def c_code(self, node, name, inputs, outputs, sub):
images, = inputs
targets, denoms = outputs
fail = sub['fail']
num_braces = 0
size_f = self._size_f
add_scale = self._add_scale
pow_scale = self._pow_scale
blocked = "true" if self._blocked else "false"
class_name = self.__class__.__name__
class_name_upper = class_name.upper()
basic_setup = self._basic_setup
setup_nv_images = (
contiguity_check("images") +
dimension_check("images", 4) +
self._images_setup
)
num_braces += 2
setup_nv_targets = output_same_shape('targets', 'images')
num_braces += 1
setup_nv_denoms = output_same_shape('denoms', 'images')
num_braces += 1
do_normalize = """
convResponseNormCrossMap(nv_images, nv_denoms, nv_targets, numFilters, sizeF,
addScale, powScale, blocked);
"""
braces = '}' * num_braces + "\n"
rval = (basic_setup +
setup_nv_images +
setup_nv_targets +
setup_nv_denoms +
do_normalize +
braces)
rval = rval % locals()
return rval
def c_code(self, node, name, inputs, outputs, sub):
images, acts, denoms, dout = inputs
targets, out_acts = outputs
fail = sub['fail']
num_braces = 0
size_f = self._size_f
add_scale = self._add_scale
pow_scale = self._pow_scale
blocked = "true" if self._blocked else "false"
inplace = "true" if self._inplace else "false"
scale_targets = int(self._scale_targets)
scale_outputs = int(self._scale_outputs)
class_name = self.__class__.__name__
class_name_upper = class_name.upper()
basic_setup = self._basic_setup
scaling_setup = """
float scaleTargets = %(scale_targets)s;
float scaleOutput = %(scale_outputs)s;
"""
setup_nv_images = (
contiguity_check("images") +
dimension_check("images", 4) +
self._images_setup
)
num_braces += 2
setup_acts = (contiguity_check("acts") +
dimension_check("acts", 4) +
"""
{ //setup_nv_images brace 1
const int * acts_dims = CudaNdarray_HOST_DIMS(%(acts)s);
""" +
ensure_same_shape('acts', 'images') +
"""
{ // setup_nv_acts brace 2
""")
num_braces += 2
setup_nv_denoms = (contiguity_check("denoms") +
dimension_check("denoms", 4) +
"""
{
const int *denoms_dims = images_dims;
""" +
ensure_same_shape("denoms", "images") +
nv_matrix_create("denoms"))
num_braces += 2
setup_nv_dout = (contiguity_check("dout") +
dimension_check("dout", 4) +
"""
{ // setup_nv_dout brace
const int *dout_dims = CudaNdarray_HOST_DIMS(%(dout)s);
""" +
ensure_same_shape("dout", "images") +
nv_matrix_create("dout"))
num_braces += 2
setup_nv_targets = output_same_shape('targets', 'images')
num_braces += 1
setup_nv_out_acts = ("""
const int *out_acts_dims = images_dims;
#if %(inplace)s
// XXX: is this right?
Py_XDECREF(%(out_acts)s);
%(out_acts)s = %(acts)s;
Py_INCREF(%(out_acts)s);
#else
if (CudaNdarray_prep_output(& %(out_acts)s, 4, out_acts_dims)) {
Py_DECREF(%(targets)s);
%(fail)s;
}
if (CudaNdarray_CopyFromCudaNdarray(%(out_acts)s, %(acts)s)) {
Py_DECREF(%(targets)s);
Py_DECREF(%(out_acts)s);
%(fail)s;
}
#endif
""" + nv_matrix_create("out_acts"))
num_braces += 1
undo_normalize = """
convResponseNormCrossMapUndo(nv_dout, nv_denoms, nv_images,
nv_out_acts, nv_targets, numFilters,
sizeF, addScale, powScale, blocked,
scaleTargets, scaleOutput);
"""
rval = "\n".join((basic_setup,
scaling_setup,
setup_nv_images,
setup_acts,
setup_nv_denoms,
setup_nv_dout,
setup_nv_targets,
setup_nv_out_acts,
undo_normalize,
"}" * num_braces))
return rval % locals()