num_channels, height, width)
// height and width: bottom = (top - 1) * sample + (weights-1)*dil + 1 - 2*pad
out_dim[0] = (npy_intp)PyArray_DIMS(top)[0];
out_dim[1] = (npy_intp)PyArray_DIMS(weights)[wdim-3] * numgroups;
out_dim[2] = (npy_intp)((%(height)s != -1) ? %(height)s : (PyArray_DIMS(top)[2] - 1) * dH + (PyArray_DIMS(weights)[wdim-2]-1)*dilH + 1 - padH_l - padH_r);
out_dim[3] = (npy_intp)((%(width)s != -1) ? %(width)s : (PyArray_DIMS(top)[3] - 1) * dW + (PyArray_DIMS(weights)[wdim-1]-1)*dilW + 1 - padW_l - padW_r);
if (unshared) {
if (out_dim[0] < 0 || out_dim[1] < 0 || out_dim[2] <= 0 || out_dim[3] <= 0)
{
PyErr_Format(PyExc_ValueError,
"CorrMM backprop wrt. inputs: impossible output shape\\n"
" bottom shape: %%ld x %%ld x %%ld x %%ld\\n"
" weights shape: %%ld x %%ld x %%ld x %%ld x %%ld x %%ld\\n"
" top shape: %%ld x %%ld x %%ld x %%ld\\n",
(long int)out_dim[0], (long int)out_dim[1], (long int)out_dim[2],
(long int)out_dim[3],
(long int)PyArray_DIMS(weights)[0], (long int)PyArray_DIMS(weights)[1],
(long int)PyArray_DIMS(weights)[2], (long int)PyArray_DIMS(weights)[3],
(long int)PyArray_DIMS(weights)[4], (long int)PyArray_DIMS(weights)[5],
(long int)PyArray_DIMS(top)[0], (long int)PyArray_DIMS(top)[1],
(long int)PyArray_DIMS(top)[2], (long int)PyArray_DIMS(top)[3]);
%(fail)s
}
}
else {
if (out_dim[0] < 0 || out_dim[1] < 0 || out_dim[2] <= 0 || out_dim[3] <= 0)
{
PyErr_Format(PyExc_ValueError,
"CorrMM backprop wrt. inputs: impossible output shape\\n"
" bottom shape: %%ld x %%ld x %%ld x %%ld\\n"
" weights shape: %%ld x %%ld x %%ld x %%ld\\n"
" top shape: %%ld x %%ld x %%ld x %%ld\\n",
(long int)out_dim[0], (long int)out_dim[1], (long int)out_dim[2],
(long int)out_dim[3],
(long int)PyArray_DIMS(weights)[0], (long int)PyArray_DIMS(weights)[1],
(long int)PyArray_DIMS(weights)[2], (long int)PyArray_DIMS(weights)[3],
(long int)PyArray_DIMS(top)[0], (long int)PyArray_DIMS(top)[1],
(long int)PyArray_DIMS(top)[2], (long int)PyArray_DIMS(top)[3]);
%(fail)s
}
}
break;
default:
PyErr_SetString(PyExc_ValueError, "BaseCorrMM: direction must be 0, 1, or 2\\n");
%(fail)s
}
// Prepare output array
int typenum;
int failure;
failure = !(*out
&& PyArray_NDIM(*out)==odim
&& PyArray_IS_C_CONTIGUOUS(*out)
&& PyArray_DIMS(*out)[0]==out_dim[0]
&& PyArray_DIMS(*out)[1]==out_dim[1]
&& PyArray_DIMS(*out)[2]==out_dim[2]
&& PyArray_DIMS(*out)[3]==out_dim[3]);
if (odim == 6){
failure = failure || !(PyArray_DIMS(*out)[4]==out_dim[4]
&& PyArray_DIMS(*out)[5]==out_dim[5]);
}
if ( failure )
{
Py_XDECREF(*out);
if (direction != 1) {
typenum = PyArray_TYPE(weights);
}
else {
typenum = PyArray_TYPE(bottom);
}
//Change to PyArray_ZEROS which is faster than PyArray_EMPTY.
*out = (PyArrayObject*)PyArray_ZEROS(odim,
out_dim,
typenum,
0);
if (NULL == *out)
{
if (odim == 4) {
PyErr_Format(PyExc_RuntimeError,
"BaseCorrMM: Failed to allocate output of %%lld x %%lld x %%lld x %%lld",
(long long)out_dim[0], (long long)out_dim[1], (long long)out_dim[2], (long long)out_dim[3]);
}
if (odim == 6) {
PyErr_Format(PyExc_RuntimeError,
"BaseCorrMM: Failed to allocate output of %%lld x %%lld x %%lld x %%lld %%lld %%lld",
(long long)out_dim[0], (long long)out_dim[1], (long long)out_dim[2], (long long)out_dim[3],
(long long)out_dim[4], (long long)out_dim[5]);
}
%(fail)s
}
}
// Call corrMM code
out2 = corrMM(%(bottom)s, %(weights)s, %(top)s, direction, dH, dW, dilH, dilW,
padH_l, padH_r, padW_l, padW_r, numgroups, unshared);
if (out2==NULL){
%(fail)s
}
assert (out2 == *out);
""" % dict(
bottom=bottom,
weights=weights,
top=top,
height=height,
width=width,
fail=sub["fail"],
params=sub["params"],
)
class CumOp(theano.Op):
# See function cumsum/cumprod for docstring
__props__ = ("axis", "mode")
check_input = False
params_type = ParamsType(c_axis=int_t,
mode=EnumList(('MODE_ADD', 'add'),
('MODE_MUL', 'mul')))
def __init__(self, axis=None, mode='add'):
if mode not in ('add', 'mul'):
raise ValueError('%s: Unknown mode "%s"' %
(type(self).__name__, mode))
self.axis = axis
self.mode = mode
c_axis = property(lambda self: np.MAXDIMS
if self.axis is None else self.axis)
def make_node(self, x):
x = basic.as_tensor_variable(x)
out_type = x.type()
if self.axis is None:
out_type = theano.tensor.vector(dtype=x.dtype) # Flatten
elif self.axis >= x.ndim or self.axis < -x.ndim:
raise ValueError('axis(={0}) out of bounds'.format(self.axis))
return theano.Apply(self, [x], [out_type])
def perform(self, node, inputs, output_storage, params):
x = inputs[0]
z = output_storage[0]
z[0] = {
'add': np.cumsum,
'mul': np.cumprod
}[self.mode](x, axis=self.axis)
def grad(self, inputs, output_gradients):
x, = inputs
gi, = output_gradients
if self.axis is None:
if self.mode == 'add':
return [cumsum(gi[::-1])[::-1].reshape(x.shape)]
elif self.mode == 'mul':
fx = cumprod(x, axis=self.axis)
return [cumsum((fx * gi)[::-1])[::-1].reshape(x.shape) / x]
else:
raise NotImplementedError(
'%s: unknown gradient for mode "%s"' %
(type(self).__name__, self.mode))
reverse_slicing = [slice(None, None, None)] * gi.ndim
reverse_slicing[self.axis] = slice(None, None, -1)
reverse_slicing = tuple(reverse_slicing)
# We need to reverse the gradients along ``self.axis``,
# compute cumsum, then reverse again
if self.mode == 'add':
return [cumsum(gi[reverse_slicing], self.axis)[reverse_slicing]]
elif self.mode == 'mul':
fx = cumprod(x, axis=self.axis)
return [
cumsum(
(fx * gi)[reverse_slicing], self.axis)[reverse_slicing] / x
]
else:
raise NotImplementedError('%s: unknown gradient for mode "%s"' %
(type(self).__name__, self.mode))
def infer_shape(self, node, shapes):
if self.axis is None:
return [(tensor.prod(shapes[0]), )] # Flatten
return shapes
def c_code(self, node, name, inames, onames, sub):
x, = inames
z, = onames
axis = self.axis
fail = sub['fail']
params = sub['params']
code = """
int axis = %(params)s->c_axis;
if (axis == 0 && PyArray_NDIM(%(x)s) == 1)
axis = NPY_MAXDIMS;
npy_intp shape[1] = { PyArray_SIZE(%(x)s) };
if(axis == NPY_MAXDIMS && !(%(z)s && PyArray_DIMS(%(z)s)[0] == shape[0]))
{
Py_XDECREF(%(z)s);
%(z)s = (PyArrayObject*) PyArray_SimpleNew(1, shape, PyArray_TYPE((PyArrayObject*) py_%(x)s));
}
else if(axis != NPY_MAXDIMS && !(%(z)s && PyArray_CompareLists(PyArray_DIMS(%(z)s), PyArray_DIMS(%(x)s), PyArray_NDIM(%(x)s))))
{
Py_XDECREF(%(z)s);
%(z)s = (PyArrayObject*) PyArray_SimpleNew(PyArray_NDIM(%(x)s), PyArray_DIMS(%(x)s), PyArray_TYPE(%(x)s));
}
if (!%(z)s)
%(fail)s;
{
PyObject * t = NULL;
if(%(params)s->mode == MODE_ADD)
t = PyArray_CumSum(
%(x)s, axis,
PyArray_TYPE(%(x)s), %(z)s);
else if(%(params)s->mode == MODE_MUL)
t = PyArray_CumProd(
%(x)s, axis,
PyArray_TYPE(%(x)s), %(z)s);
if (!t){
%(fail)s;
}
// Because PyArray_CumSum/CumProd returns a newly created reference on t.
Py_XDECREF(t);
}
""" % locals()
return code
def c_code_cache_version(self):
return (8, )
def __str__(self):
return "%s{%s, %s}" % (self.__class__.__name__, self.axis, self.mode)
class Images2Neibs(Op):
"""
Reshapes the input as a 2D tensor where each row is an pooling
example.
Parameters
----------
mode : {'valid', 'ignore_borders', 'wrap_centered'}
- 'valid' :
Requires an input that is a multiple of the pooling factor
(in each direction).
- 'half' :
Equivalent to 'valid' if we pre-pad with zeros the input on
each side by (neib_shape[0]//2, neib_shape[1]//2)
- 'full' :
Equivalent to 'valid' if we pre-pad with zeros the input on
each side by (neib_shape[0] - 1, neib_shape[1] - 1)
- 'ignore_borders' :
Same as valid, but will ignore the borders if the shape(s)
of the input is not a multiple of the pooling factor(s).
- 'wrap_centered' :
?? TODO comment
"""
__props__ = ("mode", )
BORDER_MODE = EnumList(
("MODE_VALID", "valid"),
("MODE_HALF", "half"),
("MODE_FULL", "full"),
("MODE_WRAP_CENTERED", "wrap_centered"),
("MODE_IGNORE_BORDERS", "ignore_borders"),
)
params_type = BORDER_MODE
def get_params(self, node):
return self.mode
def __init__(self, mode="valid"):
implemented_modes = self.BORDER_MODE.get_aliases()
if mode not in implemented_modes:
raise NotImplementedError(
f"Only modes {', '.join(implemented_modes)} have been implemented for {type(self).__name__}"
)
self.mode = mode
def __str__(self):
return self.__class__.__name__ + "{%s}" % self.mode
def __setstate__(self, d):
self.__dict__.update(d)
if not hasattr(self, "mode"):
self.mode = "valid"
def make_node(self, ten4, neib_shape, neib_step=None):
"""
Parameters
----------
ten4 : a list of lists of images
ten4 is of shape (list 1 dim, list 2 dim, row, col).
neib_shape
(r,c) where r is the height of the neighborhood in rows and c is
the width of the neighborhood in columns.
neib_step
(dr,dc) where dr is the number of rows to skip between patch and dc
is the number of columns. When None, this is the same as neib_shape
(patch are disjoint).
Returns
-------
matrix
A 2D matrix, written using the following pattern::
idx = 0
for i in range(list 1 dim)
for j in range(list 2 dim)
for k in <image column coordinates>
for l in <image row coordinates>
output[idx,:]
= flattened version of ten4[i,j,l:l+r,k:k+c]
idx += 1
.. note:: The op isn't necessarily implemented internally with these
for loops, they're just the easiest way to describe the output
pattern.
"""
ten4 = tt.as_tensor_variable(ten4)
neib_shape = tt.as_tensor_variable(neib_shape)
if neib_step is None:
neib_step = neib_shape
else:
neib_step = tt.as_tensor_variable(neib_step)
assert ten4.ndim == 4
assert neib_shape.ndim == 1
assert neib_step.ndim == 1
return Apply(self, [ten4, neib_shape, neib_step],
[tt.matrix(dtype=ten4.type.dtype)])
def grad(self, inp, grads):
x, neib_shape, neib_step = inp
(gz, ) = grads
if self.mode in ["valid", "ignore_borders"]:
if (neib_shape is neib_step or neib_shape == neib_step or
# Theano Constant == do not compare the data
# the equals function do that.
(hasattr(neib_shape, "equals") and neib_shape.equals(neib_step)
)):
return [
neibs2images(gz, neib_shape, x.shape, mode=self.mode),
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step),
]
if self.mode in ["valid"]:
# Iterate over neighborhood positions, summing contributions.
def pos2map(pidx, pgz, prior_result, neib_shape, neib_step):
"""
Helper function that adds gradient contribution from a single
neighborhood position i,j.
pidx = Index of position within neighborhood.
pgz = Gradient of shape (batch_size*num_channels*neibs)
prior_result = Shape (batch_size, num_channnels, rows, cols)
neib_shape = Number of rows, cols in a neighborhood.
neib_step = Step sizes from image2neibs.
"""
nrows, ncols = neib_shape
rstep, cstep = neib_step
batch_size, num_channels, rows, cols = prior_result.shape
i = pidx // ncols
j = pidx - (i * ncols)
# This position does not touch some img pixels in valid mode.
result_indices = prior_result[:, :,
i:(rows - nrows + i + 1):rstep,
j:(cols - ncols + j + 1):cstep, ]
newshape = ((batch_size, num_channels) +
((rows - nrows) // rstep + 1, ) +
((cols - ncols) // cstep + 1, ))
return tt.inc_subtensor(result_indices, pgz.reshape(newshape))
indices = tt.arange(neib_shape[0] * neib_shape[1])
pgzs = gz.dimshuffle((1, 0))
result, _ = theano.scan(
fn=pos2map,
sequences=[indices, pgzs],
outputs_info=tt.zeros(x.shape),
non_sequences=[neib_shape, neib_step],
)
grad_input = result[-1]
return [
grad_input,
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step),
]
return [
grad_not_implemented(self, 0, x),
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step),
]
def c_code_cache_version(self):
return (10, )
def perform(self, node, inp, out_, params):
ten4, neib_shape, neib_step = inp
(z, ) = out_
# GpuImages2Neibs should not run this perform in DebugMode
if type(self) != Images2Neibs:
raise theano.gof.utils.MethodNotDefined()
def CEIL_INTDIV(a, b):
if a % b:
return (a // b) + 1
else:
return a // b
grid_c = -1 # number of patch in height
grid_d = -1 # number of patch in width
assert ten4.ndim == 4
assert neib_shape.ndim == 1
assert neib_shape.shape[0] == 2
assert neib_step.ndim == 1
assert neib_step.shape[0] == 2
c, d = neib_shape
step_x, step_y = neib_step
mode = self.mode
if step_x <= 0 or step_y <= 0:
raise ValueError("neib_step wrong step ; values <= 0. Got " +
str(neib_step))
if c <= 0 or d <= 0:
raise ValueError("neib_shape values <=0. Got " + str(neib_shape))
if mode == "wrap_centered":
if (c % 2 != 1) or (d % 2 != 1):
raise TypeError(
"Images2Neibs:"
" in mode wrap_centered need patch with odd shapes")
if (ten4.shape[2] < c) or (ten4.shape[3] < d):
raise TypeError(
"Images2Neibs: in wrap_centered mode, don't support"
" image shapes smaller then the patch shapes:"
f" neib_shape=({int(c)},{int(d)}), ten4[2:]=[{int(ten4.shape[2])},{int(ten4.shape[3])}]"
)
grid_c = CEIL_INTDIV(ten4.shape[2], step_x)
grid_d = CEIL_INTDIV(ten4.shape[3], step_y)
elif mode == "valid":
if (ten4.shape[2] < c) or (((ten4.shape[2] - c) % step_x) != 0):
raise TypeError(
f"neib_shape[0]={int(c)}, neib_step[0]={int(step_x)} and"
f" ten4.shape[2]={int(ten4.shape[2])} not consistent")
if (ten4.shape[3] < d) or (((ten4.shape[3] - d) % step_y) != 0):
raise TypeError(
f"neib_shape[1]={int(d)}, neib_step[1]={int(step_y)} and"
f" ten4.shape[3]={int(ten4.shape[3])} not consistent")
# number of patch in height
grid_c = 1 + ((ten4.shape[2] - c) // step_x)
# number of patch in width
grid_d = 1 + ((ten4.shape[3] - d) // step_y)
elif mode == "ignore_borders":
# number of patch in height
grid_c = 1 + ((ten4.shape[2] - c) // step_x)
# number of patch in width
grid_d = 1 + ((ten4.shape[3] - d) // step_y)
elif mode == "half":
# This is equivalent to 'valid' with padding (c // 2, d // 2) on both sides
# Thus the expanded image will have size (h + 2 * (c // 2), w + 2 * (d // 2))
# Plugging these in the equation for 'valid' we get
# h + 2 * (c // 2) - c = h - (c % 2)
# w + 2 * (d // 2) - c = w - (d % 2)
if (ten4.shape[2] < c) or (((ten4.shape[2] -
(c % 2)) % step_x) != 0):
raise TypeError(
f"neib_shape[0]={int(c)}, neib_step[0]={int(step_x)} and"
f" ten4.shape[2]={int(ten4.shape[2])} not consistent")
if (ten4.shape[3] < d) or (((ten4.shape[3] -
(d % 2)) % step_y) != 0):
raise TypeError(
f"neib_shape[0]={int(d)}, neib_step[0]={int(step_y)} and"
f" ten4.shape[3]={int(ten4.shape[3])} not consistent")
# number of patch in height
grid_c = 1 + ((ten4.shape[2] - (c % 2)) // step_x)
# number of patch in width
grid_d = 1 + ((ten4.shape[3] - (d % 2)) // step_y)
elif mode == "full":
# This is equivalent to 'valid' with padding (c - 1, d - 1) on both sides
# Thus the expanded image will have size (h + 2 * (c - 1), w + 2 * (d - 1))
# Plugging these in the equation for 'valid' we get
# h + 2 * (c - 1) - c = h + c - 2
# w + 2 * (d - 1) - c = w + d - 2
if (ten4.shape[2] < c) or ((
(ten4.shape[2] + c - 2) % step_x) != 0):
raise TypeError(
f"neib_shape[0]={int(c)}, neib_step[0]={int(step_x)} and"
f" ten4.shape[2]={int(ten4.shape[2])} not consistent")
if (ten4.shape[3] < d) or ((
(ten4.shape[3] + d - 2) % step_y) != 0):
raise TypeError(
f"neib_shape[0]={int(d)}, neib_step[0]={int(step_y)} and"
f" ten4.shape[3]={int(ten4.shape[3])} not consistent")
# number of patch in height
grid_c = 1 + ((ten4.shape[2] + c - 2) // step_x)
# number of patch in width
grid_d = 1 + ((ten4.shape[3] + d - 2) // step_y)
else:
raise TypeError(f"Images2Neibs: unknow mode '{mode}'")
z_dim0 = grid_c * grid_d * ten4.shape[1] * ten4.shape[0]
z_dim1 = c * d
z[0] = np.empty((z_dim0, z_dim1), dtype=node.outputs[0].dtype)
nb_batch = ten4.shape[0]
nb_stack = ten4.shape[1]
height = ten4.shape[2]
width = ten4.shape[3]
wrap_centered_half_idx_shift_x = c // 2
wrap_centered_half_idx_shift_y = d // 2
for n in range(nb_batch):
for s in range(nb_stack):
# loop over the number of patch in height
for a in range(grid_c):
# loop over the number of patch in width
for b in range(grid_d):
z_row = b + grid_d * (a + grid_c * (s + nb_stack * n))
for i in range(c):
ten4_2 = i + a * step_x
if mode == "wrap_centered":
ten4_2 -= wrap_centered_half_idx_shift_x
if ten4_2 < 0:
ten4_2 += height
elif ten4_2 >= height:
ten4_2 -= height
elif mode == "half":
ten4_2 -= wrap_centered_half_idx_shift_x
elif mode == "full":
ten4_2 -= c - 1
if ten4_2 < 0 or ten4_2 >= height:
z[0][z_row, d * i:d * i + d] = 0
else:
for j in range(d):
ten4_3 = j + b * step_y
if mode == "wrap_centered":
ten4_3 -= wrap_centered_half_idx_shift_y
if ten4_3 < 0:
ten4_3 += width
elif ten4_3 >= width:
ten4_3 -= width
elif mode == "half":
ten4_3 -= wrap_centered_half_idx_shift_y
elif mode == "full":
ten4_3 -= d - 1
z_col = j + d * i
if ten4_3 < 0 or ten4_3 >= width:
z[0][z_row, z_col] = 0
else:
z[0][z_row, z_col] = ten4[n, s, ten4_2,
ten4_3]
def infer_shape(self, node, input_shape):
in_shape = input_shape[0]
c, d = node.inputs[1]
step_x, step_y = node.inputs[2]
if self.mode == "wrap_centered":
grid_c = tt.ceil_intdiv(in_shape[2], step_x)
grid_d = tt.ceil_intdiv(in_shape[3], step_y)
elif self.mode == "valid":
grid_c = 1 + ((in_shape[2] - c) // step_x)
grid_d = 1 + ((in_shape[3] - d) // step_y)
elif self.mode == "ignore_borders":
grid_c = 1 + ((in_shape[2] - c) // step_x)
grid_d = 1 + ((in_shape[3] - d) // step_y)
elif self.mode == "half":
grid_c = 1 + ((in_shape[2] - (c % 2)) // step_x)
grid_d = 1 + ((in_shape[3] - (d % 2)) // step_y)
elif self.mode == "full":
grid_c = 1 + ((in_shape[2] + c - 2) // step_x)
grid_d = 1 + ((in_shape[3] + d - 2) // step_y)
else:
raise TypeError(f"Images2Neibs: unknow mode '{self.mode}'")
z_dim0 = grid_c * grid_d * in_shape[1] * in_shape[0]
z_dim1 = c * d
return [(z_dim0, z_dim1)]
def c_code(self, node, name, inp, out, sub):
return """
#ifndef CEIL_INTDIV
#define CEIL_INTDIV(a, b) ((a/b) + ((a %% b) ? 1: 0))
#endif
int grid_c = -1; //number of patch in height
int grid_d = -1; //number of patch in width
{
if (PyArray_NDIM(%(ten4)s) != 4)
{
PyErr_Format(PyExc_TypeError, "ten4 wrong rank");
%(fail)s;
}
if (PyArray_NDIM(%(neib_shape)s) != 1)
{
PyErr_Format(PyExc_TypeError, "neib_shape wrong rank");
%(fail)s;
}
if ( (PyArray_DIMS(%(neib_shape)s))[0] != 2)
{
PyErr_Format(PyExc_TypeError, "neib_shape wrong shape ; has to"
" contain 2 elements");
%(fail)s;
}
if (PyArray_NDIM(%(neib_step)s) != 1)
{
PyErr_Format(PyExc_TypeError, "neib_step wrong rank");
%(fail)s;
}
if ( (PyArray_DIMS(%(neib_step)s))[0] != 2)
{
PyErr_Format(PyExc_TypeError,
"neib_step wrong step ; has to contain 2 elements");
%(fail)s;
}
// (c,d) = neib_shape
const npy_intp c = (npy_intp) *(dtype_%(neib_shape)s*) PyArray_GETPTR1(%(neib_shape)s, 0);
const npy_intp d = (npy_intp) *(dtype_%(neib_shape)s*) PyArray_GETPTR1(%(neib_shape)s, 1);
// (step_x,step_y) = neib_step
const dtype_%(neib_step)s step_x = *(dtype_%(neib_step)s*) PyArray_GETPTR1(%(neib_step)s, 0);
const dtype_%(neib_step)s step_y = *(dtype_%(neib_step)s*) PyArray_GETPTR1(%(neib_step)s, 1);
if (step_x <=0 || step_y <=0)
{
PyErr_Format(PyExc_ValueError,
"neib_step wrong step ; values <= 0. Got %%lld %%lld.",
(long long) step_x, (long long) step_y);
%(fail)s;
}
if (c <=0 || d <=0)
{
PyErr_Format(PyExc_ValueError,
"neib_shape values <= 0. Got %%lld %%lld.",
(long long)c, (long long)d);
%(fail)s;
}
if (%(mode)s == MODE_WRAP_CENTERED) {
if (c%%2!=1 || d%%2!=1){
PyErr_Format(PyExc_TypeError,
"Images2Neibs: in mode wrap_centered"
" need patch with odd shapes");
%(fail)s;
}
if ( (PyArray_DIMS(%(ten4)s))[2] < c ||
(PyArray_DIMS(%(ten4)s))[3] < d)
{
PyErr_Format(PyExc_TypeError,
"Images2Neibs: in wrap_centered mode, don't support image"
" shapes smaller then the patch shapes:"
" neib_shape=(%%ld,%%ld), ten4[2:]=[%%ld,%%ld]",
(long int)c, (long int)d,
(long int)(PyArray_DIMS(%(ten4)s)[2]),
(long int)(PyArray_DIMS(%(ten4)s)[3]));
%(fail)s;
}
grid_c = CEIL_INTDIV(((PyArray_DIMS(%(ten4)s))[2]),step_x);
grid_d = CEIL_INTDIV(((PyArray_DIMS(%(ten4)s))[3]),step_y);
} else if (%(mode)s == MODE_VALID) {
if ( ((PyArray_DIMS(%(ten4)s))[2] < c) ||
( (((PyArray_DIMS(%(ten4)s))[2]-c) %% step_x)!=0))
{
PyErr_Format(PyExc_TypeError,
"neib_shape[0]=%%ld, neib_step[0]=%%ld and"
" ten4.shape[2]=%%ld not consistent",
(long int)c, (long int)step_x,
(long int)(PyArray_DIMS(%(ten4)s)[2]));
%(fail)s;
}
if ( ((PyArray_DIMS(%(ten4)s))[3] < d) ||
( (((PyArray_DIMS(%(ten4)s))[3]-d) %% step_y)!=0))
{
PyErr_Format(PyExc_TypeError,
"neib_shape[1]=%%ld, neib_step[1]=%%ld and"
" ten4.shape[3]=%%ld not consistent",
(long int)d, (long int)step_y,
(long int)(PyArray_DIMS(%(ten4)s)[3]));
%(fail)s;
}
//number of patch in height
grid_c = 1+(((PyArray_DIMS(%(ten4)s))[2]-c)/step_x);
//number of patch in width
grid_d = 1+(((PyArray_DIMS(%(ten4)s))[3]-d)/step_y);
} else if (%(mode)s == MODE_IGNORE_BORDERS) {
//number of patch in height
grid_c = 1+(((PyArray_DIMS(%(ten4)s))[2]-c)/step_x);
//number of patch in width
grid_d = 1+(((PyArray_DIMS(%(ten4)s))[3]-d)/step_y);
} else if (%(mode)s == MODE_HALF) {
if ( ((PyArray_DIMS(%(ten4)s))[2] < c) ||
( (((PyArray_DIMS(%(ten4)s))[2]-(c%%2)) %% step_x)!=0))
{
PyErr_Format(PyExc_TypeError,
"neib_shape[0]=%%ld, neib_step[0]=%%ld and"
" ten4.shape[2]=%%ld not consistent",
(long int)c, (long int)step_x,
(long int)(PyArray_DIMS(%(ten4)s)[2]));
%(fail)s;
}
if ( ((PyArray_DIMS(%(ten4)s))[3] < d) ||
( (((PyArray_DIMS(%(ten4)s))[3]-(d%%2)) %% step_y)!=0))
{
PyErr_Format(PyExc_TypeError,
"neib_shape[1]=%%ld, neib_step[1]=%%ld and"
" ten4.shape[3]=%%ld not consistent",
(long int)d, (long int)step_y,
(long int)(PyArray_DIMS(%(ten4)s)[3]));
%(fail)s;
}
//number of patch in height
grid_c = 1+(((PyArray_DIMS(%(ten4)s))[2]-(c%%2))/step_x);
//number of patch in width
grid_d = 1+(((PyArray_DIMS(%(ten4)s))[3]-(d%%2))/step_y);
} else if (%(mode)s == MODE_FULL) {
if ( ((PyArray_DIMS(%(ten4)s))[2] < c) ||
( (((PyArray_DIMS(%(ten4)s))[2]+c-2) %% step_x)!=0))
{
PyErr_Format(PyExc_TypeError,
"neib_shape[0]=%%ld, neib_step[0]=%%ld and"
" ten4.shape[2]=%%ld not consistent",
(long int)c, (long int)step_x,
(long int)(PyArray_DIMS(%(ten4)s)[2]));
%(fail)s;
}
if ( ((PyArray_DIMS(%(ten4)s))[3] < d) ||
( (((PyArray_DIMS(%(ten4)s))[3]+d-2) %% step_y)!=0))
{
PyErr_Format(PyExc_TypeError,
"neib_shape[1]=%%ld, neib_step[1]=%%ld and"
" ten4.shape[3]=%%ld not consistent",
(long int)d, (long int)step_y,
(long int)(PyArray_DIMS(%(ten4)s)[3]));
%(fail)s;
}
//number of patch in height
grid_c = 1+(((PyArray_DIMS(%(ten4)s))[2]+c-2)/step_x);
//number of patch in width
grid_d = 1+(((PyArray_DIMS(%(ten4)s))[3]+d-2)/step_y);
} else {
PyErr_Format(PyExc_TypeError,
"Images2Neibs: unknow mode %%d", %(mode)s);
%(fail)s;
}
// new dimensions for z
const npy_intp z_dim1 = c * d;
const npy_intp z_dim0 = grid_c
* grid_d
* (PyArray_DIMS(%(ten4)s))[1]
* (PyArray_DIMS(%(ten4)s))[0];
if ((NULL == %(z)s)
|| ((PyArray_DIMS(%(z)s))[0] != z_dim0 )
|| ((PyArray_DIMS(%(z)s))[1] != z_dim1 )
)
{
Py_XDECREF(%(z)s);
npy_intp dims[2];
dims[0] = z_dim0;
dims[1] = z_dim1;
%(z)s = (PyArrayObject*) PyArray_EMPTY(2,
dims,
PyArray_TYPE((PyArrayObject*) py_%(ten4)s),
0);
if (!%(z)s)
{
PyErr_SetString(PyExc_MemoryError, "failed to alloc z output");
%(fail)s;
}
}
}
{ // NESTED SCOPE
const int nb_batch = (PyArray_DIMS(%(ten4)s))[0];
const int nb_stack = (PyArray_DIMS(%(ten4)s))[1];
const int height = (PyArray_DIMS(%(ten4)s))[2];
const int width = (PyArray_DIMS(%(ten4)s))[3];
// (c,d) = neib_shape
const npy_intp c = (npy_intp) *(dtype_%(neib_shape)s*) PyArray_GETPTR1(%(neib_shape)s, 0);
const npy_intp d = (npy_intp) *(dtype_%(neib_shape)s*) PyArray_GETPTR1(%(neib_shape)s, 1);
// (step_x,step_y) = neib_step
const npy_intp step_x = (npy_intp) *(dtype_%(neib_step)s*) PyArray_GETPTR1(%(neib_step)s, 0);
const npy_intp step_y = (npy_intp) *(dtype_%(neib_step)s*) PyArray_GETPTR1(%(neib_step)s, 1);
const int wrap_centered_half_idx_shift_x = c/2;
const int wrap_centered_half_idx_shift_y = d/2;
// Oh this is messed up...
for (int n = 0; n < nb_batch; n++) // loop over batches
for (int s = 0; s < nb_stack; s++) // loop over stacks
for (int a = 0; a < grid_c; a++) // loop over the number of patch in height
for (int b = 0; b < grid_d; b++) // loop over the number of patch in width
{
int z_row = b + grid_d*(a + grid_c*(s + nb_stack*n));
for (int i = 0; i < c; i++) // loop over c
{
int ten4_2 = i + a * step_x;
if (%(mode)s == MODE_WRAP_CENTERED) {
ten4_2 -= wrap_centered_half_idx_shift_x;
if ( ten4_2 < 0 ) ten4_2 += height;
else if (ten4_2 >= height) ten4_2 -= height;
} else if (%(mode)s == MODE_HALF) {
ten4_2 -= wrap_centered_half_idx_shift_x;
} else if (%(mode)s == MODE_FULL) {
ten4_2 -= c - 1;
}
if (ten4_2 < 0 | ten4_2 >= height) {
dtype_%(z)s* curr_z = (dtype_%(z)s*) PyArray_GETPTR2(%(z)s, z_row, d * i);
memset(curr_z, 0, d*sizeof(*curr_z));
} else {
for (int j = 0; j < d; j++) // loop over d
{
int ten4_3 = j + b * step_y;
if (%(mode)s == MODE_WRAP_CENTERED) {
ten4_3 -= wrap_centered_half_idx_shift_y;
if ( ten4_3 < 0 ) ten4_3 += width;
else if (ten4_3 >= width) ten4_3 -= width;
} else if (%(mode)s == MODE_HALF) {
ten4_3 -= wrap_centered_half_idx_shift_y;
} else if (%(mode)s == MODE_FULL) {
ten4_3 -= d - 1;
}
int z_col = j + d * i;
dtype_%(z)s* curr_z = (dtype_%(z)s*) PyArray_GETPTR2(%(z)s, z_row, z_col);
if (ten4_3 < 0 | ten4_3 >= width) {
*curr_z = 0;
} else {
*curr_z = *( (dtype_%(ten4)s*) PyArray_GETPTR4(%(ten4)s, n, s, ten4_2, ten4_3));
}
}
}
}
}
} // END NESTED SCOPE
""" % dict(
ten4=inp[0],
neib_shape=inp[1],
neib_step=inp[2],
z=out[0],
fail=sub["fail"],
mode=sub["params"],
)
class BaseCorrMM(gof.OpenMPOp):
"""
Base class for `CorrMM`, `CorrMM_gradWeights` and
`CorrMM_gradInputs`. Cannot be used directly.
Every sub-class must define internal attribute ``_direction`` out of __init__().
``_direction`` must take one of following values:
- "forward" to correlate bottom with weights and store results in top.
- "backprop weights" to do a valid convolution of bottom with top
(swapping the first two dimensions) and store results in weights.
- "backprop inputs" to do a full convolution of top with weights
(swapping the first two dimensions) and store results in bottom.
Parameters
----------
border_mode : {'valid', 'full', 'half'}
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)).
filter_dilation
Perform dilated correlation (default: (1,1))
num_groups
Perform grouped convolutions (default: 1)
"""
check_broadcast = False
__props__ = ('border_mode', 'subsample', 'filter_dilation', 'num_groups')
_direction = None
params_type = ParamsType(
direction=EnumList(
('DIRECTION_FORWARD', 'forward'), # 0
('DIRECTION_BACKPROP_WEIGHTS', 'backprop weights'), # 1
('DIRECTION_BACKPROP_INPUTS', 'backprop inputs')), # 2
dH=int64,
dW=int64,
dilH=int64,
dilW=int64,
padH=int64,
padW=int64,
num_groups=int64)
def __init__(self,
border_mode="valid",
subsample=(1, 1),
filter_dilation=(1, 1),
num_groups=1,
openmp=None):
super(BaseCorrMM, self).__init__(openmp=openmp)
if isinstance(border_mode, integer_types):
if border_mode < 0:
raise ValueError('invalid border_mode {}, which must be a '
'non-negative integer'.format(border_mode))
border_mode = (border_mode, border_mode)
if isinstance(border_mode, tuple):
if len(border_mode
) != 2 or border_mode[0] < 0 or border_mode[1] < 0:
raise ValueError(
'invalid border_mode {}, which must be a '
'pair of non-negative integers'.format(border_mode))
pad_h, pad_w = map(int, border_mode)
border_mode = (pad_h, pad_w)
if not ((isinstance(border_mode, tuple) and min(border_mode) >= 0)
or border_mode in ('valid', 'full', 'half')):
raise ValueError('invalid border_mode {}, which must be either '
'"valid", "full", "half", an integer or a pair of'
' integers'.format(border_mode))
self.border_mode = border_mode
if len(subsample) != 2:
raise ValueError("subsample must have two elements")
if len(filter_dilation) != 2:
raise ValueError("filter_dilation must have two elements")
self.subsample = tuple(subsample)
self.filter_dilation = tuple(filter_dilation)
if not theano.config.blas.ldflags:
# Theano will use a NumPy C implementation of [sd]gemm_ instead.
self.blas_type = ''
else:
if 'openblas' in theano.config.blas.ldflags:
self.blas_type = 'openblas'
elif 'mkl' in theano.config.blas.ldflags:
self.blas_type = 'mkl'
else:
self.blas_type = ''
if self._direction not in [
"forward", "backprop weights", "backprop inputs"
]:
raise ValueError("_direction must be one of 'forward', "
"'backprop weights', 'backprop inputs'")
if num_groups < 1:
raise ValueError("Number of groups should be greater than 0")
self.num_groups = num_groups
@property
def pad(self):
if self.border_mode == "half":
return (-1, -1)
elif self.border_mode == "full":
return (-2, -2)
elif isinstance(self.border_mode, tuple):
return self.border_mode
else:
assert self.border_mode == "valid"
return (0, 0)
# Direction should be converted to real enum value,
# as it is compared to integer later in c_code_helper().
direction = property(
lambda self: self.params_type.enum_from_alias(self._direction))
dH = property(lambda self: self.subsample[0])
dW = property(lambda self: self.subsample[1])
dilH = property(lambda self: self.filter_dilation[0])
dilW = property(lambda self: self.filter_dilation[1])
padH = property(lambda self: self.pad[0])
padW = property(lambda self: self.pad[1])
def __str__(self):
return '%s{%s, %s, %s, %s}' % (
self.__class__.__name__, self.border_mode, str(self.subsample),
str(self.filter_dilation), str(self.num_groups))
@staticmethod
def as_common_dtype(in1, in2):
"""
Upcast input variables if neccesary.
"""
dtype = theano.scalar.upcast(in1.dtype, in2.dtype)
return in1.astype(dtype), in2.astype(dtype)
def __setstate__(self, d):
self.__dict__.update(d)
if not hasattr(self, 'num_groups'):
self.num_groups = 1
def c_support_code(self):
ccodes = blas_headers.blas_header_text()
if self.blas_type == 'openblas':
ccodes += blas_headers.openblas_threads_text()
elif self.blas_type == 'mkl':
ccodes += blas_headers.mkl_threads_text()
return ccodes
def c_libraries(self):
return ldflags()
def c_compile_args(self):
compile_args = ldflags(libs=False, flags=True)
compile_args += super(BaseCorrMM, self).c_compile_args()
return compile_args
def c_lib_dirs(self):
return ldflags(libs=False, libs_dir=True)
def c_header_dirs(self):
return ldflags(libs=False, include_dir=True)
def c_headers(self):
headers = ['<stdio.h>']
headers += super(BaseCorrMM, self).c_headers()
return headers
def c_code_cache_version(self):
# raise this whenever modifying any of the support_code_files
return (7, self.openmp, blas_header_version())
def c_support_code_apply(self, node, nodename):
# REMEMBER TO RAISE c_code_cache_version when changing any of
# these files
sub = {}
dtype = str(node.__dict__['inputs'][0].dtype)
assert dtype in ('float32', 'float64')
if dtype == 'float32':
sub['gemm'] = 'sgemm_'
sub['float_type'] = 'npy_float'
sub['float_typenum'] = 'NPY_FLOAT'
sub['n_bytes'] = 4
sub['c_float_type'] = 'float'
else:
sub['gemm'] = 'dgemm_'
sub['float_type'] = 'npy_double'
sub['float_typenum'] = 'NPY_DOUBLE'
sub['n_bytes'] = 8
sub['c_float_type'] = 'double'
if self.openmp:
sub['omp_flags'] = '#pragma omp parallel for schedule(static)'
sub['omp_get_max_threads'] = 'omp_get_max_threads()'
sub['omp_get_thread_num'] = 'omp_get_thread_num()'
if self.blas_type == 'openblas':
sub['blas_set_num_threads'] = 'openblas_set_num_threads'
sub['blas_get_num_threads'] = 'openblas_get_num_threads()'
elif self.blas_type == 'mkl':
sub['blas_set_num_threads'] = 'mkl_set_num_threads'
sub['blas_get_num_threads'] = 'mkl_get_max_threads()'
else:
sub['blas_set_num_threads'] = ''
sub['blas_get_num_threads'] = '0'
else:
sub['omp_flags'] = ''
sub['omp_get_max_threads'] = '1'
sub['omp_get_thread_num'] = '0'
sub['blas_set_num_threads'] = ''
sub['blas_get_num_threads'] = '0'
files = [os.path.join('c_code', 'corr_gemm.c')]
codes = [
open(os.path.join(os.path.split(__file__)[0], f)).read()
for f in files
]
final_code = ''
for code in codes:
final_code += code
return final_code % sub
def c_code_helper(self,
bottom,
weights,
top,
sub,
height=None,
width=None):
"""
This generates the C code for CorrMM (direction="forward"),
CorrMM_gradWeights (direction="backprop weights"), and
CorrMM_gradInputs (direction="backprop inputs").
Depending on the direction, one of bottom, weights, top will
receive the output, while the other two serve as inputs.
:param bottom: Variable name of the input images in the forward pass,
or the gradient of the input images in backprop wrt. inputs
:param weights: Variable name of the filters in the forward pass,
or the gradient of the filters in backprop wrt. weights
:param top: Variable name of the output images / feature maps in the
forward pass, or the gradient of the outputs in the backprop passes
:param sub: Dictionary of substitutions useable to help generating the
C code.
:param height: If self.subsample[0] != 1, a variable giving the height
of the filters for direction="backprop weights" or the height of
the input images for direction="backprop inputs".
If self.border_mode == 'half', a variable giving the height of the
filters for direction="backprop weights". Ignored otherwise.
:param width: If self.subsample[1] != 1, a variable giving the width
of the filters for direction="backprop weights" or the width of the
input images for direction="backprop inputs".
If self.border_mode == 'half', a variable giving the width of the
filters for direction="backprop weights". Ignored otherwise.
"""
# When subsampling, we cannot unambiguously infer the height and width
# of bottom and weights from top, so we require them to be given.
# Similarly, when border_mode="half", we cannot infer the weight size.
if height:
height = '(*(npy_int64 *)(PyArray_DATA(%s)))' % height
else:
if ((self.direction != 0) and
(self.dH != 1)) or ((self.direction == 1) and
(self.padH == -1)):
raise ValueError(
"height must be given for backprop with vertical sampling or border_mode='half'"
)
height = '-1'
if width:
width = '(*(npy_int64 *)(PyArray_DATA(%s)))' % width
else:
if ((self.direction != 0) and
(self.dW != 1)) or ((self.direction == 1) and
(self.padW == -1)):
raise ValueError(
"width must be given for backprop with horizontal sampling or border_mode='half'"
)
width = '-1'
return """
// Mandatory args
int direction = %(params)s->direction; // forward, bprop weights, bprop inputs
// Optional args
int dH = %(params)s->dH;
int dW = %(params)s->dW;
int dilH = %(params)s->dilH;
int dilW = %(params)s->dilW;
int padH = %(params)s->padH;
int padW = %(params)s->padW;
int numgroups = %(params)s->num_groups;
PyArrayObject * bottom = %(bottom)s;
PyArrayObject * weights = %(weights)s;
PyArrayObject * top = %(top)s;
PyArrayObject * out2 = NULL;
PyArrayObject **out = NULL;
switch(%(params)s->direction) {
case DIRECTION_FORWARD:
out = &%(top)s;
break;
case DIRECTION_BACKPROP_WEIGHTS:
out = &%(weights)s;
break;
case DIRECTION_BACKPROP_INPUTS:
out = &%(bottom)s;
break;
default:
PyErr_SetString(PyExc_ValueError, "CPU CorrMM: Invalid direction.");
{%(fail)s}
break;
}
// Obtain or infer kernel width and height
// (we need to know it early to be able to handle auto-padding)
int kH, kW, dil_kH, dil_kW;
if (direction != 1) {
// weight is an input variable, we can just read its shape
kH = PyArray_DIMS(weights)[2];
kW = PyArray_DIMS(weights)[3];
}
else {
if (%(height)s != -1) {
// kernel height is specified (perhaps vertical subsampling or half padding)
kH = %(height)s;
}
else if (padH == -2) {
// vertical full padding, we can infer the kernel height
kH = (2 - PyArray_DIMS(bottom)[2] + (PyArray_DIMS(top)[2] - 1) * dH - 1)/ dilH + 1;
}
else {
// explicit padding, we can infer the kernel height
kH = (PyArray_DIMS(bottom)[2] + 2*padH - (PyArray_DIMS(top)[2] - 1) * dH - 1) / dilH +1;
}
if (%(width)s != -1) {
// kernel width is specified (perhaps horizontal subsampling or half padding)
kW = %(width)s;
}
else if (padW == -2) {
kW = (2 - PyArray_DIMS(bottom)[3] + (PyArray_DIMS(top)[3] - 1) * dW - 1) / dilW + 1;
}
else {
kW = (PyArray_DIMS(bottom)[3] + 2*padW - (PyArray_DIMS(top)[3] - 1) * dW - 1) / dilW + 1;
}
}
// Implicit dilated kernel size
dil_kH = (kH - 1) * dilH + 1;
dil_kW = (kW - 1) * dilW + 1;
// Auto-padding if requested
if (padH == -1) { // vertical half padding
padH = dil_kH / 2;
}
else if (padH == -2) { // vertical full padding
padH = dil_kH - 1;
}
else if (padH < 0) {
PyErr_SetString(PyExc_ValueError, "BaseCorrMM: padH must be >= -2");
%(fail)s
}
if (padW == -1) { // horizontal half padding
padW = dil_kW / 2;
}
else if (padW == -2) { // horizontal full padding
padW = dil_kW - 1;
}
else if (padW < 0) {
PyErr_SetString(PyExc_ValueError, "BaseCorrMM: padW must be >= -2");
%(fail)s
}
// Infer output shape
npy_intp out_dim[4];
switch(direction) {
case 0: // forward pass
// output is top: (batchsize, num_filters, height, width)
// height and width: top = (bottom + 2*pad - ((weight-1)*dil + 1)) / sample + 1
out_dim[0] = (npy_intp)PyArray_DIMS(bottom)[0];
out_dim[1] = (npy_intp)PyArray_DIMS(weights)[0];
out_dim[2] = (npy_intp)((PyArray_DIMS(bottom)[2] + 2*padH - ((PyArray_DIMS(weights)[2]-1)*dilH + 1)) / dH + 1);
out_dim[3] = (npy_intp)((PyArray_DIMS(bottom)[3] + 2*padW - ((PyArray_DIMS(weights)[3]-1)*dilW + 1)) / dW + 1);
if (out_dim[0] < 0 || out_dim[1] < 0 || out_dim[2] <= 0 || out_dim[3] <= 0)
{
PyErr_Format(PyExc_ValueError,
"CorrMM: impossible output shape\\n"
" bottom shape: %%ld x %%ld x %%ld x %%ld\\n"
" weights shape: %%ld x %%ld x %%ld x %%ld\\n"
" top shape: %%ld x %%ld x %%ld x %%ld\\n",
(long int)PyArray_DIMS(bottom)[0], (long int)PyArray_DIMS(bottom)[1],
(long int)PyArray_DIMS(bottom)[2], (long int)PyArray_DIMS(bottom)[3],
(long int)PyArray_DIMS(weights)[0], (long int)PyArray_DIMS(weights)[1],
(long int)PyArray_DIMS(weights)[2], (long int)PyArray_DIMS(weights)[3],
(long int)out_dim[0], (long int)out_dim[1], (long int)out_dim[2],
(long int)out_dim[3]);
%(fail)s
}
break;
case 1: // backprop wrt. weights
// output is weights: (num_filters, num_channels, height, width)
// height and width: weights = (bottom + 2*pad - (top - 1) * sample - 1) / dil + 1
out_dim[0] = (npy_intp)PyArray_DIMS(top)[1];
out_dim[1] = (npy_intp)PyArray_DIMS(bottom)[1] / numgroups;
out_dim[2] = (npy_intp)kH; // already inferred further above
out_dim[3] = (npy_intp)kW; // how convenient
if (out_dim[0] < 0 || out_dim[1] < 0 || out_dim[2] <= 0 || out_dim[3] <= 0)
{
PyErr_Format(PyExc_ValueError,
"CorrMM backprop wrt. weights: impossible output shape\\n"
" bottom shape: %%ld x %%ld x %%ld x %%ld\\n"
" weights shape: %%ld x %%ld x %%ld x %%ld\\n"
" top shape: %%ld x %%ld x %%ld x %%ld\\n",
(long int)PyArray_DIMS(bottom)[0], (long int)PyArray_DIMS(bottom)[1],
(long int)PyArray_DIMS(bottom)[2], (long int)PyArray_DIMS(bottom)[3],
(long int)out_dim[0], (long int)out_dim[1], (long int)out_dim[2],
(long int)out_dim[3],
(long int)PyArray_DIMS(top)[0], (long int)PyArray_DIMS(top)[1],
(long int)PyArray_DIMS(top)[2], (long int)PyArray_DIMS(top)[3]);
%(fail)s
}
break;
case 2: // backprop wrt. inputs
// output is bottom: (batchsize, num_channels, height, width)
// height and width: bottom = (top - 1) * sample + (weights-1)*dil + 1 - 2*pad
out_dim[0] = (npy_intp)PyArray_DIMS(top)[0];
out_dim[1] = (npy_intp)PyArray_DIMS(weights)[1] * numgroups;
out_dim[2] = (npy_intp)((%(height)s != -1) ? %(height)s : (PyArray_DIMS(top)[2] - 1) * dH + (PyArray_DIMS(weights)[2]-1)*dilH + 1 - 2*padH);
out_dim[3] = (npy_intp)((%(width)s != -1) ? %(width)s : (PyArray_DIMS(top)[3] - 1) * dW + (PyArray_DIMS(weights)[3]-1)*dilW + 1 - 2*padW);
if (out_dim[0] < 0 || out_dim[1] < 0 || out_dim[2] <= 0 || out_dim[3] <= 0)
{
PyErr_Format(PyExc_ValueError,
"CorrMM backprop wrt. inputs: impossible output shape\\n"
" bottom shape: %%ld x %%ld x %%ld x %%ld\\n"
" weights shape: %%ld x %%ld x %%ld x %%ld\\n"
" top shape: %%ld x %%ld x %%ld x %%ld\\n",
(long int)out_dim[0], (long int)out_dim[1], (long int)out_dim[2],
(long int)out_dim[3],
(long int)PyArray_DIMS(weights)[0], (long int)PyArray_DIMS(weights)[1],
(long int)PyArray_DIMS(weights)[2], (long int)PyArray_DIMS(weights)[3],
(long int)PyArray_DIMS(top)[0], (long int)PyArray_DIMS(top)[1],
(long int)PyArray_DIMS(top)[2], (long int)PyArray_DIMS(top)[3]);
%(fail)s
}
break;
default:
PyErr_SetString(PyExc_ValueError, "BaseCorrMM: direction must be 0, 1, or 2\\n");
%(fail)s
}
// Prepare output array
int typenum;
if ( !(*out
&& PyArray_NDIM(*out)==4
&& PyArray_IS_C_CONTIGUOUS(*out)
&& PyArray_DIMS(*out)[0]==out_dim[0]
&& PyArray_DIMS(*out)[1]==out_dim[1]
&& PyArray_DIMS(*out)[2]==out_dim[2]
&& PyArray_DIMS(*out)[3]==out_dim[3]))
{
Py_XDECREF(*out);
if (direction != 1) {
typenum = PyArray_TYPE(weights);
}
else {
typenum = PyArray_TYPE(bottom);
}
//Change to PyArray_ZEROS which is faster than PyArray_EMPTY.
*out = (PyArrayObject*)PyArray_ZEROS(4,
out_dim,
typenum,
0);
if (NULL == *out)
{
PyErr_Format(PyExc_RuntimeError,
"BaseCorrMM: Failed to allocate output of %%lld x %%lld x %%lld x %%lld",
(long long)out_dim[0], (long long)out_dim[1], (long long)out_dim[2], (long long)out_dim[3]);
%(fail)s
}
}
// Call corrMM code
out2 = corrMM(%(bottom)s, %(weights)s, %(top)s, direction, dH, dW, dilH, dilW, padH, padW, numgroups );
if (out2==NULL){
%(fail)s
}
assert (out2 == *out);
""" % dict(bottom=bottom,
weights=weights,
top=top,
height=height,
width=width,
fail=sub['fail'],
params=sub['params'])